{"id":2498,"date":"2021-11-22T00:05:25","date_gmt":"2021-11-21T23:05:25","guid":{"rendered":"https:\/\/phd-ai-society.di.unipi.it\/training\/"},"modified":"2026-03-19T10:54:02","modified_gmt":"2026-03-19T09:54:02","slug":"training","status":"publish","type":"page","link":"https:\/\/phd-ai-society.di.unipi.it\/en\/training\/","title":{"rendered":"Study plan"},"content":{"rendered":"<div style=\"text-align: justify\">Ph.D. students have to attend at least 140 hours of courses overall in 3 years (the earlier the better).<br \/>\nEach Ph.D. student is expected to:<\/div>\n<ul style=\"text-align: justify\">\n<li>attend and take three or more exams of courses involving at least 80 hours of lectures in total;<\/li>\n<li>attend at least additional 60 hours of training activities without exam (or without taking the exam, if the course is with exam);<\/li>\n<li>attend the <a href=\"https:\/\/phd-ai-society.di.unipi.it\/en\/summer-schools\/\">PhD school organized by PhD-AI.it<\/a> at the first year of study (the corresponding hours are included in the 60 hours without exams above)<\/li>\n<\/ul>\n<div style=\"text-align: justify\">The additional 60 hours of training activities may include:<\/div>\n<ul>\n<li>cycles of seminars and doctoral schools with specific indication that they are aimed exclusively or mainly at doctoral students;<\/li>\n<li>(up to 20 hours) activities on soft skills, research management, European and international research systems, entrepreneurship, intellectual property, etc. See e.g., the PhD+ and CyB+ courses offered by the University of Pisa program <a href=\"https:\/\/contaminationlab.unipi.it\/en\/home-english\/\">Contamination Lab<\/a>, and\u00a0the <a href=\"https:\/\/www.unipi.it\/didattica\/corsi\/dottorati\/dottorandi\/didattica-trasversale\">cross-curricular educational activities<\/a>\u00a0 (open science, soft skills, English for research publication and presentation, etc.) offered at the University of Pisa.<\/li>\n<\/ul>\n<div>The courses with exams and training activities without exams should be selected among the ones made available by:<\/div>\n<ul>\n<li>our Ph.D. program (see below) and by the other 4 Ph.D. programs of <a href=\"https:\/\/phd-ai.it\/\">PhD-AI.it<\/a>;<\/li>\n<li>by the <a href=\"https:\/\/dottorato.di.unipi.it\/phd-programme\/teaching\/\">Ph.D. program in Computer Science<\/a> at the University of Pisa or by Ph.D. programs at the host University;<\/li>\n<li>by other Italian and International universities or research institutions (subject to approval).<\/li>\n<\/ul>\n<p>The list of curses of the previous Academic Years are available: <a href=\"https:\/\/phd-ai-society.di.unipi.it\/training-2024-2025\/\">2024-2025<\/a>, <a href=\"https:\/\/phd-ai-society.di.unipi.it\/en\/?page_id=3849\">2023-2024<\/a>,\u00a0<a href=\"https:\/\/phd-ai-society.di.unipi.it\/en\/?page_id=3697\">2022-2023<\/a>, <a href=\"https:\/\/phd-ai-society.di.unipi.it\/en\/?page_id=3702\">2021-2022<\/a>.<\/p>\n<p>First year Ph.D. students have to <strong>submit their study plan by 20th December<\/strong>. Second and third year Ph.D. students may update their study plan yearly by the same deadline. Study plans shall be agreed with the PhD student supervisors (or with the principal investigator of the Ph.D. scholarship, if supervisors have not been finalized yet).<\/p>\n<p style=\"text-align: center\"><em><strong>To submit\/update your study plan, please <a href=\"https:\/\/phd-ai-society.di.unipi.it\/wp-content\/uploads\/sites\/6\/2024\/12\/StudyPlan_SURNAME.docx\">fill and sign this template<\/a>, and submit it <a href=\"https:\/\/forms.office.com\/e\/JzDNSMHzy9\">through this form<\/a>.<\/strong><\/em><\/p>\n<p style=\"text-align: left\">You can attend the courses freely. It may be useful for the lecturers to know the number of interested students. Thus, it would be kind of you to send an email to the lectures of courses included in your study plan to let them know that you intend to attend the course.<\/p>\n<p><strong>Certification of attendance\/exam.<\/strong> After passing the exam (or attending the course, if it is without exam), please ask the lecture to fill and sign the <a href=\"https:\/\/phd-ai-society.di.unipi.it\/wp-content\/uploads\/sites\/6\/2026\/03\/Attendance_STUDENTSURNAME_COURSEACRONYM.docx\">attendance statement<\/a> (for summer schools, the certificate provided by the organizer is ok). Keep all statements in PDF with you. The statements will have to be attached at the yearly report you will submit for the yearly assessment in October next year.<\/p>\n<p>&nbsp;<\/p>\n<h1 class=\"entry-title bgpantone bgtitle py-1\" style=\"text-align: justify\">Ph.D. courses &#8211; Academic Year 2025-2026<\/h1>\n<p>&nbsp;<\/p>\n<p>Last update: 17 November 2025. Number of courses: 65.<\/p>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Advanced Laboratory of Complex Network Analysis<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 di Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Computer Science Dept. University of Pisa, Largo Bruno Pontecorvo<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giulio Rossetti, Barbara Guidi<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giulio.rossetti@isti.cnr.it, barbara.guidi@unipi.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">48<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">September-December 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\">\n<p><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Delving deep into the intricacies of complex systems\u2014be they social, biological, or technological\u2014is vital for accurately modeling and effectively tackling the pressing issues that define our world today. Take, for instance, the need to diminish polarization and radicalization in online discussions, predict high-frequency financial transactions or understand the dynamic interactions among proteins.<\/span><\/p>\n<p>This laboratory course aims to equip students with the tools to construct robust pipelines for analyzing complex systems derived from real-world data and represented as graphs. It will introduce key methodologies for data collection and preprocessing for graph analysis, delve into models designed to enhance the graph\u2019s descriptive capabilities and practical applications and explore techniques for evaluating experimental outcomes. The course emphasizes hands-on learning, empowering students to gain practical insights into these concepts through the utilization of dedicated Python libraries.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Where to start: Formulating Hypotheses<br \/>\nModeling Choices: From simple graphs to advanced models<br \/>\nNetwork Sampling<br \/>\nData Collection: API &amp; Web Scraping<br \/>\nGraph Transformation<br \/>\nFeature-rich modeling<br \/>\nHow to Validate: check the statistical significance of network-based studies<br \/>\nExperiment reproducibility &amp; Open Science<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/elearning.di.unipi.it\/course\/view.php?id=1113\">https:\/\/elearning.di.unipi.it\/course\/view.php?id=1113<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Advanced Methods for Complex Systems<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza San Francesco 19, IMT Campus<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Diego Garlaschelli<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">diego.garlaschelli@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Second half on May 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course has the following learning goals:<br \/>\n\u2022 Identifying real-world situations where methods based on traditional hypotheses such as homogeneity, independence, additivity, ensemble equivalence, or scale separation fail.<br \/>\n\u2022 Familiarizing with non-Gaussian alpha-stable distributions, correlation matrices deviating from random Wishart matrices, ensemble non-equivalence in networks, non-Shannon entropies and generalized maximum-entropy distributions, self-organization in network dynamics, network renormalization.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\"><span class=\"field-content\">1. Introduction:<br \/>\npresentation of the main topics of the course.<\/span><\/span>2. Beyond the Gaussian (theory and examples from time series in econophysics):<br \/>\nsums of independent random variables; from Bernoulli to Binomial; from Binomial to Gaussian; the Central Limit Theorem; random walks versus empirical log-return distributions in financial markets; alpha-stable distributions and their asymptotic properties; non-Gaussian attractors and the Generalized Central Limit Theorem; stylised facts of univariate financial time series.3. Beyond independence (in multivariate time series and correlation matrices):<br \/>\ncorrelation matrices (definition and examples from multivariate time series in financial markets and neuroscience); correlation networks; Random Matrix Theory, the Wishart ensemble and the Marcenko-Pastur eigenvalue distribution; interpretation of empirical eigenvalue and eigenvectors of correlation matrices; decomposition of correlation matrices in random, group, and global modes; community detection for correlation matrices.4. Beyond ensemble equivalence:<br \/>\nDefinitions of equivalence of canonical and microcanonical ensembles in statistical physics (measure, macrostate and thermodynamic equivalence); evidences for the breakdown of ensemble equivalence in physical systems and in networks; generalized correspondence between ensembles; minimum description length of canonical and microcanonical models.5. Beyond Shannon entropy:<br \/>\nShannon-Khinchin and Shore-Johnson axioms; generalizations of the axioms; the Uffink family of entropies and Renyi entropy; the uninformativeness axiom and the maximum-likelihood estimation of the entropic parameter; the generalized maximum-entropy principle; the q-exponential distribution.6. Beyond the separation of time-scales in in network dynamics:<br \/>\n(de)coupling dynamics and structure in network models; the Bak-Sneppen model and self-organized criticality; the fitness network model; feedback between dynamics and topology in self-organized network models; the Bouchaud-Mezard model and real-world networks.7. Beyond the separation of structural scales in networks:<br \/>\nchallenges around coarse-graining in networks; different approaches to network coarse-graining: geometric, Laplacian and multiscale network renormalization; applications of multiscale network renormalization in the quenched and annealed settings.<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/sys.imtlucca.it\/program-overview\/complex-systems-and-networks-cn\">https:\/\/sys.imtlucca.it\/program-overview\/complex-systems-and-networks-cn<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Advanced Topics in Machine Learning<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza S. Francesco 19, IMT Lucca, Classroom to be chosen<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Stefano Gnecco<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.gnecco@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">July 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course provides an introduction to the theory behind some advanced machine learning techniques, including some topics of recent research. MATLAB implementations of most of the techniques examined in the course are described in the related course \u201cMATLAB for Data Science\u201d.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Lecture 1: Advanced treatment of principal component analysis and linear discriminant analysis.<br \/>\nLecture 2: Convergence analysis of batch gradient descent and stochastic gradient descent.<br \/>\nLecture 3: The perceptron learning algorithm. Backpropagation.<br \/>\nLecture 4: Matrix completion and its application to recommendation systems.<br \/>\nLecture 5: Network Lasso.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">AI Bearing with the AI Act, research exemptions and other traps: navigating legal and ethical dimensions<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Superiore sant&#8217;Anna Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Prof. dr. Giovanni Comand\u00e8<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">g.comande@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">15<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\"><span class=\"field-content\">agreed online with stundents on Jan. 14 2026 at 1400<br \/>\nhttps:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NzZiZmNjZTktNGJjNy00NWNjLTkyNzQtMGQ4YTI4NzEyMTU5%40thread.v2\/0?context=%7b%22Tid%22%3a%22d97360e3-138d-4b5f-956f-a646c364a01e%22%2c%22Oid%22%3a%22e574304f-dfca-439c-98fc-250f0987eb3e%22%7d<\/span><\/span>PLEASE CONNECT to this short online schedling meeting<\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course introduces the candidates to the key elements of the AI Act. It analyses the twists and thorns of the rules \u201cin favor\u201d of research and SME and casts regulation in the framework of compliance needs and ethical constraints.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Will be shared with students that enrol writing to the teacher<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">NO<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">AI, Design and Society<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 di Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">DETAILLs Living Lab, Via San Martino, 3, Pisa (PI)<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Cycle of seminars<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giula Giunti, Angela Zammuto, Silvia Benevenuta, Alessandro Lolli, Francesco Catelani, Alessio Malizia, Luca Pappalardo, Paolo Ferragina, Rappresentanti dell&#8217;ADI (Associazione Dottorandi e Dottori di Ricerca in Italia), Lorenzo Angeli<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">detaills.project@ing.unipi.it, filippo.chiarello@unipi.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">16<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">20.10.2025 Academic Work-Life Balance: Mission Impossible?<br \/>\n27.10.2025 Gen AI &amp; Scuola: come educare all\u2019AI<br \/>\n30.10.2025 L\u2019impatto della comunicazione nel XXI secolo<br \/>\n07.11.2025 Il futuro della Fama nel mondo digitale<br \/>\n10.11.2025 Visione del film &#8220;Ex Machina&#8221; e discussione<br \/>\n11.11.2025 The Doctorate Blueprint: Una Guida Pratica e Completa per Laureati e Giovani Accademici in Discipline scientifiche e Ingegneria<br \/>\n20.11.2025 Io, chatbot: le leggi della robotica di Asimov nel mondo di oggi<br \/>\n25.11.2025 L&#8217;evoluzione dei Motori di Ricerca: da Strumenti a Smart Assistants<br \/>\n04.12.2025 Dottorato in Italia: Il ruolo dell\u2019ADI tra rappresentanza, tutela ed ascolto<br \/>\n12.12.2025 Esplorare gli impatti sociali della digitalizzazione con il gioco di ruolo dal vivo<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\"><span class=\"field-content\">The series of events offers an interdisciplinary journey exploring the impacts and applications of artificial intelligence in contemporary society \u2014 across personal, cultural, and collective dimensions.<br \/>\nThrough talks, debates, readings, and screenings, participants will be encouraged to reflect critically and creatively on how emerging technologies are transforming communication, knowledge, research, and human relationships.<\/span><\/span>Aimed at PhD candidates in AI &amp; Society, the series of events seeks to foster a deep and responsible understanding of the relationship between humans and artificial systems, promoting an open dialogue between science, ethics, and society.This is a modular course, with recognition granted for each individual event. Events will be held in Italian, with the possibility of English upon request at the time of registration. Registration is mandatory via Eventbrite.<\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\"><span class=\"field-content\">20.10.2025 \u2013 Academic Work-Life Balance: Mission Impossible?<br \/>\nAnalysis of work-life boundaries and well-being in academic careers.<\/span><\/span>27.10.2025 \u2013 Gen AI &amp; Scuola: come educare all\u2019AI<br \/>\nReflection on educational practices and challenges in teaching responsible AI use.30.10.2025 \u2013 L\u2019impatto della comunicazione nel XXI secolo<br \/>\nExploration of relational paradigms, nonviolent communication, and structural violence in digital society.07.11.2025 \u2013 Il futuro della Fama nel mondo digitale<br \/>\nDiscussion on fame, technology, and power through literature, graphic art, and digital culture.10.11.2025 \u2013 Visione del film \u201cEx Machina\u201d e discussione<br \/>\nViewing and debate on artificial intelligence, consciousness, and ethics in cinema.11.11.2025 \u2013 The Doctorate Blueprint: Una Guida Pratica e Completa per Laureati e Giovani Accademici in Discipline scientifiche e Ingegneria<br \/>\nPresentation of a practical guide to doctoral research, supervision, and career development in science and engineering.20.11.2025 \u2013 Io, chatbot: le leggi della robotica di Asimov nel mondo di oggi<br \/>\nLive readings and musical reflections on Asimov\u2019s legacy and contemporary AI ethics.25.11.2025 \u2013 L\u2019evoluzione dei Motori di Ricerca: da Strumenti a Smart Assistants<br \/>\nOverview of the technological transformation from early search engines to intelligent digital agents.04.12.2025 \u2013 Dottorato in Italia: Il ruolo dell\u2019ADI tra rappresentanza, tutela ed ascolto<br \/>\nOpen dialogue on rights, representation, and community building within Italian doctoral education.12.12.2025 \u2013 Esplorare gli impatti sociali della digitalizzazione con il gioco di ruolo dal vivo<br \/>\nExperiential workshop using live-action role-play to reflect on the personal and social effects of digital transformation.<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/www.eventbrite.it\/o\/detaills-106457636291\">https:\/\/www.eventbrite.it\/o\/detaills-106457636291<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Applied Statistical Modelling 2<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna, Pisa, Italy<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Post graduate Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Valentina Lorenzoni<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">valentina.lorenzoni@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">To be defined, approximately March<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course aims to provide students with methodological and applied background of models for time-to-event data (focusing on survival analysis and specifically on Cox proportional hazard model and on models for competing risks), models for constrained and censored response variables focusing on survival analysis, beta and tobit regression providing a practice-oriented approach with applications in the context of social sciences.<br \/>\nThe course assumes prior knowledge of foundations of Probability, Inferential Statistics and Regression models<br \/>\nSyllabus: Intro to time-to-event data; Life tables; Main statistical methods for survival analysis<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Introduction to time-to-event data; Describe time-to-event data; Modelling time-to-event data; The proportional hazard Cox model; Extension of the Cox proportional hazard model; Introduction to competing isk analysis; Constrained regression; Beta regression; Tobit regression<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/github.com\/EMbeDS-education\/ComputingDataAnalysisModeling20252026\">https:\/\/github.com\/EMbeDS-education\/ComputingDataAnalysisModeling20252026<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Artificial Intelligence in Criminal Law and Justice: Challenges and Perspectives<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Sant&#8217;Anna School of Advanced Studies, Pisa, P.za Martiri della Libert\u00e0 33, room tbd + online Teams<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Prof. Gaetana Morgante, Dr. Gaia Fiorinelli<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">gaetana.morgante@santannapisa.it; gaia.fiorinelli@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Tentative timetable: March\u2013April, one 2-hour class per week<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course aims to explore the complex relationship between artificial intelligence and criminal law and justice. In the first part, it focuses on prohibited, unlawful and malicious uses of AI, as well as on models of criminal responsibility and culpability where harm or unlawful outcomes arise from AI systems. In the second part, the course will investigate the use of AI in criminal justice, covering all phases from policing to risk assessment, sentencing and detention. The course aims to provide students with a critical understanding of the theoretical, social and legal implications of the use of AI in these contexts, with reference to AI regulations, fundamental rights, and criminal law principles. Particular attention will be devoted to international, European, and national frameworks such as the EU AI Act, the Council of Europe Framework Convention on AI, various EU and CoE recommendations and resolutions on AI in criminal justice, and especially the recent Italian Law on AI (Law No. 132\/2025). No prior knowledge of criminal law is required, as the course is intended to foster interdisciplinary dialogue among students from different academic backgrounds.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">-The Malicious Use of AI: Between Prohibited Practices and Criminal Law Principles<br \/>\n-Models of Criminal Liability for the use of AI systems<br \/>\n-Predictive Policing and AI: Applications, Risks, and Legal Safeguards<br \/>\n-AI in Risk Assessment and Sentencing: Redefining Dangerousness and Recidivism<br \/>\n-AI in Prisons and Probation: Implications for Fundamental Rights and Rehabilitation<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Bioinformatics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Normale Superiore<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Normale Superiore, Carovana Building<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Post graduate Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Francesco Raimondi<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">francesco.raimondi@sns.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">40<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">End of January &#8211; April<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Aim of the course is to provide students with the basic knowledge of bioinformatics techniques as an easy and friendly support for their study and research careers. This will entail: 1) theory of the most common bioinformatics algorithms and resources: who they are, what they do and why they are so important and increasingly used in modern biology research; 2) basic practical experience through hands-on-sessions on typical problems that can be answered by using popular online tools.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1) Introduction to bioinformatics<\/span><\/p>\n<p>2) Biological databases<\/p>\n<p>3) Pairwise sequence alignments<\/p>\n<p>4) Basic Local Alignment Search Tool (BLAST)<\/p>\n<p>5) Multiple sequence Alignment<\/p>\n<p>6) Molecular Phylogenetics<\/p>\n<p>7) Protein domains and proteome modularity<\/p>\n<p>8) Protein structure analysis, alignments and classification<\/p>\n<p>9) Protein structure prediction<\/p>\n<p>10) Biomolecular interaction networks<\/p>\n<p>11) hands-on (based on available freely available webservers and softwares): a) sequence alignments, b) protein structure analysis and prediction, c) molecular interaction network analysis (visualization, annotation and functional enrichment)<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/www.sns.it\/en\/corsoinsegnamento\/bioinformatics\">https:\/\/www.sns.it\/en\/corsoinsegnamento\/bioinformatics<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Biostatistics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna, Pisa, Italy<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Valentina Lorenzoni<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">valentina.lorenzoni@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">16<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">February\/March<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course offers an introduction into main concept and measures used in epidemiology and offers a snapshot of main statistical methods for the analysis of epidemiological and clinical data. The course will consist of both theoric and applied classes to let students familiarize with biostatistical methods and with practical problems in the analysis of real data. The course will also provide cues for a correct use of methods, interpretation and critical appraisal of analyses.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Introduction to epidemiology; Relation and agreement; Linear regression; Logistic regression; Dealing with collinearity, confounding and interaction; Survival analysis<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/github.com\/EMbeDS-education\">https:\/\/github.com\/EMbeDS-education<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Causal Inference in Macroeconometrics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna, Piazza Martiri della Libert\u00e0 33, Pisa. Check room number here: https:\/\/www.santannapisa.it\/it\/calendar-classes<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Alessio Moneta<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">a.moneta@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">April-May 2026. Link to the timetable: https:\/\/sites.google.com\/view\/alessiomoneta\/teaching<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\">\n<p><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course aims at addressing the problem of identifying and quantifying causal relationships in macroeconomics. The course will deliver an overview of methods that allow researchers to estimate causal effects from time-series data in a non-experimental setting. <\/span><\/p>\n<p>Outline:<\/p>\n<p>&#8211; A Historical Perspective on Causal Inference in Macroeconometrics<\/p>\n<p>&#8211; The Structural Vector Autoregressive Model: Identification Strategies<\/p>\n<p>&#8211; Causal Inference by Graphical Causal Models (an Introduction)<\/p>\n<p>&#8211; Causal Inference by Independent Component Analysis<\/p>\n<\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">https:\/\/www.dropbox.com\/scl\/fi\/6peb6sq8emcyasiacwcg2\/Syllabus_causal_inference_macro.pdf?rlkey=zri2kyb4h05ed5ovq919nfsbo&amp;dl=0<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/sites.google.com\/view\/alessiomoneta\/teaching\">https:\/\/sites.google.com\/view\/alessiomoneta\/teaching<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Cloud Computing &amp; Big Data Lab<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Sant&#8217;Anna TECIP Institute, CNR Area<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tommaso Cucinotta<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tommaso.cucinotta@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">30<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The course begins typically in April\/May.<br \/>\nSee also: https:\/\/retis.santannapisa.it\/~tommaso\/eng\/courses\/CloudComputingBigDataLab.html<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This is a hands-on and applied course following up to the Cloud Computing &amp; Big-Data course. Here, students will put in practice the theoretical\/abstract concepts acquired in the general course on Cloud Computing &amp; Big-Data. During the practical sessions, we&#8217;ll have a deep dive on such concepts as: machine virtualization and OS-level virtualization on Linux; virtual networking on Linux; programming abstractions for cloud and distributed computing; elasticity in practice; big-data programming frameworks; command-line interface for major public cloud services; popular open-source cloud platforms.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Virtualization Fundamentals<br \/>\nKVM Command-Line Interface<br \/>\nlibvirt and virtual-manager<br \/>\nVirtual Switching on Linux<br \/>\nbrctl and OpenVSwitch<br \/>\nContainers<br \/>\nLXC and netns<br \/>\nPublic Cloud Services<br \/>\nAWS EC2, CloudWatch<br \/>\nAWS S3, DynamoDB<br \/>\nOpen-source cloud platforms<br \/>\nOpenStack Nova, Glance, Neutron<br \/>\nOpenStack Heat\/Senlin, Ceilometer\/Monasca<br \/>\nKubernetes<br \/>\nPlatforms for Big Data and Analytics<br \/>\nMap Reduce<br \/>\nApache Spark<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/retis.santannapisa.it\/~tommaso\/eng\/courses\/CloudComputingBigDataLab.html\">https:\/\/retis.santannapisa.it\/~tommaso\/eng\/courses\/CloudComputingBigDataLab.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Cloud Computing &amp; Big-Data<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Sant&#8217;Anna TECIP Institute, CNR Area<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tommaso Cucinotta<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tommaso.cucinotta@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">30<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The course begins in mid\/late November, typically with 3-hours lectures twice a week.<br \/>\nFor details, see https:\/\/retis.santannapisa.it\/~tommaso\/eng\/courses\/CloudComputingBigData.html<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This course provides an overview of the challenges to face, and the technical solutions to embrace, when building large-scale, fault-tolerant, distributed and replicated real-time cloud services. These systems need to be capable of serving millions\/billions of requests per second with industrial-grade reliability, availability and performance, and are composed of thousands of components spanning across millions of machines, worldwide. The course focuses on design, development and operations of scalable software systems, including big-data processing and analytics, as used increasingly often for nowadays intensive computations needed to train large machine-learning and artificial intelligence models, where the huge volumes of data to handle mandates the use of heavily distributed algorithms. The course covers also basic concepts on networking architectures for data-centre and cloud computing infrastructures.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Cloud Computing<br \/>\nBasic concepts<br \/>\nScalability and elasticity in cloud systems<br \/>\nFault-tolerance and replication<br \/>\nReal-time cloud services<br \/>\nOperations and devops engineering<br \/>\nBig Data and Analytics<br \/>\nBasic concepts<br \/>\nReal-time data streaming and analytics<br \/>\nDistributed file-system<br \/>\nSQL vs NoSQL data-base systems<br \/>\nBig-Data and the Internet of Things<br \/>\nPlatforms<br \/>\nOverview of public cloud services (AWS EC2, Google GCP, &#8230;)<br \/>\nApache Hadoop, Storm, Spark<br \/>\nMap Reduce<br \/>\nOpenStack<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/retis.santannapisa.it\/~tommaso\/eng\/courses\/CloudComputingBigData.html\">https:\/\/retis.santannapisa.it\/~tommaso\/eng\/courses\/CloudComputingBigData.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Complexity in Ecology<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies, Piazza S. Francesco 19, Lucca; the link for online classes is different on each day<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Andrea Perna<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">andrea.perna@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The course is currently scheduled for the following dates:<br \/>\n25th, 26th and 28th May 2026,<br \/>\n3rd and 4th June 2026<br \/>\nAlways at 4:00 PM.<br \/>\nIt is advised to check with the lecturer for possible changes of time or date.<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This short module is designed to provide a basic understanding of the way how ecological systems self-assemble and function, and of the mathematical and computational tools that can be used to characterise these systems.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">* Patterns at the individual level: scaling of ontogenetic growth, movement and metabolism.<br \/>\n* Patterns at the level of groups and populations: group-size distribution, collective behaviour.<br \/>\n* Patterns at the level of ecological communities and ecological interactions (size-abundance distribution, ecological networks).<br \/>\n* Ecosystem-level patterns: diversity and productivity, geographic variation.<br \/>\n* Ecosystems through change: multiple stable states and ecological transitions.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Computational Econometrics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Sant&#8217;Anna School of Advanced Studies<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Mario Martinoli<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">m.martinoli@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">6<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">https:\/\/sites.google.com\/view\/mariomartinoli\/teaching<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This crash course introduces a toolkit for taking models to data, covering estimation and inference for simulation models. We will start by briefly reviewing core time-series\/structural concepts and comparing leading simulation-based estimators \u2013 indirect inference, method of simulated moments, simulated maximum likelihood \u2013 and causal-inference-oriented approaches. Then, we will establish definitions and contrasts among calibration, estimation, and validation, alongside a compact primer on structural time series. Finally, we will address identification and inference challenges in complex simulation models, and we will discuss the estimation of the parameters of a simple heterogeneous model with R.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">https:\/\/sites.google.com\/view\/mariomartinoli\/teaching<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/sites.google.com\/view\/mariomartinoli\/teaching\">https:\/\/sites.google.com\/view\/mariomartinoli\/teaching<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Computational Economics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Sede centrale Sant&#8217;Anna, Piazza Martiri liberta&#8217; 33, Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Post graduate Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Fagiolo, Andrea Roventini, Andrea Vandin<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.fagiolo@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">58<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">March-May 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This course is intended to serve as a broad introduction to the huge literature using agent-based computational approaches to the study of economic dynamics. It is organized in three parts. The first one (\u201cWhy?\u201d) will discuss the roots of the critiques to the mainstream paradigm from a methodological, empirical and experimental perspective. We shall briefly review the building blocks of mainstream models (rationality, equilibrium, interactions, etc.) and shortly present some of the evidence coming from cognitive psychology and experimental economics, network theory and empirical studies, supporting the idea that bounded rationality, non-trivial interactions, non-equilibrium dynamics, heterogeneity, etc. are irreducible features of modern economies. In the second part (\u201cWhat?\u201d) we shall discuss what ACE is and what are its main tools of analysis. We will define an ABM and present many examples of classes of ABMS, from the simplest (cellular automata, evolutionary games) to the most complicated ones (micro-founded macro models).The third part (\u201cHow?\u201d) aims at understanding how ABMs can be designed, implemented and statistically analyzed. The course also contains an introduction to programming in Python (Andrea Vandin) and applications of agent-based models to macroeconomics (Andrea Roventini).<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Introduction to programming in Python; agent-based computational economics (why? what? how?); applications of agent-based models to macroeconomics.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/sites.google.com\/view\/giorgiofagiolo\/home#h.p_3wLFg28TkYQH\">https:\/\/sites.google.com\/view\/giorgiofagiolo\/home#h.p_3wLFg28TkYQH<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Computational fluid dynamics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna Main Campus<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giovanni Stabile<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giovanni.stabile@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Starting end of October (classes are recorded)<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course deals with a list of advanced topics in computational fluid dynamics. In the first part, the governing equations governing fluid dynamics problems are reviewed and derived. Both compressible and incompressible flows are considered. The basics of the finite volume methods for the numerical discretization of fluid dynamics problems are reviewed and discussed starting from the basic example of an advection-diffusion equation. Particular emphasis is given to the treatment of non-orthogonality, convection-dominated flows, time discretization, and order of accuracy. Numerical schemes for the treatment of velocity pressure coupling for the incompressible Navier-Stokes equation such as the SIMPLE and the PISO algorithms are reviewed and a discussion on chequerboard effects on collocated grids is provided. The last part of the course focuses on advanced topics related to turbulent and compressible flows. The various alternatives (RANS, LES) for turbulence modeling are introduced and discussed in detail. Finally, an introduction to numerical methods for compressible flows is provided.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">no syllabus<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Computing Methods for Experimental Physics and Data Analysis<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 di Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Universita&#8217; di Pisa, Area Pontecorvo, Edificio B. Online: Teams link should be asked to the lecturers<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Post graduate Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Alessandra Retico, Andrea Rizzi, Francesca Lizzi<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">alessandra.retico@pi.infn.it, andrea.rizzi@unipi.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">40<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Lectures (for PhD students) start on Nov 11th. Depending on the module selected by the student some lectures could be in the second semester. Lectures end by April 2026. Contact the lecturers for additional information.<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">fundamental tools and definitions in machine learning, feedforward networks, CNN, recurrent networks, generative networks (GAN and<br \/>\nautoencoders), graph networks, specific tools for particles physics or medical physics<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">https:\/\/unipi.coursecatalogue.cineca.it\/insegnamenti\/2025\/52569_695968_76342\/2023\/52569\/10452?annoOrdinamento=2023<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/elearning.df.unipi.it\/course\/view.php?id=344\">https:\/\/elearning.df.unipi.it\/course\/view.php?id=344<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Critical Thinking<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza San Francesco 19 55100 Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Gustavo Cevolani<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">gustavo.cevolani@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">April-May 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Constructing and evaluating arguments is fundamental in all branches of science,<br \/>\nas well as in everyday life. The course provides the basic tools to recognize and<br \/>\nanalyze correct forms of inference and reasoning, detect the unsound or fallacious<br \/>\nones, and assess the strength of various kinds of argument. The toolbox<br \/>\nincludes elementary deductive logic, na\u00efve set theory, patterns of inductive and<br \/>\nabductive inference, and the elements of statistical and probabilistic reasoning.<br \/>\nBy engaging in real-world exercises of correct and incorrect reasoning, students<br \/>\nwill familiarize with basic epistemological notions (truth vs. certainty, knowledge<br \/>\nvs. belief, theory vs. evidence, etc.), with the analysis of relevant informal<br \/>\nconcepts (like truth, falsity, truthlikeness, lies, misinformation, disinformation,<br \/>\npost-truth, fake news, etc.) and with common reasoning pitfalls, heuristics and<br \/>\nbiases as investigated in cognitive psychology and behavioral economics.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Reasoning and rationality, knowledge and science.<br \/>\n2. Deductive and non-deductive reasoning.<br \/>\n3. Bayesian reasoning.<br \/>\n4. Heuristics, biases, and fallacies.<br \/>\n5. Reasoning, science, and society.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">Visit the IMT page of the related PhD.<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Developing in Human-Robot Interaction<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 Cattolica del Sacro Cuore<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">TBD<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Online<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Cinzia Di Dio, Antonella Marchetti, Federico Manzi, Giulia Peretti, Laura Miraglia<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">cinzia.didio@unicatt.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">16<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">23 June 2026 (10:00-12:00) \u2013 Di Dio<br \/>\n25 June morning 2026 (10:30-12:30) \u2013 Marchetti<br \/>\n25 June afternoon 2026 (14:30-16:30) \u2013 Manzi<br \/>\n1 July 2026 (9:00-14:00) \u2013 Miraglia<br \/>\n2 July 2026 (9:00-14:00) \u2013 Peretti<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Social robots represent the new frontier of interactions. We are looking at a future in which these agents will be included and integrated within many types of everyday activities, where they will be our new friends, collaborators, educators, and care assistants. In this course we will therefore offer a look at the state of the art in the development of robots as socially effective agents in psychology, highlighting their strengths, and trying to project our thinking into a future where these agents can be perceived as social partners. We will approach to the main psychological developmental steps in early infancy (e.g., gaze, imitation, action understanding), embodied cognition, social cognition (i.e., Theory of Mind) with respect to social and educational robotics. These will help to better understand the role of developmental psychology in AI and Human-Robot Interaction.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Cinzia Di Dio, Module 1 \u2013 Embodied cognition<br \/>\nNeuropsychological background<br \/>\nEmbodied and disembodied intellingence: what differences?<br \/>\nAntonella Marchetti, Module 2 \u2013 Theory of Mind<br \/>\nTheoretical introduction<br \/>\nDevelopmental steps<br \/>\nMethodological issues<br \/>\nMeasuring Theory of Mind<br \/>\nFederico Manzi, Module 3 \u2013Early social cognition<br \/>\nLook at me: gaze following and social cognition<br \/>\nFollow me: action understanding<br \/>\nMirror me: the sense of imitation<br \/>\nLaura Miraglia, Module 4 &#8211; Affective robotics<br \/>\nPsycho-physiological background<br \/>\nEmotional resonance: what matters?<br \/>\nExperimental issues<br \/>\nGiulia Peretti, Module &#8211; Educational robotics<br \/>\nTheoretical background<br \/>\nRobots in schools: what can they do?<br \/>\nExperimental design with infants and toddlers<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">TBD<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Dynamic Factor Models<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza Martiri della Libert\u00e0, 33 &#8211; PIsa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers:<\/strong>Laura Magazzini<\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">laura.magazzini@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">8<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">TBC (please refer to Laura Magazzini for information)<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The analysis of high-dimensional time series has become one of the most active subjects of modern statistical methodology. To achieve dimension reduction, several new analytical and computational techniques have been developed under the name of machine learning methods. Among these factor models not only are one of the pioneering methods in the field of unsupervised learning (dating back to Spearman, 1904), but up to these days have also been one of the most popular and most employed ones. The aim of this course is to provide an introduction to factor models in time series analysis by teaching students the basic theoretical foundations and by illustrating them some applications to econometric analysis (knownledge of time series analysis is required).<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">History and Taxonomy.<br \/>\nRepresentation and identification.<br \/>\nPrincipal component analysis.<br \/>\nQuasi maximum likelihood.<br \/>\nExpectation Maximization algorithm<br \/>\nDynamic principal component analysis.<br \/>\nDetermining the number of factors.<br \/>\nImpulse response analysis and counterfactuals.<br \/>\nCoincident indicators.<br \/>\nNowcasting and forecasting.<br \/>\nThe case of cointegrated factors.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">no link<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Elements of statistical inference and information theory<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Alti Studi IMT Lucca. Piazza S. Francesco, 19 &#8211; 55100 Lucca, LU<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Miguel Ib\u00e1\u00f1ez-Berganza<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">miguel.ibanezberganza@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">30<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">1st (10-hour) third in December 2025; 2nd (10-hour) third in the first half of January 2026; 3rd (10-hour) third in the first half of February<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">An introductory overview of some topics in statistical inference and information theory, often adopting the perspective of statistical physics, and with a background motivational interest in probabilistic approaches to cognitive science. The emphasis is not on formal demonstrations or mathematical proofs.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Outline of the course contents:<\/span><\/p>\n<p>&#8211; Motivational, introductory notions of statistical inference and probabilistic approaches to cognition. Sampling vs inferring. The Markov Chain Monte Carlo method. Statistical ensembles and emergence. Correlations, cumulants, partial correlations, and interactions: the cumulant expansion. Variational free energy. Relation between the Gaussian and Ising models.<br \/>\n&#8211; The inverse problem: Bayesian estimators. Maximum entropy inference: two applications. Unsupervised learning in energy-based neural networks. Expectation-Maximisation learning. Elements of Bayesian model selection. Principal Component Analysis (PCA) as a probabilistic model. Model selection in PCA. Elements of random matrix ensembles. Bayesian inference of correlation and precision matrices. Model selection and clustering: Latent Dirichlet Allocation. Variational inference. Notions of probabilistic models of cognition. The Hierarchical Gaussian Filter.<br \/>\n&#8211; Elements of information theory: information and entropy (Shannon entropy, Fisher information, mutual information, differential information, the data processing inequality). The o-information. Model selection and Minimum Description Length. Information relevance. Model selection and inference of higher-order interaction tensors.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/berganzami.github.io\/inference_entropy.html\">https:\/\/berganzami.github.io\/inference_entropy.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Explainable AI<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Normale Superiore<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Palazzo Carovana, Piazza dei Cavalieri 7, Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Fosca Giannotti and Roberto Pellungrini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">fosca.giannotti and Roberto Pellungrini<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">30<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The course will start on January 19th and February 18th, 4 hours a week. Monday: 14-16, Tuesday: 11-13<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\"><span class=\"field-content\">This (30 hours) course provides a reasoned introduction to the work of Explainable AI (XAI) to date, and surveys the literature with a focus on post-hoc and by-design approaches. We motivate the needs of XAI in real-world and large-scale application, while presenting state-of-the-art techniques and best practices, as well as discussing the many open challenges. An XAI platform with collection of many of the recently proposed algorithms will be presented on specific use cases and it will be possible familiarize with some of the methods.<br \/>\nThe course is organized as follows in three modules: i) an introductory one providing motivations, main concepts and main methods; ii) an advanced one where the students will actively participate to monographs topics with readings interleaved with interventions of international scholars working on the sector; iii) an hands-on module where the students will be introduced to the usage on XAI methods.<br \/>\nThe period is between January 19th and February 18th.<\/span><\/span><\/p>\n<div>To enroll in the course, follow the instructions at the link: <a href=\"https:\/\/www.sns.it\/en\/admission-single-courses\">https:\/\/www.sns.it\/en\/admission-single-courses<\/a><\/div>\n<\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Module1 (10 hours): Crush course on XAI.<br \/>\na. Motivation for XAI: Why explanation and What is an explanation The taxonomy of XAI methods for Machine Learning<br \/>\nb. Overview post-hoc explanation methods<br \/>\nc. Overview of transparent by-design methods<br \/>\n2) Module2 (10 hours): Advanced Concepts<br \/>\na. Counterfactual explanations<br \/>\nb. Explaining by design \u2013 argumentation and knowledge graph \u2013<br \/>\nc. Explaining by design &amp; Global Explainer: on the integration of symbolic and sub-symbolic<br \/>\nd. Interactive XAI \u2013 the new research challenges in XAI<br \/>\ne. Student seminars (4 hours)<br \/>\n3) Module3 (10 hours): Hands-on: on XAI methods. (By Roberto Pellungrini)<br \/>\na. The students will be introduced to python library of XAI-Lib methods for tabular data (4h)<br \/>\nb. The students will be introduced to python library of XAI methods for images data (4h)<br \/>\nc. The students will be introduced to some global explanation method (2h)<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><a href=\"https:\/\/docs.google.com\/document\/d\/1jex-uMZ5hvTuKWu42m1iVdTtefwVRlJv\/edit?usp=sharing&amp;ouid=118028209741916777546&amp;rtpof=true&amp;sd=true\">https:\/\/docs.google.com\/document\/d\/1jex-uMZ5hvTuKWu42m1iVdTtefwVRlJv\/edit?usp=sharing&amp;ouid=118028209741916777546&amp;rtpof=true&amp;sd=true<\/a><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Geospatial Analytics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Consiglio Nazionale delle Ricerche<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Department of Computer Science, University of Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Luca Pappalardo and Mirco Nanni<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">luca.pappalardo@isti.cnr.it, mirco.nanni@isti.cnr.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">42<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">From mid-September to early December.<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The analysis of geographic information, such as those describing human movements, is crucial due to its impact on several aspects of our society, such as disease spreading (e.g., the COVID-19 pandemic), urban planning, well-being, pollution, and more. This course will teach the fundamental concepts and techniques underlying the analysis of geographic and mobility data, presenting data sources (e.g., mobile phone records, GPS traces, geotagged social media posts), data preprocessing techniques, statistical patterns, predicting and generative algorithms, and real-world applications (e.g., diffusion of epidemics, socio-demographics, link prediction in social networks). The course will also provide a practical perspective through the use of advanced geoanalytics Python libraries.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">MODULE 1: Spatial and Mobility Data Analysis<\/span><\/p>\n<p>Fundamentals of Geographical Information Systems<br \/>\nGeographic coordinates systems<br \/>\nVector data model<br \/>\nTrajectories<br \/>\nSpatial Tessellations<br \/>\nFlows<br \/>\nDigital spatial and mobility data<br \/>\nMobile Phone Data<br \/>\nGPS data<br \/>\nSocial media data<br \/>\nOther data (POIs, Road Networks, etc.)<br \/>\nPreprocessing mobility data<br \/>\nfiltering compression<br \/>\nstop detection<br \/>\ntrajectory segmentation<br \/>\ntrajectory similarity and clustering<\/p>\n<p>MODULE 2: Mobility Patterns and Laws<\/p>\n<p>individual mobility laws and models<br \/>\ncollective mobility laws and models<\/p>\n<p>MODULE 3: Predictive and Generative Models<\/p>\n<p>Next-location prediction<br \/>\nSpatial interpolation<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/didawiki.di.unipi.it\/doku.php\/geospatialanalytics\/gsa\/start\">https:\/\/didawiki.di.unipi.it\/doku.php\/geospatialanalytics\/gsa\/start<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Higher Order Interactions<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza San Francesco 19<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tommaso Gili<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tommaso.gili@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">May 2025<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Introduction to higher order systems. Hypergraphs. Directed Hypergraphs and bipartite representation. Simplicial complexes. Nerev complex and Nerve theorem. Flag Complex. Delaunay triangulation. Cech Complexes. Vietoris-Rips complexes. Filtering function and partial order. Oriented simplices. n-chain group. Boundary operators and cycles. Homology group of a complex. Betti numbers. Persistent Homology. Random hypergraphs. Preferential attachemnt for hypergraphs. Complexes based on homogeneous random graphs. Multiparameter random compelxes. Homological connectivity. Dynamical systems on hypergraphs. Diffusion-reaction and synchronization. Master stability function. Temporal higher order interactions. O-info, redundancy and synergy.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Human-AI Coevolution<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 di Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Dipartimento di Informatica, Universit\u00e0 di Pisa. Largo Bruno Pontecorvo 3, 56127 Pisa, aula Seminari Ovest\/Est.<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Dino Pedreschi, Luca Pappalardo<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">dino.pedreschi@unipi.it, luca.pappalardo@isti.cnr.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Spring 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The rise of socio-technical systems, where humans interact with AI assistants and recommenders, poses risks of unintended consequences. Navigation apps like Google Maps may cause congestion by directing too many drivers to the same route; profiling and targeted ads can reinforce biases and inequality; and generative AI chatbots risk declining quality as synthetic data increasingly drives retraining. These issues stem from Machine Learning (ML) models trained on user behaviour, creating feedback loops that shape future choices and data. This course equips students to analyse and model these loops, designing experiments to assess their societal impact and foster responsible AI development.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Lesson 1 Introduction to Human-AI Coevolution. An overview of the course topic, delivered as a lecture with interactive discussions to engage students. (4 hours)<\/span><\/p>\n<p>Lesson 2 Recommender systems: a recap. Overview of the main technologies related to recommender systems.<\/p>\n<p>Lesson 3 The Human-AI Feedback Loop. Exploration and formalisation of feedback loops in ML models, with practical examples from social media, online retail, urban mapping, and chatbot platforms. (4 hours)<\/p>\n<p>Lesson 4 Types of Experiments. An overview of experimental methods to evaluate feedback loop impacts, illustrated with examples from diverse human-AI ecosystems. (4 hours)<\/p>\n<p>Lesson 5 Unintended Consequences in Social Media. Case studies of unintended AI recommendation effects on platforms like Facebook, Twitter, and YouTube.<\/p>\n<p>Lesson 6 Unintended Consequences in Online Retail. Real-world examples of AI recommendation impacts on platforms like Amazon and Spotify.<\/p>\n<p>Lesson 7 Unintended Consequences in Urban Mapping. Analysis of unintended effects of AI recommendations on platforms such as Google Maps and Airbnb.<\/p>\n<p>Lesson 8 Unintended Consequences in Chatbots. Examples of challenges posed by AI-generated chatbots like LLAMA and ChatGPT.<\/p>\n<p>Lesson 9 Open Challenges in Human-AI Coevolution. A discussion of unresolved technical, legal, and political issues in measuring human-AI coevolution.<\/p>\n<p>Lesson 10 Pratice and discussion.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><a href=\"https:\/\/jonpappalord.github.io\/human-ai-coevolution-course\/\">https:\/\/jonpappalord.github.io\/human-ai-coevolution-course\/<\/a><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Introduction to energy and resources economics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Angelo Facchini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">angelo.facchini@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">12<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">June 10 2026: 11-13 and 16-18<br \/>\nJune 17 2026: 11-13 and 16-18<br \/>\nJune 24 2026: 11-13 and 16-18<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course provides a first understanding of resource management and energy economics. The student will become familiar with the basic concepts of resources and energy economics, including the taxonomy of resources, the Hartwick and the Hotelling rules. The second part of the course focuses on energy and electricity, exploring the market mechanisms and the main liberalisation reforms that occurred in Italy.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">The course is organised in the following lectures:<br \/>\n1) Basic Resource Management<br \/>\n2) Introduction to Energy<br \/>\n3) The transition to Renewable Energy Sources<br \/>\n4) Electricity transmission, distribution and markets<br \/>\n5) The liberalisation of electricity markets: the Italian way<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Introduction to Epistemology<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza San Francesco 19, 55100 Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Matteo De Benedetto<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">matteo.debenedetto@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">21\/01\/2026, 28\/01, 30\/01, 03\/02, 04\/02<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">How do we know what we know? This course introduces students to epistemology \u2014 the study of knowledge, belief, and justification \u2014 through examples from scientific practice. We explore questions such as: What does it mean to \u201cknow\u201d something? What makes a belief justified? How is reliable knowledge structured? How should we reason under uncertainty or high-stakes contexts? Designed for participants without prior philosophical background, the course introduces key epistemological concepts \u2014 such as belief, justification, truth, evidence, and epistemic virtue \u2014 through examples drawn from real scientific contexts. Participants will learn to analyze what it means to \u201cknow\u201d something, how to evaluate the reliability of methods and inferences, how practical stakes can sometimes influence our knowledge claims, and how social and ethical factors shape collective knowledge. By the end, students will gain conceptual tools for thinking more clearly and critically about evidence, uncertainty, and expertise in their own research<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">&#8211; Lecture 1 (21st of January 2026): Introduction, What does it mean to<br \/>\nknow something?<br \/>\n&#8211; Lecture 2 (28th of January): Justification and epistemic norms.<br \/>\n&#8211; Lecture 3 (30th of January): The Structure of Knowledge<br \/>\n&#8211; Lecture 4 (3rd of February): Epistemic Risk, Fallibility, and Standards of<br \/>\nKnowledge.<br \/>\n&#8211; Lecture 5 (4th of February): The social dimensions of knowing.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">Visit the IMT website.<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Introduction to Life Cycle Assessment<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Angelo Facchini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">angelo.facchini@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">24 FEB 2026, 16:00 &#8211; 18:00<br \/>\n25 FEB 2026, 16:00 &#8211; 18:00<br \/>\n3 MAR 2026, 16:00 &#8211; 18:00<br \/>\n4 MAR 2026, 16:00 &#8211; 18:00<br \/>\n10 MAR 2026, 16:00 &#8211; 18:00<br \/>\n11 MAR 2026, 16:00 &#8211; 18:00<br \/>\n17 MAR 2026, 16:00 &#8211; 18:00<br \/>\n18 MAR 2026, 16:00 &#8211; 18:00<br \/>\n24 MAR 2026, 16:00 &#8211; 18:00<br \/>\n26 MAR 2026, 16:00 &#8211; 18:00<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course aims to introduce the Life Cycle Assessment to evaluate environmental impact. The student will become familiar with the LCa and other material flow accounting methods. In the first part of the course, the student will become familiar with the basic concepts of MFA and LCA. During the second part, the lectures will focus on using common LCA software to implement case studies of increasing complexity.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">The course is organised in two parts. The first part is composed by the following lectures:<br \/>\n1) Introduction and principles of sustainability<br \/>\n2) Environmental accounting<br \/>\n3) Flows and Life-Cycle<br \/>\n4) LCA: Focus on phases<br \/>\n5) LCA: Systems and processes<br \/>\n6) Operational details and examples<br \/>\nThe second part of the course is devoted to the use of OPENLCA, with different case studies of increasing complexity<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Introduction to Machine Learning<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Normale Superiore<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Palazzo Carovana, Scuola Normale Superiore, Monday: 14-16 Aula Contini, Tuesday: 11-13 aula Bianchi Scienze.<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level:<\/strong>Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Fosca Giannotti and Roberto Pellungrini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">fosca.giannotti@sns.it and roberto.pellungrini@sns.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">40<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Scheduling: Monday: 14-16 Aula Contini, Tuesday: 11-13 aula Bianchi Scienze.<br \/>\nThe course starts on March 2nd, 2026 and ends on May 19th. The exam will consist of a project work and its discussion<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course will introduce the foundations of learning and making predictions from data. A special focus is dedicated to modern Deep Neural Network architectures. The course aims to provide students with basic knowledge of both theoretical foundations and practical aspects of data mining and machine learning, with attention to the overall process of extracting knowledge and its engineering issues. The course is organized around 5 modules: The Knowledge Discovery process and its preliminary steps (4 hours), Unsupervised learning methods and practicals (10 hours), Supervised Learning: methods and practicals (24 hours), Introduction to Deep Learning architectures: methods and hands-on exercises, Design principles and Trustworthy issues on AI systems (2 hours). The course starts on March 2nd, 2026, and ends on May 19th. The exam will consist of a project and its discussion.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\"><span class=\"field-content\">1) Introduction: the Knowledge Discovery process.<br \/>\nKDD process: all the steps at a glance.<br \/>\nData understanding and data exploration.<br \/>\nExercises: hands-on practice on simple case studies using Python libraries<br \/>\nIntroduction to NumPy, Pandas and Seaborn (extra support in lab)<\/span><\/span>2) Unsupervised learning methods: : methods and hands-on exercises<br \/>\nPattern Mining and Association Rules: basic concepts and a-priori algorithm<br \/>\nTutorials: practical exercises on simple case studies using Python libraries3) Supervised learning: methods and practical exercises<br \/>\nClassification: introduction, performance evaluation. A first simple classifier:Decision tree<br \/>\nExercises: hands-on practice on simple case studies using Python libraries<br \/>\nOverview of advanced methods: Random Forest, Support Vector Machine<br \/>\nIntroduction to Neural Networks, project description and assignment<br \/>\nExercises: hands-on practice on advanced classification methods and Neural Networks withPyTorch4) Introduction to Deep Learning architectures: methods and hands-on exercises<br \/>\nConvolutional Neural Networks, theory and practice with PyTorch<br \/>\nRecurrent Neural Networks<br \/>\nAdversarial Generative Networks<br \/>\nTransformers<br \/>\nGraph Neural Networks5) Design principles and Trustworthy issues in AI-based systems<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/www.sns.it\/it\/node\/982175\">https:\/\/www.sns.it\/it\/node\/982175<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Introduction to Network Science<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca, P.zza San Francesco 19, 55100 Lucca (Italy)<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tiziano Squartini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tiziano.squartini@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The timetable may be subject to changes: please write to phd@imtlucca.it and ask to have the calendar shared<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course offers a panoramic view of network science. Following its historical development, we will review the main concepts and methods of this discipline. Moving from the basic, stylized facts characterizing real-world networks, we will describe the most popular techniques to extract information from them<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Introduction to graph theory. Empirical properties of complex networks (scale invariance of the degree, small-world phenomenon, modularity). Network representations (monopartite, bipartite and multilayer; binary and weighted; undirected and directed networks; unsigned and signed networks; hypergraphs; simplicial complexes). Centrality. Ranking and reputation algorithms. Mesoscale structures (communities, core-periphery and bow-tie structures). A primer on dynamical models: Watts-Strogatz and Barabasi-Albert models. A primer on static models: Erdos-Renyi, Chung-Lu and fitness models<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/drive.google.com\/drive\/folders\/1on0TE8dMCLO00iw385EqHRwQtjFGVQJr\">https:\/\/drive.google.com\/drive\/folders\/1on0TE8dMCLO00iw385EqHRwQtjFGVQJr<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Introduction to sustainability and ecological economics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Angelo Facchini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">angelo.facchini@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">15 GIU 2026, 16:00 &#8211; 18:00<br \/>\n18 GIU 2026, 14:00 &#8211; 16:00<br \/>\n22 GIU 2026, 16:00 &#8211; 18:00<br \/>\n26 GIU 2026, 14:00 &#8211; 16:00<br \/>\n2 LUG 2026, 11:00 &#8211; 13:00<br \/>\n3 LUG 2026, 14:00 &#8211; 16:00<br \/>\n8 LUG 2026, 11:00 &#8211; 13:00<br \/>\n10 LUG 2026, 14:00 &#8211; 16:00<br \/>\n13 LUG 2026, 11:00 &#8211; 13:00<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">What is sustainability? Which is the link between the economy and the Environment? Which are the main challenges and the main research topics? Which are the main factors that influence a transition to sustainability?<br \/>\nProviding the first insights to answer the above questions is the aim of this course. Lectures are devoted to the fundamental topics in the field of sustainability science and environmental economics. This last is used as a ground to explain the main differences and similarities between ecological and environmental economics, that will be highlighted and discussed with practical examples.<br \/>\nThe course is divided into the following modules:<br \/>\n1. Introduction to sustainability science (lectures 1-2)<br \/>\n2. Basic principles of environmental and resource economics (lectures 3-7)<br \/>\n3. Methods and applications (lectures 8-9)<br \/>\n4. Advanced and research topics (lecture 10)<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Learning Outcomes:<br \/>\nThis course aims to provide students with fundamental concepts of sustainability science and the economics view of the environment.<br \/>\nUpon completion, participants will have the knowledge and skills to:<br \/>\n1. Have a basic understanding of the principles of environmental and ecological economics, with a clear overview on the fundamental principles for the understanding of human-economy- environment interaction<br \/>\n2. Have a basic understanding of environmental problems and environmental policies.<br \/>\n3. Have a first knowledge of the current research topics, directions, and funding opportunities.<br \/>\nParticipants will also rely on the main topics regarding the European Green Deal and the Ecological transition.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Laboratorio di Tecnologie del Linguaggio<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 degli Studi di Napoli L\u2019Orientale<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Univ. di Napoli L&#8217;Orentale, Dip. di Studi Letterari, Linguistici e Comparati, Via Duomo, 219 80138 Napoli<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Maria Pia di Buono<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">mpdibuono@unior.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">18<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Marzo &#8211; Maggio 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The workshop aims to provide applied knowledge on the main tools for automatic natural language processing.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Theoretical-practical exercises in natural language processing.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/unifind.unior.it\/resource\/af\/643508-120448-PDS0-2024-2-3-18\">https:\/\/unifind.unior.it\/resource\/af\/643508-120448-PDS0-2024-2-3-18<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Legal issues on AI-Applications for vulnerable groups<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Room 5 Sede centrale &#8211; Scuola Superiore Sant&#8217;Anna &#8211; Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Denise Amram<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">denise.amram@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">12<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">8.1.2026 9-13, 14- 18 and 9.1.2026 9-15 Room 5 Sede Centrale.<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The Course focuses on the general legal framework and tailored safeguards applicable to data analysis related to vulnerable individuals\/groups and their impact on policy and law-making.<br \/>\nCase studies will be presented in particular on children, patients, workers, consumers.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Overview on the regulatory framework (EU strategy on data, GDPR, AI-Act) impacting on data-driven and ai-based research life-cycles.<br \/>\n2. Protocols for developers, deployers, and providers of AI-based systems to process general and sensitive data.<br \/>\n3. Case-studies on vulnerable users: consumers, patients, children, workers.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">Link: https:\/\/www.santannapisa.it\/it\/denise-amram<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Logic and Formalized Reasoning<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza San Francesco 19 and Piazza San Ponziano 6<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Cosimo Perini Brogi (SySMA@IMT), Gustavo Cevolani (MoMiLab@IMT)<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">cosimo.perinibrogi@imtlucca.it, gustavo.cevolani@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">May 12&#8211;May 28 (Five Sessions + 1 Optional Backup)<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\">\n<p><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This seminar will provide an hands-on introduction to logic, with a particular focus on natural deduction calculi and their operational semantics. <\/span><\/p>\n<p>It is intended to anyone interested in familiarizing with logic, formal reasoning and their applications (not limited to computer science).<\/p>\n<p>We will explore the fundamental concepts of propositional and predicate logic as a basis for formal thinking, and gain practical experience in using natural deduction systems to construct proofs and reason about logical statements.<\/p>\n<p>We will also delve into dialogue games (game-theoretic semantics), exploring an interactive approach to logical proof and meaning.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Please refer to: https:\/\/drive.google.com\/file\/d\/16ZrQFtg_n-MraDrtCg18L0SA9xGvfDO8\/view?usp=sharing<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/drive.google.com\/file\/d\/16ZrQFtg_n-MraDrtCg18L0SA9xGvfDO8\/view?usp=sharing\">https:\/\/drive.google.com\/file\/d\/16ZrQFtg_n-MraDrtCg18L0SA9xGvfDO8\/view?usp=sharing<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Machine Learning Methods for Physics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 degli Studi di Genova<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Dipartimento di Fisica, Universit\u00e0 degli Studi di Genova, Via Dodecaneso 33, 16146 Genova<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Post graduate Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Dr. Riccardo Torre, Dr. Andrea Coccaro, Dr. Marco Raveri<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">riccardo.torre@ge.infn.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">48<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">https:\/\/corsi.unige.it\/off.f\/2025\/ins\/87930<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\">\n<p><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">What is a machine learning algorithm? Why is machine learning playing a primary role in physics? Which problems can be optimized using it? What is the most suitable algorithm to solve my physics problem?<\/span><\/p>\n<p>These are some of the questions that this course aims to answer, providing students with the state-of-the-art knowledge regarding the usage and understanding of artificial intelligence algorithms applied to physics. The course also focuses on developing a critical comprehension of results, exploring the development of future algorithms, and the most promising technologies.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">The course aims to:<\/span><\/p>\n<p>\u2013 Introduce the concepts of minimization algorithms for a scalar functional (the loss function).<br \/>\n\u2013 Provide the necessary tools for practical course execution, such as Python, Tensorflow, and Pytorch.<br \/>\n\u2013 Cover dense neural networks and examples of their applications in physics.<br \/>\n\u2013 Explore convolutional neural networks and examples of their applications in physics.<br \/>\n\u2013 Discuss recurrent neural networks and examples of their applications in physics.<br \/>\n\u2013 Investigate graph neural networks: inductive bias and examples of their applications in physics.<br \/>\n\u2013 Examine attention mechanisms: transformers and examples of their applications in physics.<br \/>\n\u2013 Study generative neural networks and examples of their applications in physics.<br \/>\n\u2013 Provide an overview of differentiable programming.<\/p>\n<p>The course encompasses these topics to provide students with a comprehensive understanding of machine learning algorithms in the context of physics applications.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/corsi.unige.it\/off.f\/2025\/ins\/87930\">https:\/\/corsi.unige.it\/off.f\/2025\/ins\/87930<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">MATLAB for Data Science<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza S. Francesco 19, IMT Lucca, Classroom to be chosen<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Stefano Gnecco<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.gnecco@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">March-April 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course provides MATLAB implementations of several machine learning techniques.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Introduction to MATLAB. Presentation and discussion of MATLAB code for machine learning techniques, including:<br \/>\n&#8211; principal component analysis;<br \/>\n&#8211; spectral clustering;<br \/>\n&#8211; linear and polynomial regression;<br \/>\n&#8211; bias\/variance trade-off;<br \/>\n&#8211; logistic regression;<br \/>\n&#8211; batch gradient descent and stochastic gradient descent for training perceptrons\/multilayer neural networks;<br \/>\n&#8211; perceptrons\/multilayer neural networks applied to the XOR problem;<br \/>\n&#8211; digit recognition via neural networks;<br \/>\n&#8211; backpropagation with momentum;<br \/>\n&#8211; backpropagation applied to the minimization of the cross-entropy function;<br \/>\n&#8211; comparison of backpropagation applied to the minimization of the cross-entropy function and of the sum of squares error function;<br \/>\n&#8211; spam recognition via support vector machines;<br \/>\n&#8211; matrix completion;<br \/>\nand, if time permits,<br \/>\n&#8211; resampling methods;<br \/>\n&#8211; bounding box identification via the quasi-Monte Carlo method;<br \/>\n&#8211; symmetry and antisymmetry in support vector machine training problems;<br \/>\n&#8211; trade-off between number of examples and precision of supervision in ordinary least squares, weight least squares, and fixed effects panel data models;<br \/>\n&#8211; learning with boundary conditions;<br \/>\n&#8211; learning with mixed hard\/soft constraints;<br \/>\n&#8211; (Linear Quadratic Gaussian) LQG online learning;<br \/>\n&#8211; surrogate optimization for optimal material design;<br \/>\n&#8211; (Radial Basis Function) RBF interpolation;<br \/>\n&#8211; curve identification in the presence of curve intersections.<br \/>\nDepending on the students\u2019 background, additional slides will be presented\/provided to them, illustrating a summary of the theory behind some of the techniques considered in the course.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Maximum-Entropy Models of Complex Systems I<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza San Francesco 19, IMT Campus<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Diego Garlaschelli<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">diego.garlaschelli@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Second half of January 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course has the following learning goals:<br \/>\n\u2022 Identifying qualitative aspects of complexity in a multidisciplinary context, from physics to biology and economics;<br \/>\n\u2022 Achieving command of quantitative methods of inference for complex systems from limited information;<br \/>\n\u2022 Familiarizing with detailed examples of application to complex networks, such as network reconstruction and pattern detection in graphs.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\"><span class=\"field-content\">1. Introduction:<br \/>\npresentation of the course; examples of complex systems in physics, biology, social science and economics; aspects of complexity.<\/span><\/span>2. From small to large systems:<br \/>\nquick overview of the three-body problem; the magnetic pendulum; emergent randomness; empirical \u201cprinciples\u201d of thermodynamics; kinetic theory; statistical thermodynamics; principles of statistical physics (ergodicity, metric transitivity, equal a-priori probability, ensembles versus trajectories); entropy in thermodynamics and physics.3. Entropy in Information Theory and Statistical Inference:<br \/>\nShannon-Khinchin axioms; Shannon Entropy; Jaynes\u2019 Maximum-Entropy Principle; statistical inference; the Maximum Likelihood principle; canonical and microcanonical ensembles; entropy-likelihood relationships; canonical covariance of the constraints.4. Ensembles of random graphs:<br \/>\ncanonical and microcanonical graph ensembles; the Erd\u00f6s-R\u00e9nyi model; link stub reconnection; the local rewiring algorithm; the Chung-Lu model; the Park-Newman model.5. Maximum-entropy (re)formulation of network models:<br \/>\ncanonical ensembles with given number of links and given degree sequence (the canonical Binary Undirected Configuration Model).6. The Configuration Model at work:<br \/>\npattern detection (assortativity and clustering) using the Binary Undirected Configuration Model; graph modelling (the International Trade Network) using the Binary Undirected Configuration Model; the Binary Directed Configuration Model: derivation and use for pattern detection.7. Network Reconstruction:<br \/>\nthe \u201cfitness ansatz\u201d for economic and financial networks; reconstruction of interbank and interfirm networks from aggregate input and output flows.8. Reciprocity:<br \/>\nintroduction to reciprocity in directed networks; definition of reciprocity indicators; joint and conditional reciprocation probabilities; (non-)reciprocated degrees; the Binary Reciprocal Configuration Model; use of the model for triadic motif detection.9. Triadic motifs detection:<br \/>\napplications to food webs, international trade, supply networks, interbank networks.<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/sys.imtlucca.it\/program-overview\/complex-systems-and-networks-cn\">https:\/\/sys.imtlucca.it\/program-overview\/complex-systems-and-networks-cn<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Maximum-Entropy Models of Complex Systems II<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca, p.zza San Francesco 19, 55100 Lucca (Italy)<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tiziano Squartini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tiziano.squartini@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The timetable may be subject to changes: please write to phd@imtlucca.it and ask to have the calendar shared<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course heavily focuses on deeper theoretical aspects of maximum-entropy models and their consequences. Particular emphasis will be put on maximum-entropy models to study weighted complex networks<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">How to build statistical models in a principled way: a review of maximum-entropy models. From null models to true models: weighted reciprocal configuration models and block-structured models. Bipartite formalism for Exponential Random Graph models. Continuous formalism for Exponential Random Graph models. Conditional framework for discrete and continuous Exponential Random Graph models. Information criteria for model selection (Likelihood Ratio Test, Akaike Information Criterion, Bayesian Information Criterion, Minimum Description Length). Applications to economic and financial systems. The course will include an overview of ongoing research carried out by Networks@IMT, thereby offering directions for possible PhD projects in this area<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/drive.google.com\/drive\/folders\/14EXjE_lUhPcbWPsmj5WdXHt7Exe8KMU4\">https:\/\/drive.google.com\/drive\/folders\/14EXjE_lUhPcbWPsmj5WdXHt7Exe8KMU4<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Microeonometrics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza Martiri della Libert\u00e0 33, Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Laura Magazzini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">laura.magazzini@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">16<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">February-March 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course aims at providing students the tools for dealing with microeconomic analysis of the behavior of individuals or firms. Regression methods for the study of panel data models and estimation of limited dependent variable models will be considered. Besides the theoretical background, students will be exposed to the discussion and the analysis of empirical applications. (the course requires basic knowledge of OLS and IV regression methods and maximum likelihood estimation).<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Linear panel data models<br \/>\nNon linear regression models (binary choice, count data)<br \/>\nIntroduction to nonlinear modeling of panel data..<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">no link<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Model reduction, scientific machine learning and data-driven methods for computational mechanics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Biorobotics Institute, Pontedera<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giovanni Stabile<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giovanni.stabile@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">March \u2013 May<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course deals with the main tools to perform dimensionality reduction and data-driven approximation of engineering problems. Both linear (proper orthogonal decomposition) and nonlinear approaches (autoenconders) will be reviewed in relation to dimensionality reduction. The main aspects of defining and designing experiments to implement a data-driven surrogate model will be reviewed. The main techniques discussed are the dynamic mode decomposition, the proper orthogonal decomposition with interpolation, neural networks, and Gaussian progress regression. The methodology is demonstrated using Python examples in Colab.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">no syllabus<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Modern NLP: Mechanistic Interpretability of Large Language Models<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Consiglio Nazionale delle Ricerche<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong>the course will be held on Microsoft Teams, try to join at this link https:\/\/teams.microsoft.com\/l\/channel\/19%3A0E8ryZN6cwb5sV0o0QvSxbDpSqyQQvsQuzg5qcuUfFU1%40thread.tacv2\/General?groupId=f15b142a-ee56-4ac9-92fd-df4dc3eb0ee2&amp;tenantId=34c64e9f-d27f-4edd-a1f0-1397f0c84f94 if it does not work please write to us so we can add you to the Teams channel<\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Online<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giovanni Puccetti, Andrea Esuli<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">g.puccetti92@gmail.com, andrea.esuli@isti.cnr.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable:<\/strong>the course will be held on Tuesdays from 11:00 &#8211; 13:00 starting on Match 24th and ending on May 26th. The classes on the 12th and 19th will be in the afternoon (16:00 &#8211; 18:00)<\/div>\n<div class=\"views-field views-field-field-abstract\">\n<p><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Generative Large Language Models (LLMs) can fluently generate text after training on large amounts of text. LLMs training corpora are so large that extrapolating model behaviour from training data is too challenging. This difficulty popularized Mechanistic Interpretability as an alternative solution. <\/span><\/p>\n<p>Mechanistic Interpretability consists in relating specific model properties and behaviours to mechanistic properties to be able to understand or even control model text generation. In this course we will explore some of the most adopted techniques for mechanistic interpretability of LLMs, among others: outliers in large language models, sparse autoencoders and task vectors. Each of these topics will be presented both from an abstract and a practical perspective.<\/p>\n<p>After understanding these techniques and their current applications we will show ongoing research in this field both from the teachers as well as from invited speakers.<\/p>\n<p>The final exam consists of the presentation of a research paper.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">\u2022 Introduction to Mechanistic Interpretability and LLMs recap<br \/>\n\u2022 Model Structural Properties: Outlier Dimensions (Theory and Hands on)<br \/>\n\u2022 Model Interpretation: Sparse Autoencoders (Theory and Hands on)<br \/>\n\u2022 Model Conditioning: Task Vectors (Theory and Hands on)<br \/>\n\u2022 Guest Lectures on Outliers and Sparse Autoencoders<br \/>\n\u2022 Recent Developments<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">We will shortly provide a webpage<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Moral Reasoning<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza San Francesco 19, 55100 Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Gustavo Cevolani<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">gustavo.cevolani@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">19\/11\/2025, 20\/11, 25\/11, 26\/11, 11\/12.<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The analysis of moral reasoning and surrounding topics \u2013 how to assess<br \/>\n\u201cgood\u201d and \u201cbad\u201d actions, how to choose between different moral principles,<br \/>\nhow to justify these choices \u2013 is a classical problem of moral philosophy. More<br \/>\nrecently, moral psychologists started tackling those problems using a descriptive,<br \/>\nempirically based approach. Even more recently, \u201cneuroethicists\u201d began<br \/>\ninvestigating the neural correlates of moral judgment and the implications<br \/>\nof neuroscientific results for moral philosophy. In the meantime, behavioral<br \/>\neconomists started addressing issues like fairness, altruism, reciprocity and<br \/>\nsocial preferences, documenting the influence of (broadly construed) moral<br \/>\nconsiderations on human decision-making. The course is an introduction to<br \/>\nthe analysis of moral reasoning at the interface between neuroscience, moral<br \/>\npsychology, moral philosophy, and economics. We shall explore problems<br \/>\nconcerning the biological and neural bases of moral thinking, the role of<br \/>\nemotions in moral reasoning, the economic way of interpreting moral behavior,<br \/>\nthe significance of empirical results for normative theories of morality,<br \/>\nand some methodological issues arising within neuroethics.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Presentation, introduction, choice of topics.<br \/>\n2. Moral philosophy: deontology, consequentialism, virtue ethics<br \/>\n3. Moral psychology<br \/>\n4. Neuroethics: moral reasoning and neuroscience<br \/>\n5. Economics and human sociality<br \/>\n6. Objectivity, reason, and facts in moral reasoning<br \/>\n7. Recap, verification and general discussion<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">Visit the IMT website.<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Network Neuroscience and Medicine<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza San Francesco 19<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tommaso Gili<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tommaso.gili@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">16<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">April 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">During the course the main techniques to obtain functional networks from neurovascular and neurophysiological signals of the brain at the mesoscopic scale are introduced. Along with this, the connectome will be discussed as a proxy of structural brain connectedness. Topological properties of functional and structural brain networks, able to discriminate healthy and pathological conditions, will be explained. Advanced methods for model reduction able to coarse-grain brain networks will be discussed.<br \/>\nThe Interactome and the diseasesome will be introduced. Principles of Boolean Networks will be discussed to model patterns of genes expression, transcription and translation. Gene Expression, Transcription, Translation.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Introduction to complex systems. Description of the brain as a complex system. Brain connectivity. Functional MRI. The BOLD signal. Energy consumption at rest.<br \/>\nCoherence in brain tissues, multiscale coherent activity of the brain, functional connectivity, sources of noise, the global signal, from timeseris to functional networks, diffusion tensor imaging, tractography. structural networks.<br \/>\nNeurophysiological signals, Basics of EEG, source of the EEG signal, artefacts, EEG driven brain networks, basics of MEG, source of the MEG signal, MEG driven networks. MEasures of coherence, The Hilbert Transofrmation. Null models.<br \/>\nIntroduction to networks, useful metrics for neuroscience, and interpretation of local and global topological properties. Community detection methods.<br \/>\nAdvanced centrality measures, applications of network neuroscience: altered resting state networks in sedation, dementia and aphasic patients, time scales in brain networks.<br \/>\nSmall worldness. Topological thresholds. Percolation Theory. Application of percolation to functional networks. The maximum spanning tree in healthy subjects and in schizophrenia patients. Allometric relations.<br \/>\nHigher-order interactions in the brain. Coarse-graining the brain. Colouring symmetries and symmetry breaking in the brain. Introduction to the Laplacian of a network.<br \/>\nThe Laplacian renormalisation group. Renormalising the brain. The density operator and the communication distance. Commuication processes in brain networks.<br \/>\nIntroduction to network medicine. The Interactome. The diseasesome. Diseases modules. Drugs and diseases. Gene regulatory networks. Boolean Networks. Gene Expression, Transcription, Translation. The case of the lac operon in E.Coli.<br \/>\nThe gut microbiome: a network approach. Multiborbidity and comorbidity. The Comorbidity Network in COVID-19. The foodome. Complexity of a diet.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Network Reconstruction<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca, p.zza San Francesco 19, 55100 Lucca (Italy)<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tiziano Squartini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tiziano.squartini@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The timetable may be subject to changes: please write to phd@imtlucca.it and ask to have the calendar shared<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course focuses on the topic of network reconstruction. Early attempts to infer missing information about networks will be reviewed, putting particular emphasis on the use of such techniques to reconstruct financial networks<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Literature review about network reconstruction. Early attempts to infer a network structure from partial information (MaxEnt approach; the copula approach; MECAPM; Iterative Proportional Fitting algorithm; Minimum Density algorithm). Monopartite and bipartite financial networks reconstruction via the fitness model. Systemic risk estimation. How to build statistical models in a principled way: maximum-entropy models. Econometric VS maximum-entropy models: a comparison<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/drive.google.com\/drive\/folders\/19-E1MWo7IWiF_WnVKKhY04vNr31waUnJ\">https:\/\/drive.google.com\/drive\/folders\/19-E1MWo7IWiF_WnVKKhY04vNr31waUnJ<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Networks Dynamics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza San Francesco 19<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Tommaso Gili<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">tommaso.gili@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">16<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">January 2025<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Networks growth. Eredos-Renyi Random network. The giant component and the transition to the connected regime. Subcritical and supercritical regime. The growth of a Watss- Strogatz model. Preferential attachment. Degree dynamics. Preferential attachment with fitness. Epidemic modeling. SI, SIR, SIS models. Network epidemics. Synchronization on networks. The master stability function. The Kuramoto model. Diffusion and Random walks on networks. Cooperative systems on networks.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Neural Networks &amp; Learning Machines<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Sapienza Universit\u00e0 di Roma<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Rome, Via Scarpa (Ingegneria@Roma1): I do not know yet the room detail (nor the sharp time).<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Adriano Barra<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">adriano.barra@uniroma1.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">30<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\">\n<p><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">The course is scheduled for the second semester and it should start around April 2026 and it will end up to May 2026: updated information (once available) will appear on the website asap<\/span><\/p>\n<p>https:\/\/www.adrianobarra.com\/neural-networks&#8211;learning-machines.html<\/p>\n<\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Advanced Course for the PhD<br \/>\nFollowing a historical perspective, the course analyzes mathematical models and methods related to the spontaneous information processing capabilities shown by networks of neurons (biological or artificial, once suitably stylized). After summarizing key concepts from statistical mechanics, stochastic processes and statistical inference, the course starts analyzing main models for the emission of an electrical signal by a single neuron. Then we will study how these interact in simple neural architectures, analyzing both the statistical learning capabilities that these networks enjoy as well as their retrieval skills. In particular, due to the Nobel Prize in Physics awarded in 2024 to John Hopfield and Geoffrey Hinton for their pioneering studies on neural networks, particular emphasis will be placed on their contributions and on the close connection that exists between them. The methodological leitmotif will be the statistical mechanics of complex systems (i.e. Parisi&#8217;s theory, Nobel Prize in Physics in 2021) with its associated package of observables and typical tools (replicas, overlaps, etc.).\u200b<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\"><span class=\"field-content\">1) The first section serves to ensure that we share a basic scientific knowledge (obviously a necessary prerequisite to take the first steps together towards a formal mathematical framework for neural networks). In short, the student will be provided with rudiments of Statistical Mechanics, Stochastic Processes and Statistical Inference by revisiting together some fundamental topics (adapted for this course) of canonical relevance of these disciplines.<br \/>\n\u200b<br \/>\n2) The second section instead introduces mathematical methods and models aimed at quantitatively characterizing simple and complex systems in statistical mechanics, which will be fundamental for a subsequent mathematical analysis of the functioning of neural networks from a theoretical and systemic perspective. In this section we develop in detail the mathematical methods necessary to describe and understand the phenomenology that these systems exhibit (from spontaneous ergodicity breaking to spontaneous replica symmetry breaking) by providing ourselves with both very effective yet heuristic methods (widely used in Theoretical Physics approaches \u00e0 la Parisi, e.g. \u201creplica trick\u201d, \u201cmessage passage\u201d, etc.), and more rigorous ones (the prerogative of the know-how of Mathematical Physics \u00e0 la Guerra, e.g. \u201cstochastic stability\u201d, \u201ccavity fields\u201d, etc.).<\/span><\/span>3) The last and most important section is instead completely dedicated to neural networks and follows the main path traced by Amit, Gutfreund &amp; Sompolinsky: after a minimal description (always in mathematical terms) of the key mechanisms of spike emission in biological neuron models -e.g. Stein&#8217;s integrate&amp;fire (as well as their electronic implementation, e.g. Rosenblatt&#8217;s perceptron) &#8211; and the propagation of information between them through axons and dendrites, we will see the limits of single-neuron computation by looking at it from different perspectives (e.g. Minsky &amp; Papert&#8217;s criticism in the construction of logic gates as i.e. the XOR, etc.). Having shifted the focus from the subject (the neuron) to its interactions (neural networks), we will then build neural networks and study their information processing emergent properties (namely those not immediately deducible solely by looking at the behavior of the single neuron), persisting in a statistical mechanics perspective. Specifically, we will try to see how these networks are able to learn and abstract &#8220;archetypes&#8221; by looking at examples supplied by the external world. Subsequently, we will show how these networks use what they have learned to respond appropriately, when stimulated, to the external world by carrying out tasks such as &#8220;pattern recognition&#8221;, &#8220;associative memory&#8221;, &#8220;pattern disentanglement&#8221;, etc.<br \/>\nWe will also understand how these processes can sometimes go wrong, and why.<br \/>\nUsing Hopfield &amp; Hinton&#8217;s neural networks as leitmotif for several variations on this theme, the section will close by showing the deep structural and computational equivalence between these two theories, Hopfield&#8217;s pattern recognition and Hinton&#8217;s statistical learning, unifying these pillars of the discipline in a single and coherent scenario for the whole phenomenon of &#8220;cognition&#8221;: ideally &#8211; and hopefully &#8211; at the end of the course the student should be able to independently continue in the study of these topics. In particular, the student should be able, interacting in a team in the future, to play a complementary role to the figures of the computer scientist and the information engineer, taking an interest in their same topics, but offering a different perspective, intrinsically more abstract and synthetic (that is, where the myriad of algorithmic recipes that we produce every day find a natural placement) helping in the optimization of a research group itself.<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/www.adrianobarra.com\/english-version-nnlm.html\">https:\/\/www.adrianobarra.com\/english-version-nnlm.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Neural Networks and Deep Learning: Advanced Topics<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">https:\/\/teams.microsoft.com\/l\/team\/19%3a41d73846febf4e3ab4b2250c219977ed%40thread.tacv2\/conversations?groupId=d1f2ddc5-becd-4f9d-95d9-7aeeda301f49&amp;tenantId=d97360e3-138d-4b5f-956f-a646c364a01e<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Online<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Buttazzo<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.buttazzo@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">See link at: https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This course presents recent techniques proposed to improve classical neural netowork models and overcome their limitations. Topics include recurrent neural networks, transformers, deep reinforcement learning, semi-supervised and contrastive learning, special deep learning models, neural networks for object tracking, and generative networks.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Recurrent Neural Networks<br \/>\n2. Natural Language processing<br \/>\n3. Transformers<br \/>\n4. Deep reinforcement learning<br \/>\n5. Policy gradient RL<br \/>\n6. Model-based RL<br \/>\n7. Semi-Supervised and Contrastive Learning<br \/>\n8. Special deep learning models<br \/>\n9. Multi-object tracking<br \/>\n10. Generative Networks<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html\">https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Neural Networks and Deep Learning: Implementation Issues<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">https:\/\/teams.microsoft.com\/l\/team\/19%3a41d73846febf4e3ab4b2250c219977ed%40thread.tacv2\/conversations?groupId=d1f2ddc5-becd-4f9d-95d9-7aeeda301f49&amp;tenantId=d97360e3-138d-4b5f-956f-a646c364a01e<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Online<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Buttazzo<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.buttazzo@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">24<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">See link at: https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course focuses on practical and implementation issues useful to deploy neural networks on a variety of embedded platforms using different languages and development environments.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Implementing neural networks in C<br \/>\n2. Implementing reinforcement learning in C<br \/>\n3. Frameworks for training and testing deep neural networks<br \/>\n4. Modeling neural networks in Tensorflow and Pytorch<br \/>\n5. GPU programming in CUDA<br \/>\n6. Accelerating deep networks on GPGPUs<br \/>\n7. DNN optimization for embedded platforms<br \/>\n8. The NVIDIA TensorRT framework<br \/>\n9. Accelerating DNNs on FPGA<br \/>\n10. The Xilinx Deep Processing Unit (DPU)<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html\">https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Neural Networks and Deep Learning: Theoretical Foundations<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">https:\/\/teams.microsoft.com\/l\/team\/19%3a41d73846febf4e3ab4b2250c219977ed%40thread.tacv2\/conversations?groupId=d1f2ddc5-becd-4f9d-95d9-7aeeda301f49&amp;tenantId=d97360e3-138d-4b5f-956f-a646c364a01e<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Online<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Buttazzo<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.buttazzo@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">30<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The aim of the course is to provide key concepts and methodologies to understand<br \/>\nneural networks, explaining how to use them for pattern recognition, image classification, signal prediction, system identification, and adaptive control.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Basic concepts and learning paradigms<br \/>\n2. Unsupervised learning<br \/>\n3. Clustering algoritms<br \/>\n4. Reinforcement learning<br \/>\n5. Supervised learning<br \/>\n6. Performance metrics and RBF networks<br \/>\n7. Towards deep networks: problems and solutions<br \/>\n8. Autoencoders and Convolutional networks<br \/>\n9. Convolutional networks for classification<br \/>\n10. Convolutional networks for object detection<br \/>\n11. Convolutional networks for segmentation<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html\">https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Neural Networks and Deep Learning: Trustworthy AI<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">https:\/\/teams.microsoft.com\/l\/team\/19%3a41d73846febf4e3ab4b2250c219977ed%40thread.tacv2\/conversations?groupId=d1f2ddc5-becd-4f9d-95d9-7aeeda301f49&amp;tenantId=d97360e3-138d-4b5f-956f-a646c364a01e<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Online<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Buttazzo, Giulio Rossolini, Federico Nesti, Daniel Casini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.buttazzo@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">See link at: https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This course present recent methodologies to make deep neural networks more robust, interpretable, and trustworthy. It will coverspecific methods for addressing anomaly detection and adversarial attacks, in the context of computer vision and autonomous driving.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Explainable and Interpretable AI<br \/>\n2. Anomaly and out-of-distribution detection methods<br \/>\n3. Domain generalization and domain adaptation<br \/>\n4. Attention mechanisms in computer vision<br \/>\n5. Adversarial attacks and defenses<br \/>\n6. Real-world attacks and defenses<br \/>\n7. Simulators for autonomous driving<br \/>\n8. HW in the loop simulation<br \/>\n9. Functional components in autonomous driving<br \/>\n10. The Autoware framework for autonomous driving<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html\">https:\/\/retis.santannapisa.it\/~giorgio\/courses\/neural\/nn.html<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Optimal Control and Differential Games<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Lucca, Piazza S. Francesco 19, IMT Lucca, Classroom to be chosen<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Giorgio Stefano Gnecco<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giorgio.gnecco@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">December 2025-January 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course provides an overview of optimal control theory for the deterministic and stochastic cases. Both discrete-time and continuous-time problems are considered, together with some applications to economics.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">\u2013 An overview of optimal control problems.<br \/>\n\u2013 An economic example of an optimal control problem: the cake-eating problem.<br \/>\n\u2013 Dynamic programming and Bellman\u2019s equations for the deterministic discrete-time case.<br \/>\n\u2013 Reachability\/controllability and observability\/reconstructability for time-invariant linear dynamical systems.<br \/>\n\u2013 The Hamilton-Jacobi-Bellman equation for continuous-time deterministic optimal control problems.<br \/>\n\u2013 Pontryagin\u2019s principle for continuous-time deterministic optimal control problems.<br \/>\n\u2013 LQ optimal control in discrete time for deterministic problems.<br \/>\n\u2013 Application of dynamic programming to stochastic and in\ufb01nite-horizon optimal control problems in discrete time.<br \/>\n\u2013 LQ optimal control in discrete time for stochastic problems and Kalman \ufb01lter.<br \/>\n\u2013 Introduction to approximate dynamic programming and reinforcement learning.<br \/>\n\u2013 An economic application of optimal control: a dynamic limit pricing model of the \ufb01rm.<br \/>\n\u2013 An introduction to differential games: an application to transboundary pollution.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Philosophy of Cognitive Science<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza San Francesco 19, 55100 Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Matteo De Benedetto<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">matteo.debenedetto@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">07\/04\/2026, 08\/04, 14\/04, 15\/04, 21\/04<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">How do we really understand the mind? Cognitive science gives us tools to study<br \/>\nperception, memory, language, and reasoning\u2014but underlying every experiment<br \/>\nand model are deep philosophical questions. This course explores how philosophy<br \/>\nhelps make sense of ideas, methods, and results in psychology, neuroscience,<br \/>\nand linguistics. Students will engage with fundamental questions such as: What is<br \/>\nthe mind? Can cognition be reduced to computation? How does language structure<br \/>\nthought? What is the nature of mental representation? What is a good<br \/>\nexplanation of a cognitive phenomenon? How do we define and measure mental<br \/>\ncapacities? Through discussion, conceptual exercises, and real-world examples,<br \/>\nthis course takes a close look at the concepts, assumptions, and frameworks<br \/>\nthat shape the study of cognition, exploring the philosophical foundations of<br \/>\ncognitive science as an interdisciplinary inquiry.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">&#8211; Lecture 1 (7th of April 2026): Introduction. What is Cognitive Science? What is Philosophy of Cognitive Science?<br \/>\n&#8211; Lecture 2 (8th of April): Minds, Brains, and Computation.<br \/>\n&#8211; Lecture 3 (14th of April): Language and Representation<br \/>\n&#8211; Lecture 4 (15th of April): Mechanisms, Models, and Mechanistic Explanations<br \/>\n&#8211; Lecture 5 (21st of April): Operationalism and Construct Validity.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">Visit the IMT website.<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Philosophy of Science<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza San Francesco 19, 55100 Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Gustavo Cevolani; Matteo De Benedetto<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">gustavo.cevolani@imtlucca.it; matteo.debenedetto@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">17\/02\/2026, 18\/02, 24\/02, 25\/02, 03\/03, 04\/03, 10\/03, 11\/03, 17\/03, 18\/03.<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course provides an introduction to the basic concepts and problems in the<br \/>\nphilosophical analysis of scientific reasoning and inquiry. We will focus on some<br \/>\ncentral patterns of reasoning and argumentation in science and critically discuss<br \/>\ntheir features and limitations. Topics covered include the nature of theory and<br \/>\nevidence, the logic of theory testing, and the debate about the aims of science<br \/>\nand the trustworthiness of scientific results. We shall discuss classical examples<br \/>\nand case studies from the history and practice of science to illustrate the relevant<br \/>\nproblems and theoretical positions. Students will freely engage in brainstorming<br \/>\non these topics and are welcome to propose examples, problems, and methods<br \/>\nfrom their own disciplines.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Introduction, discussion and choice of specific topics. What is science?<br \/>\n2. Howmany sciences? The method(s) of science. Exact and inexact sciences.<br \/>\n3. Theories, models, data. Experiments and observations.<br \/>\n4. Inferences in science. Falsification, confirmation, disconfirmation.<br \/>\n5. Bayesian rationality and scientific reasoning.<br \/>\n6. Science, pseudoscience, junk science.<br \/>\n7. History of science and scientific progress. The aim(s) of science.<br \/>\n8. Trust and objectivity in science. The role of experts.<br \/>\n9. Social and human sciences.<br \/>\n10. Science, truth, and reality.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">Visit the IMT website.<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Predictive Models for Time Series Analysis<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 di Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">MS Teams<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level:<\/strong><span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Online<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Francesco Spinnato, Riccardo Guidotti<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">francesco.spinnato@unipi.it, riccardo.guidotti@unipi.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">24<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">From 30\/04\/2026 to 19\/05\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This course on Time Series Analytics is designed to equip students with comprehensive knowledge and skills to analyze, interpret, and build predictive models for time series data. The course covers fundamental concepts, including time series components, normalizations, stationarity, autocorrelation, approximation and various forms of time series transformations, while delving into predictive models passing from DTW based kNN to state-of-the-art kernel-based and dictionary-based approaches. Emphasis is placed on both the theoretical underpinnings and practical applications of these techniques in general-purpose domains. By the end of the course, participants will have developed the analytical acumen and technical expertise necessary to conduct independent research and contribute novel insights to the field of time series analysis. This course is essential for aspiring data scientists, quantitative analysts, and researchers seeking to deepen their understanding and application of time series methods.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">1. Introduction &amp; Preprocessing (4 hours)<br \/>\n2. Distances, Approximation &amp; Global Features (4 hours)<br \/>\n3. Classification &amp; Regression Part 1 (4 hours)<br \/>\n4. Classification &amp; Regression Part 2 (4 hours)<br \/>\n5. Forecasting (4 hours)<br \/>\n6. In-class Project<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/docs.google.com\/document\/d\/1pxMpyYTqXUSAr2kPojST9XniOXuxhVYsT50RDj2PQrc\/edit?tab=t.0\">https:\/\/docs.google.com\/document\/d\/1pxMpyYTqXUSAr2kPojST9XniOXuxhVYsT50RDj2PQrc\/edit?tab=t.0<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Principles of Digital Twins<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca (rooms to be confirmed; possibility to access online from remote via teams)<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Maria Rosaria Marulli, Andrea Mola, Marco Paggi<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">mariarosaria.marulli@imtlucca.it, andrea.mola@imtlucca.it, marco.paggi@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">Timetable to be finalized by the end of October 2025. Approximate period: March-May 2026.<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Digital twins represent the digital counterparts of real processes or products and are one of the enabling technologies for Industry 4.0. Equipped with simulation tools, they can effectively reduce the time requested for product innovation and the associated R&amp;D costs. Moreover, coupled with optimization and control, they can be exploited to test scenarios that cannot be easily assessed experimentally in order to identify innovative optimal solutions. The course covers, in a self-contained manner, the fundamentals of simulation tools to create digital twins for both high-fidelity (model-based) simulations and data-driven (artificial neural networks) models.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Learning Outcomes: <\/span><\/p>\n<p>Ability to create digital twin models of products and processes, integrating computer aided design and real geometrical data with computer aided engineering tools. Both high-fidelity (model-based) simulations and data-driven (artificial neural networks) models are presented and discussed with practical examples.<\/p>\n<p>Lecture Contents:<\/p>\n<p>Introduction to digital twins and their use in technology (1h, Marco Paggi)<br \/>\nData-driven models based on artificial neural networks: neurons, activation functions, cost functions, back-propagation algorithm for a simple artificial neural network (1h, Marco Paggi)<br \/>\nArtificial neural networks for linear and nonlinear classification problems (1h, Marco Paggi)<br \/>\nTime series networks (1h, Marco Paggi)<br \/>\nConvolutional neural networks (1h, Marco Paggi)<br \/>\nFrom Computer Aided Design (CAD) or real geometrical data (from images, laser scanner, etc.) to Computer Aided Engineering (CAE): how to integrate realistic object geometries in simulation tools (3h, Maria Rosaria Marulli)<br \/>\nDigital twins for cultural heritage (2h, Maria Rosaria Marulli)<br \/>\nUser element routines for model-based simulations of surface problems (5h, Maria Rosaria Marulli)<br \/>\nInterfaces between different CAE software (1h, Andrea Mola)<br \/>\nIntegrating functions from software libraries in CAE simulation tools (2h, Andrea Mola)<br \/>\nIntegrating data driven information in numerical models (2h, Andrea Mola)<\/p>\n<p>Teaching Method:<br \/>\nThe lectures will feature both lessons delivered using standard presentations, and hands-on interactive examples.<\/p>\n<p>Bibliography:<br \/>\nSpecific didactic material tailored to the course contents will be provided to the students before the scheduled lessons.<\/p>\n<p>Final Exam:<br \/>\nThe final exam will be based on the evaluation of an application of the taught methodologies to one case study of relevance for the PhD student&#8217;s research.<\/p>\n<p>Prerequisites:<br \/>\nThe course is self-contained. Fundamentals of algebra are required. A general knowledge on CAD and CAE software is recommended, but not essential.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">A link to a shared folder with the didactic material will be provided, please contact marco.paggi@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Programming &amp; Data Analytics &amp; AI for non-computer scientists (PDAI)<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Sede centrale Sant&#8217;Anna, Piazza Martiri delle Liberta&#8217; 33, Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Andrea Vandin, Daniele Licari<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">andrea.vandin@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">40<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">https:\/\/github.com\/EMbeDS-education\/ComputingDataAnalysisModeling20252026\/wiki\/General-Calendar<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\">\n<p><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This course is structured in three modules of 20-hours each (PDAI1, PDAI2-ML, PDAI2-PM) that students can attend in different years. PDAI1 is offered each year, while the other two alternate. PDAI1 is preparatory to the other two, which can be taken independently of each other. In this A.Y., PDAI1 and PDAI2-ML will be offered.<\/span><\/p>\n<p>The course provides a well-structured introduction to the fundamentals of (object-oriented) programming (PDAI1), data processing and artificial intelligence (PDAI2-ML), and process-oriented data science (process mining, PDAI2-PM). The course will focus on how to create good quality software (PDAI1), on how to carry out good quality data analysis and artificial intelligence projects (PDAI2-ML), and on research-oriented aspects related to process-oriented data science, in particular on process mining, where the aim is to analyse and optimise the data-generating process (PDAI2-PM). The student who has achieved the course objectives will gain an understanding of the problems and tasks related to structured programming, data analysis and machine learning in order to be able to make informed decisions. The student will be able to write Python programmes of various kinds, with a focus on complex data analysis and AI tasks, and process mining.<\/p>\n<p>PDAI1 runs in the first semester (November-December), while the modules 2 on the second (usually February). The course has been designed to target non-computer science students with no or limited experience in programming and machine learning. Students will be guided through a step-by-step learning process enabling them to write complex Python programs, with a focus on complex data analysis and machine learning tasks.<\/p>\n<p>It is possible to attend single modules (20h each).<\/p>\n<p>Evaluation: Group project with final presentation and Jupyter notebook documentation.<br \/>\nMaterials: All material will be made available through the course&#8217;s website.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">&#8211; PDAI 1 introduces students to the fundamental principles of structured programming, with basic applications to data processing. It starts from basic notions of programming (variables, data types, collections, control &amp; repetition structures, functions &amp; modules), and progresses to basic data processing functionalities (loading, manipulation, and visualization of CSV data).<\/span><\/p>\n<p>&#8211; PDAI 2-ML introduces students to the components of typical data analysis processes and machine learning pipelines. It first builds the necessary toolset by introducing popular Python libraries for data manipulation\/visualization (NumPy, Pandas, Seaborn, scikit-learn) with simple applications. The toolset is then applied to a more complex case study on the classification of benign and malignant breast cancer, including aspects of data preprocessing, dimensionality reduction, clustering, and classification. The course will conclude with one research-driven topics like process-oriented data science (Process Mining).<\/p>\n<p>&#8211; PDAI 2-PM introduces students to recent data-driven techniques where the main component is the process that generated the data (the data generating process). This is a particularly hot topic, with many companies and researchers involved (see, e.g., the list of industrial that sponsored the reference conference in 2023 https:\/\/icpmconference.org\/2023\/sponsor-and-exhibition\/). We will consider techniques known as Process Mining, in which logs generated during the execution of a process (e.g., an industrial production process, business processes, social system \u2018processes\u2019) are used to infer the structure of the process. Questions of interest are, e.g.: What is the actual process being executed? Are there possibilities for improvement? Does the actual process conform to the intended reference process?<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/github.com\/EMbeDS-education\/ComputingDataAnalysisModeling20252026\/wiki\/PDAI\">https:\/\/github.com\/EMbeDS-education\/ComputingDataAnalysisModeling20252026\/wiki\/PDAI<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Random effects models for multilevel data<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 di Firenze<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Dipartimento di Statistica, Informatica, Applicazioni &#8211; Universit\u00e0 di Firenze, Viale Morgagni 59 &#8211; 50134 Firenze<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Leonardo Grilli, Carla Rampichini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">leonardo.grilli@unifi.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">First semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">12<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">26 to 29 January 2026 (10:00-13:00)<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course introduces the concepts of multilevel analysis, whose main aim is to model the relationships between and within groups. Typical situations include individuals clustered into families, schools, firms, and geographical areas. The course focuses on the two-level linear model as a template to illustrate specification, estimation, and inference issues. The main ideas are illustrated through case studies concerning typical fields of application, such as education (assessing the role of student and school factors on student achievement) and cross-country research (evaluating how individual and country-specific characteristics affect the behaviour of citizens across different countries). The case studies are worked out with Stata. Moreover, each lesson includes guided exercises using Stata. Special attention is devoted to critical and controversial issues, such as group-mean centering of the covariates, sample size requirements, choosing between fixed and random effects, and using sampling weights.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">&#8211; Basics of the two-level linear model: no covariates (random effects ANOVA); covariates at level 1; covariates at level 2<br \/>\n&#8211; Inference<br \/>\n&#8211; Between, within and contextual effects<br \/>\n-Fixed effects versus random effects<br \/>\n-Model specification<br \/>\n-Sample size requirements<br \/>\n-Complex sampling designs<br \/>\n&#8211; Multiple levels of nesting<br \/>\n&#8211; The random effects logit models for binary responses<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">&#8212;<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Responsible Generative AI<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Normale Superiore<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza dei Cavalieri, SNS &#8211; Teams channel will be used managed by the SNS didattica<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Advanced course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Prof. Fosca Giannotti, Gizem Gezici<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">fosca.giannotti@sns.it, gizem.gezici@sns.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">April-May<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The rapid development and deployment of generative AI models and applications has the potential to revolutionise various domains which brings about the urgency to use these models in a responsible manner. Generative AI refers to creating new content in different modalities of digital text, images, audio, code, and other artifacts based on already existing content. Text generator models such as GPT-4, and its chat version, ChatGPT as well as text-to-image models such as DALL-E 3 and Stable Diffusion are popular generative AI models. Although these models have significant implications for a wide spectrum of domains, there are several ethical and social considerations associated with generative AI models and applications. These concerns include the existence of bias, lack of interpretability, privacy, fake and misleading content such as hallucinations. Thus, it is very crucial to discuss these risks with their corresponding potential safeguards (if any) in addition to the technical details of these powerful models.<\/span><\/div>\n<div>To enroll in the course, follow the instructions at the link: <a href=\"https:\/\/www.sns.it\/en\/admission-single-courses\">https:\/\/www.sns.it\/en\/admission-single-courses<\/a><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">https:\/\/docs.google.com\/document\/d\/1mdqoqu7ymz7XDtJaF3tMDT2u5gwt34RaisPbocdzW6M\/edit?usp=sharing<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Secondary use of personal data in AI design and development: How to?<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; offered specifically for the Ph.D. in AI for Society (&#8220;corso erogato dal DIN in AI&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Prof. dr. Giovanni Comand\u00e8<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">g.comande@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">15<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">agreed online with students on Jan. 14 2026 at 1400<br \/>\nhttps:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NzZiZmNjZTktNGJjNy00NWNjLTkyNzQtMGQ4YTI4NzEyMTU5%40thread.v2\/0?context=%7b%22Tid%22%3a%22d97360e3-138d-4b5f-956f-a646c364a01e%22%2c%22Oid%22%3a%22e574304f-dfca-439c-98fc-250f0987eb3e%22%7d<br \/>\nPLEASE connect to this short meeting to schedule<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Every data scientist sooner or later will embark into secondary uses of personal data in AI design and development. The course explores and explain the legal and ethical constraints and develop an hands-on approach enabling the students to pursue their research goals in a legally and ethically compliant way. The exam will consist in drafting tentative protocols for students own research.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Shared with enrolling students<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">no<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Social network analysis<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 di Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Computer Science Dept. University of Pisa, Largo Bruno Pontecorvo<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Dino Pedreschi, Giulio Rossetti<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">giulio.rossetti@isti.cnr.it, dino.pedreschi@unipi.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">48<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">February &#8211; June 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Over the past decade there has been a growing public fascination with the complex \u201cconnectedness\u201d of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. This course is an introduction to the analysis of complex networks, with a special focus on social networks and the Web \u2013 their structure and function, and how it can be exploited to search for information. Drawing on ideas from computing and information science, applied mathematics, economics and sociology, the course describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected. Data-driven analysis of complex networks using a variety of models and software tools.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Real-world network characterization:<br \/>\nBig graph data and social, information, biological and technological networks<br \/>\nThe architecture of complexity and how real networks differ from random networks: node degree and long tails, social distance and small worlds, clustering, and triadic closure.<br \/>\nComparing real networks and random graphs. The main models of network science: small world and preferential attachment.<br \/>\nAssortativity and homophilic behaviors.<br \/>\nStrong and weak ties, community structure, and long-range bridges.<br \/>\nNetwork beyond pairwise interactions: high-order network modeling.<br \/>\nApplications:<br \/>\nRobustness of networks to failures and attacks.<br \/>\nDynamic Network modeling.<br \/>\nDynamic Community Discovery.<br \/>\nLink Prediction<br \/>\nCascades and spreading.<br \/>\nNetwork models for opinion dynamics and epidemics.<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Statistical Learning and Large Data (SLLD)<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">room TBD, Sant&#8217;Anna School (Piazza Martiri della Libert\u00e0, 33 56127 Pisa, IT)<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Post graduate Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Francesca Chiaromonte<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">francesca.chiaromonte@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">40<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">February (Module 1) and March (Module 2)<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">This course will introduce the students to various aspects of contemporary Statistical Learning, with a particular focus on approaches for the analysis of large, complex datasets. The content will be organized in two Modules of 20h each (these can be attended in different years; see Syllabus for topics). Compared to traditional courses on multivariate statistics, regression and linear\/generalized linear models, we will stress the analysis of actual datasets of interest to the students through projects and Practicum sessions associated to each lecture.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Lecture topics will be selected from the following areas:<\/span><\/p>\n<p>Module 1<br \/>\n&#8211; Unsupervised classification; Clustering methods<br \/>\n&#8211; Unsupervised dimension reduction; Principal Components Analysis and related techniques<br \/>\n&#8211; Supervised classification methods<br \/>\n&#8211; Non-parametric regression methods<br \/>\n&#8211; Resampling methods, Cross Validation, the Bootstrap and permutation-based techniques.<\/p>\n<p>Module 2<br \/>\n&#8211; Feature selection and regularization techniques for high-dimensional Linear and Generalized Linear Models<br \/>\n&#8211; Feature screening algorithms for ultra-high dimensional supervised problems<br \/>\n&#8211; Supervised dimension reduction; Sufficient Dimension Reduction and related techniques<br \/>\n&#8211; Subsampling\/partitioning approaches for ultra-high sample sizes<br \/>\n&#8211; Under- and oversampling approaches for data rebalancing<\/p>\n<p>Materials: Our main reference texts will be (a) An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani; see https:\/\/www.statlearning.com\/); (b) Computer Age Statistical Inference (Efron, Hastie). Links to lecture notes, practicum materials and further materials will be provided.<\/p>\n<p>Evaluation: Evaluation will be based on project presentations and written reports to be held\/handed in at the end of the course. Each project will revolve around a dataset, to which students will apply techniques and approaches described during the course \u2013 thus building an overall analysis to be summarized in the final presentation and report. Ideally, students will work on datasets of their own choice. These could be related to their own research, or selected from public sources.<\/p>\n<p>Prerequisites: A working knowledge of basic statistical inference procedures (point estimation, confidence intervals, testing) and linear and generalized linear models. For Module 2, a working knowledge of the methods comprised in Module 1.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/github.com\/EMbeDS-education\/ComputingDataAnalysisModeling20252026\/wiki\/SLLD\">https:\/\/github.com\/EMbeDS-education\/ComputingDataAnalysisModeling20252026\/wiki\/SLLD<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Sustainable policy design<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Angelo Facchini<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">angelo.facchini@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">4 MAG 2026, 09:00 &#8211; 11:00<br \/>\n7 MAG 2026, 11:00 &#8211; 13:00<br \/>\n8 MAG 2026, 14:00 &#8211; 16:00<br \/>\n15 MAG 2026, 14:00 &#8211; 16:00<br \/>\n21 MAG 2026, 11:00 &#8211; 13:00<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course explores the normative foundations of economic analysis, focusing on the design of sustainability-oriented public policies. Using the tools of welfare economics, the course examines the concepts of efficiency, equity and social welfare, considering the extent to which they can guide or need to be rethought in the face of the challenges posed by sustainable development. The fundamental theorems of welfare economics, social choice theory, market failures and ethical frameworks for intergenerational justice will be covered. The course combines rigorous formal analysis with critical reflection on the role of economic policies in promoting societies&#8217; enduring well-being.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Upon completion of the course, the student will be able to:<\/span><\/p>\n<p>1. Understand and apply normative principles of welfare theory to public policy analysis.<br \/>\n2. Critically evaluate efficiency, equity and sustainability in policy design.<br \/>\n3. Integrate formal welfare economics analysis with the challenges posed by environmental and intergenerational sustainability.<\/p>\n<p>Structure of the course:<\/p>\n<p>Lecture 1 &#8211; Introduction to welfare economics and sustainability<br \/>\n\u2022 Normative vs. positive economics: conceptual differences and implications for policy<br \/>\n\u2022 Utility, preferences and well-being: individualistic and collective approaches<br \/>\n\u2022 Sustainability as a normative criterion: intergenerational well-being and ecological constraints<br \/>\nLecture 2 &#8211; Paretian efficiency and the fundamental theorems of welfare economics<br \/>\n\u2022 Paretian efficiency: definition, implications and limitations<br \/>\n\u2022 First and second theorems of welfare economics: formulation and proof<br \/>\n\u2022 Criticisms of the applicability of theorems in sustainability contexts<br \/>\nLesson 3 &#8211; Equity, social choice and collective welfare criteria<br \/>\n\u2022 Functions of social welfare: utilitarianism, egalitarianism, Rawls, Sen<br \/>\n\u2022 Arrow&#8217;s impossibility theorem and its limitations<br \/>\n\u2022 Capabilities approach and pluralism in evaluation criteria<br \/>\nLecture 4 &#8211; Market failures and sustainability.<br \/>\n\u2022 Environmental externalities, public goods, and imperfect information<br \/>\n\u2022 The \u201csecond best\u201d and systemic constraints in policy design<br \/>\n\u2022 Regulatory tools for sustainability: environmental taxation, tradable permits, standards<br \/>\nLesson 5 &#8211; Rethinking well-being: normative perspectives for sustainability<br \/>\n\u2022 Critique of standard models of well-being and growth<br \/>\n\u2022 Alternative indicators: natural capital, subjective well-being, wealth accounting<br \/>\n\u2022 The role of welfare economics in the ecological transition<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Tecnologie del Linguaggio<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Universit\u00e0 degli Studi di Napoli L\u2019Orientale<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Univ. di Napoli L&#8217;Orientale, Dip. di Studi Letterari Linguistici Comparati, Via Duomo, 219 80138 Napoli, Italy e su piattaforma Teams<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Master level<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Maria Pia di Buono<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">mpdibuono@unior.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">36<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">March &#8211; May 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Il corso si propone di fornire agli studenti una conoscenza introduttiva ma solida delle tecnologie del linguaggio, con particolare attenzione all\u2019analisi automatica dei testi e alla traduzione automatica neurale. Gli studenti svilupperanno capacit\u00e0 di comprensione e commento di saggi specialistici, acquisendo competenze fondamentali per la risoluzione di problemi linguistici e traduttologici.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\">\n<p><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">Le lezioni saranno principalmente frontali, con un approccio interattivo volto a facilitare la comprensione dei concetti fondamentali della linguistica computazionale e della traduzione automatica. Saranno analizzati e discussi i testi di riferimento di Jezek &amp; Sprugnoli (2023) e Di Buono (2023), integrati da articoli scientifici recenti che verranno forniti durante il corso.<\/span><\/p>\n<p>Verranno proposti esercizi di analisi critica di testi specialistici e casi di studio per stimolare la riflessione autonoma e l\u2019elaborazione di idee originali.<\/p>\n<p>Sono inoltre previste attivit\u00e0 di laboratorio per l\u2019applicazione pratica degli strumenti di analisi automatica del testo e di valutazione della traduzione neurale.<\/p>\n<\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\"><a href=\"https:\/\/unifind.unior.it\/individual?uri=http%3A%2F%2Firises.unior.it%2Fresource%2Faf%2F644112-119436-MTS%252F2-1-12-72\">https:\/\/unifind.unior.it\/individual?uri=http%3A%2F%2Firises.unior.it%2Fresource%2Faf%2F644112-119436-MTS%252F2-1-12-72<\/a><\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Time Series Analysis<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola Superiore Sant&#8217;Anna<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza Martiri della Libert\u00e0, 33 &#8211; Pisa<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Intermediate course<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">In presence<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers:<\/strong>Laura Magazzini<\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">laura.magazzini@santannapisa.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">20<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">january-february 2026<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">The course aims to cover basic topics in time-series with focus on macroeconomic and financial applications. It will cover univariate and multivariate time serieswith a focus on linear models and their estimation. Such tools are essential for PhD students who aspire to conduct careful, state-of-the-art empirical research. In addition, the course will provide general guidance on formulating and executing (empirical) research ideas.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">\u2013 Stationarity of univariate time series<br \/>\n\u2013 ARMA<br \/>\n\u2013 Unit roots<br \/>\n\u2013 Forecasting<br \/>\n\u2013 Non linear models for volatiltiy ARCH GARCH<br \/>\n\u2013 Stationarity of multivariate time series<br \/>\n\u2013 Wold representation<br \/>\n\u2013 VAR<br \/>\n\u2013 Unit roots and cointegration<br \/>\n\u2013 VECM<br \/>\n\u2013 Impulse response analysis<br \/>\n\u2013 Structural time series models<br \/>\n\u2013 Kalman filter<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">no link<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">NA because the course is only in presence<\/span><\/div>\n<\/div>\n<div class=\"views-row views-row-1 views-row-odd views-row-first\">\n<h2 class=\"views-field views-field-title\"><span class=\"field-content\">Values in Science<\/span><\/h2>\n<div class=\"views-field views-field-field-institution\"><strong class=\"views-label views-label-field-institution\">Institution: <\/strong> <span class=\"field-content\">Scuola IMT Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-location\"><strong class=\"views-label views-label-field-location\">Location: <\/strong> <span class=\"field-content\">Piazza San Francesco 19, 55100 Lucca<\/span><\/div>\n<div class=\"views-field views-field-field-level\"><strong class=\"views-label views-label-field-level\">Level: <\/strong> <span class=\"field-content\">Ph.D. level &#8211; already offered by other Ph.D. programs (&#8220;corso condiviso\/mutuato da altri dottorati&#8221;)<\/span><\/div>\n<div class=\"views-field views-field-field-type2\"><strong class=\"views-label views-label-field-type2\">Type: <\/strong> <span class=\"field-content\">Introductory course (no or few prerequisites)<\/span><\/div>\n<div class=\"views-field views-field-field-attendance-mode\"><strong class=\"views-label views-label-field-attendance-mode\">Attendance Mode: <\/strong> <span class=\"field-content\">Blended<\/span><\/div>\n<div class=\"views-field views-field-field-exam\"><strong class=\"views-label views-label-field-exam\">Exam: <\/strong> <span class=\"field-content\">No<\/span><\/div>\n<div class=\"views-field views-field-field-lecturers\"><strong class=\"views-label views-label-field-lecturers\">Lecturers: <\/strong> <span class=\"field-content\">Matteo De Benedetto<\/span><\/div>\n<div class=\"views-field views-field-field-email\"><strong class=\"views-label views-label-field-email\">Email: <\/strong> <span class=\"field-content\">matteo.debenedetto@imtlucca.it<\/span><\/div>\n<div class=\"views-field views-field-field-academic-year\"><strong class=\"views-label views-label-field-academic-year\">Academic Year: <\/strong> <span class=\"field-content\">2025\/2026<\/span><\/div>\n<div class=\"views-field views-field-field-semester2\"><strong class=\"views-label views-label-field-semester2\">Semester: <\/strong> <span class=\"field-content\">Second semester<\/span><\/div>\n<div class=\"views-field views-field-field-hours\"><strong class=\"views-label views-label-field-hours\">Hours: <\/strong> <span class=\"field-content\">10<\/span><\/div>\n<div class=\"views-field views-field-field-timetable\"><strong class=\"views-label views-label-field-timetable\">Timetable: <\/strong> <span class=\"field-content\">05\/05\/2026, 06\/05, 12\/05, 13\/05, 19\/05<\/span><\/div>\n<div class=\"views-field views-field-field-abstract\"><strong class=\"views-label views-label-field-abstract\">Abstract: <\/strong> <span class=\"field-content\">Science is often seen as the pure pursuit of truth \u2014 but can it ever really stand<br \/>\napart from human values? This course explores how moral beliefs, political priorities,<br \/>\nand social ideals influence the questions scientists ask, the theories they<br \/>\nchoose, the concepts they use, and the evidence they trust. From Darwin\u2019s theory<br \/>\nof evolution to Cold War biology, from climate modeling to evidence-based<br \/>\nmedicine, we\u2019ll investigate the intricate relationship between human values and<br \/>\nscience through the analysis of case studies from the history of science. Through<br \/>\nreadings, discussions, and analyses of primary sources, the course critically examines<br \/>\nthe ideal of a value-free science and explores frameworks for responsible,<br \/>\nsocially engaged scientific practice. The course invites critical reflection on science<br \/>\nnot as a detached process, but as a deeply human enterprise \u2014 one driven<br \/>\nby curiosity, guided by ideals, and entangled with our collective hopes and fears.<\/span><\/div>\n<div class=\"views-field views-field-field-syllabus\"><strong class=\"views-label views-label-field-syllabus\">Syllabus: <\/strong> <span class=\"field-content\">&#8211; Lecture 1 (5th of May 2026): Introduction \u2013 Science, Values, and Objectivity.<br \/>\n&#8211; Lecture 2 (06th of May): Epistemic Values in Scientific Reasoning.<br \/>\n&#8211; Lecture 3 (12th of May): Against the Value-free Ideal \u2013 Ethics, Politics, and Social Considerations in Scientific Reasoning.<br \/>\n&#8211; Lecture 4 (13th of May): Controversies and Value-Laden Judgments in Contemporary Science.<br \/>\n&#8211; Lecture 5 (19th of May): Integrating Values \u2013 Norms and Frameworks for a Responsible Science<\/span><\/div>\n<div class=\"views-field views-field-field-link\"><strong class=\"views-label views-label-field-link\">Link: <\/strong> <span class=\"field-content\">Visit the IMT website.<\/span><\/div>\n<div class=\"views-field views-field-field-circleu\"><strong class=\"views-label views-label-field-circleu\">Available for CircleU students: <\/strong> <span class=\"field-content\">Yes<\/span><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Ph.D. students have to attend at least 140 hours of courses overall in 3 years (the earlier the better). Each&hellip;<\/p>\n<p><a class=\"btn btn-dark btn-sm unipi-read-more-link\" href=\"https:\/\/phd-ai-society.di.unipi.it\/en\/training\/\">Read More&#8230;<\/a><\/p>\n","protected":false},"author":17,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/fullwidthpage.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-2498","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Study plan - National Ph.D. in Artificial Intelligence for Society<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/phd-ai-society.di.unipi.it\/en\/training\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Study plan - National Ph.D. in Artificial Intelligence for Society\" \/>\n<meta property=\"og:description\" content=\"Ph.D. students have to attend at least 140 hours of courses overall in 3 years (the earlier the better). 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