Barbara Caputo is a Full Professor at Politecnico di Torino, leading the VANDAL Laboratory and directing the AI@PoliTo Interdepartmental Lab. She holds a double affiliation with the Italian Institute of Technology (IIT) and has held roles at Idiap-EPFL and Sapienza University. Her research focuses on AI, computer vision, domain adaptation, and federated learning. She contributes to national AI policy, including the Italian Strategy on AI and the National PhD on AI for Industry 4.0. She is an ERC Laureate and ELLIS Fellow, co-founding ELLIS society. Her work spans visual place recognition, action recognition, and cross-domain learning. Education: PhD in Computer Science from KTH Royal Institute of Technology (2005). Major roles include Rector’s Advisor on AI at PoliTo, Board Member of ELLIS, and coordinator of the AI & Industry 4.0 vertical in the National PhD program. Awards include ERC Laureate (2017), ELLIS Fellow (2019), and Inspiring Fifty Italy (2018). Her research emphasizes federated learning, domain adaptation, and AI ethics. Recent articles explore domain generalization, resource-efficient federated models, and AI-environment interactions. She collaborates with institutions like MUR, CNR, and the European Commission on AI policy and tech initiatives.
Luca De Benedictis is a Professor of International Economics and Network Analysis at the University of Macerata's Department of Economics and Law. His research focuses on international trade empirics, including trade specialization measurement, network analysis, and causal models. He has authored numerous articles on topics like gravity models, migration impacts, and historical trade networks. His work spans journals such as the Journal of the Royal Statistical Society and Network Science . He teaches courses in International Economics and Network Analysis. His research interests include economic geography, policy evaluation, and applied econometrics. Notable projects include analyzing the Erasmus Program's inclusivity, Roman road networks' legacy, and immigration's effect on trade. De Benedictis has secured funding from EU initiatives like COSTNET and GeComplexity, focusing on network data science and economic systems. He serves on editorial boards of journals like Italian Economic Journal and Journal of Historical Network Research . His work bridges theoretical models with empirical applications in trade, migration, and policy.
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
Valter Moretti is a Full Professor in the Department of Mathematics at the University of Trento. His academic career spans roles from Research Fellow to Full Professor, focusing on Mathematical Physics and Quantum Field Theory (QFT) in curved spacetime. He earned an MSc in Physics from Genova University and a PhD in Theoretical Physics from Trento University. Research Interests : Algebraic QFT, General Relativity, Quantum Mechanics, Operator Algebras, and Spectral Theory. His work bridges mathematical rigor with physical applications, particularly in quantum localization, entanglement, and curved spacetime phenomena. Publications : Authored 15+ recent papers on topics like quantum particle localization, entanglement certification, and QFT on curved backgrounds. Collaborated on a 2022 patent for generating entangled photon states. Awards : Holds a patent for a quantum-certified random number generator (2022). Supervision : Advised 8 PhD students, including N. Pinamonti, L. Franceschini, and C. van de Ven. Coordinated national and international research projects (e.g., H2020-MSCA-COFUND-2015). Labs & Collaborations : Affiliated with INFN, TIFPA-INFN, and Q@TN (Quantum@Trento). Organized conferences like Quantum Physics and Geometry (2014) and Quantum Machine Learning (2023). Teaching : Lectures on Analytical Mechanics, Quantum Relativistic Theories, and Special Relativity. Authored textbooks on Spectral Theory and Quantum Mechanics.
Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).
Luca Iocchi is a Full Professor at Sapienza University of Rome, where he teaches in the Master in Artificial Intelligence and Robotics program. He is affiliated with the Department of Computer, Control, and Management Engineering and the Faculty of Engineering of Information, Computer Science and Statistics. Iocchi serves as an Associate Editor for Artificial Intelligence Journal and has been the scientific coordinator of Spoke of PNRR project FAIR (Future AI Research). His educational background includes: Master in Engineering in Computer Science (Laurea in Ingegneria Informatica) cum Laude, Sapienza Università di Roma, 1995 PhD in Engineering in Computer Science (Dottorato in Ingegneria Informatica), Sapienza Università di Roma, 1999 Professor Iocchi's research focuses on cognitive robotics, task planning, multi-robot coordination, robot perception, robot learning, human-robot interaction, and social robotics. His work has significant applications in security, surveillance, and environmental monitoring. He has published over 200 referred papers with an h-index of 46 (Google Scholar). His research bridges theoretical AI with practical robotic systems operating in real-world environments, with a particular emphasis on developing intelligent systems that can interact effectively with humans. His recent publications show a strong trend toward multi-agent reinforcement learning, trust modeling in human-AI teams, UAV coordination, and planning systems. There's a clear focus on making robotic systems more reliable, efficient, and capable of operating in complex real-world scenarios like healthcare facilities and smart cities. His work increasingly integrates formal planning approaches with machine learning techniques. Professor Iocchi has received numerous scientific awards: 1999 Top Paper Award WebNet'99 2006 Best Paper Award RoboCup 2006 2008 Best Robotics Demo Award AAMAS 2008 2014 Best Paper Award For Engineering Contribution RoboCup 2014 2017 RoboCup@Home SSPL 2017 - 3rd place 2018 Canada-Italy Innovation Award 2019 Best Paper Award For Engineering Contribution RoboCup 2019 As an academic advisor, Iocchi has directed the PhD Program in Engineering in Computer Science from 2020 to 2023. He has been Principal Investigator for numerous research projects including SciRoc (European Robotics League), AI4EU (European AI project), BUBBLES, AIPlan4EU, ROSITA, Trust Your Agents, and FAIR. His research has been supported by EU H2020 programs, national grants, and industry collaborations, demonstrating strong connections between academia and practical applications. Professor Iocchi is actively involved with the Cognitive Cooperating Robots Lab (LabRoCoCo) and is a key member of the RoboCup Federation, having served as Vice-President from 2019 to 2024. He has played a significant role in benchmarking domestic service robots through RoboCup@Home and the European Robotics League Service Robots (ERL-SR), which he helped establish. His leadership in organizing international scientific robot competitions has been instrumental in advancing the field of service robotics.
Armando Rungi is a Professor of Economics at IMT School for Advanced Studies in Lucca, Italy. He teaches econometrics, international economics, and macroeconomics to PhD students. In addition to his academic role, he serves as a research fellow at the Observatory on Foreign Firms in Italy and has consulted for the European Commission, OECD, and UNCTAD on international trade and investment issues. His research focuses on international economics, industrial organization, applied econometrics, and statistical learning. Recent work emphasizes the organization of multinational enterprises, global value chains, labor markets, cyber-resilience of supply chains, and the integration of econometric and machine learning tools for policy evaluation and predictive analysis. His recent publications explore topics such as the impact of trade agreements, multinational enterprises' strategies, and the application of machine learning in predicting firm behaviors and evaluating economic policies. A common theme is the analysis of supply chain resilience, corporate ownership structures, and the effects of globalization on firms' competitiveness and productivity. No scientific awards are mentioned in the provided information. No advisees or grant details are listed in the text. His professional activities include consulting roles and research collaborations. He is affiliated with the Observatory on Foreign Firms in Italy, which evaluates the impact of multinational companies and strategies to attract foreign investment in Italy.
Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.
Stefano Grivet Talocia is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin. He serves as Director of the Doctoral School, is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and holds positions on the University Committee for Research and the Commission for the Promotion of Library, Archive and Museum Heritage. He is also President of the Doctoral School Council. His educational background includes a Laurea degree (summa cum laude) in Electronic Engineering (1994) and a Ph.D. in Electronic and Communication Engineering (1998), both from Polytechnic University of Torino. From 1994 to 1996, he worked at NASA/Goddard Space Flight Center in Greenbelt, MD, USA. Professor Grivet Talocia's research focuses on passive macro-modeling of concentrated and distributed interconnect structures for Signal/Power Integrity, order reduction techniques, and modeling and simulation of fields, circuits, and their interactions. His work spans several key areas including fast simulation of transmission lines (TOPLine technique), macromodeling and model order reduction, simulation methods for fields and circuits, passivity enforcement of lumped macromodels, waveform relaxation techniques, and wavelet applications. His research has significant applications in electromagnetic compatibility and signal integrity verification of complex electronic systems. His recent publications demonstrate strong trends in model order reduction techniques applied to power integrity verification, advanced macromodeling for electromagnetic compatibility, nonlinear circuit analysis, uncertainty quantification in PCB design, and power electronics modeling. These works consistently address practical engineering challenges in high-speed electronic design with emphasis on computational efficiency and accuracy. URSI Young Scientist Award (1999) Best symposium paper (2006) Three IBM Shared University Research Awards (2007-2009) IEEE Transactions on Advanced Packaging Best Paper Award (2007) Best EPEP conference paper awards (2007, 2008) Best Associate Editor Award - IEEE Transactions (2020) Best Conference Paper Award (2020) Three Intel SRS Grants (2022-2024) IEEE Fellow (2018) Professor Grivet Talocia actively supervises PhD students working on cutting-edge topics including machine learning applications in signal integrity, model reduction techniques, and electromagnetic compatibility. He has secured significant research funding through competitive grants including PRIN projects and multiple industry-sponsored research contracts with major technology companies such as IBM, Intel, Nokia, Hitachi, and Infineon. His technology transfer activities include co-founding the spin-off IdemWorks (acquired by CST in 2016) and maintaining active collaborations with industry partners. He leads the EMC Group (Electromagnetic Compatibility) within the Department of Electronics and Telecommunications and has developed the autoCircuits web service for automated generation of circuit theory problems. His research has been recognized by inclusion in the top 2% worldwide researcher catalog (Stanford) since 2019.
Sandra Paterlini is a Full Professor in the Department of Economics and Management at the University of Trento, Italy. She holds academic roles including Co-Chair of the ERCIM Working Group on Optimization Heuristics and Vice-Chair of the IEEE Task Force on Portfolio Optimization. Her career includes visiting positions at institutions such as the University of Minnesota and Ludwig-Maximilians-Universität München. She earned a PhD in Computational Methods for Financial and Economic Decisions from the University of Bergamo, an MSc in Financial Mathematics from the University of Warwick, and a Laurea in Economics from the University of Modena and Reggio E. Her research focuses on quantitative finance, risk management, portfolio optimization, and network analysis, with applications to ESG, systemic risk, and financial stability. Key research contributions include methodologies for sparse graphical modeling, systemic risk analysis, and ESG scoring frameworks. She has received multiple awards for research excellence and serves on editorial boards of journals like Computational Statistics & Data Analysis and Frontiers in Applied Mathematics and Statistics . Her work bridges academia and policy, with contributions to the European Central Bank’s Financial Stability Directorate and involvement in global conferences on computational finance and econometrics.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
Francesco Caravenna is a Full Professor at the Department of Mathematics and Applications of the University of Milan-Bicocca. His research focuses on Probability Theory and Mathematical Statistics, particularly in stochastic processes, disordered systems, and scaling limits. Editorial Roles: Associate Editor for The Annals of Applied Probability (2019) and Annales de l'Institut Henri Poincaré-Probabilités et Statistiques (2018). His recent work includes studies on the critical 2D stochastic heat flow, directed polymers, and the interplay between disorder and criticality. Publications emphasize rigorous mathematical analysis of stochastic partial differential equations, Gaussian multiplicative chaos, and universal scaling properties. He has received grants from MIUR and the Italian-French University (UniTO) for projects like Random Walks and Polymers (2019) and Large Scale Random Structures (2016). Scientific Awards: Fubini Award (2011), Mario Boella High School, with the Polymath Project and Subalpine Mathesis Association. His contributions span stochastic analysis, disordered systems, and interdisciplinary applications in statistical mechanics and financial modeling. Key themes include pathwise analysis, multiscale behavior, and critical phenomena in random systems.
Carlo Baldassi is an Associate Professor at Bocconi University, where he has served as Director of the BSc in Mathematical and Computing Sciences for Artificial Intelligence (BAI) since 2023/24. He holds a background in Theoretical Physics from the University of Trieste and a PhD in Computational Neuroscience from the University of Turin. His research focuses on applying Statistical Mechanics to Machine Learning and Neural Networks, particularly studying loss landscapes, optimization problems, and the role of quantum annealing in nonconvex learning. He teaches courses in Machine Learning, Artificial Intelligence, and Computer Science, emphasizing Python and Julia programming. His work bridges theoretical physics and AI, exploring topics like synaptic stochasticity in low-precision neural networks and the efficiency of quantum vs. classical annealing. He has published extensively in top journals such as Physical Review Letters and Proceedings of the National Academy of Sciences , contributing to foundational understanding of neural network dynamics and optimization techniques. Teaching includes Machine Learning and Artificial Intelligence Computer Science I Machine Learning II Machine Learning and Artificial Intelligence Lab Research emphasizes large-scale inference problems, with a focus on the interplay between statistical mechanics and modern AI architectures.
Matteo Penegini serves as an Associate Professor in the Department of Mathematics at the University of Genoa, where he holds a seat on the Department Board. His teaching portfolio spans multiple degree programs including Economic and Financial Sciences, Biomedical Engineering, and Mathematical Statistics, with courses such as General Mathematics, Geometry, and Linear Algebra and Analytic Geometry. His research centers on advanced Algebraic Geometry, specializing in the classification and structural analysis of algebraic surfaces and threefolds. Key investigations include triple covers of K3 surfaces, surfaces with pg=q=2 invariants, and projective varieties of general type. His work integrates cohomological methods, birational transformations, and moduli space theory to explore geometric genus constraints and Albanese map properties. Recent publications reveal a consistent focus on geometric invariants and covering spaces, with collaborative studies examining K3 surface covers (2022), surface families with specific Chern numbers (2021), threefold classification (2021), cohomology of irregular surfaces (2020), and Zariski multiplets from isogenous surfaces (2020). This trajectory demonstrates deepening engagement with Hodge theory and moduli problems in complex algebraic geometry.
Orazio Giancola is Associate Professor at the Department of Social and Economic Sciences, Sapienza University of Rome. His research focuses on social research methodologies, educational inequality analysis, and policy evaluation in Italian and comparative contexts. He teaches courses such as Applied Social Research Methodology , Research Methods for Social Policies , and data analysis techniques. Research Interests : Sociology of education, social stratification, big data analytics, educational equity, tertiary education access, and policy evaluation. Key Projects : School experiences and educational choices in the post-pandemic landscape (Rome high school survey). His recent publications examine educational poverty , digital technology disparities , and policy impacts in Italy and Europe. Articles employ multivariate analysis and logistic modeling to explore structural inequalities. No scientific awards are currently documented.
