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.
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).
Mirco Musolesi is a Full Professor of Computer Science at both University College London (UCL) and the University of Bologna. He leads the Machine Intelligence Lab at UCL, part of the UCL Centre for Artificial Intelligence. His research focuses on Machine Learning, Generative AI, and computational models of human behavior, with applications in ubiquitous systems and societal impacts of AI. Education: PhD in Computer Science from UCL (2007) and Laurea in Electronic Engineering from the University of Bologna (2002). Previous roles include positions at the University of Birmingham, Dartmouth College, and the Alan Turing Institute. Research spans multi-agent systems, reinforcement learning, and AI ethics. Notable awards include ACM UbiComp 10-Year Impact Award (2020/2024) and the NetExplorateur/UNESCO Top 100 Innovations (2011). His work on EmotionSense and CenceMe applications has been recognized with Test-of-Time awards. Recent publications (2024-2025) address moral alignment in AI agents, multi-agent environmental policy simulations, and creativity in LLMs. His labs explore AI-driven solutions for urban systems and ethical decision-making frameworks.
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.
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.
Monica Pratesi is a Full Professor of Statistics at the Department of Economics and Management of the University of Pisa. She currently serves on leave as Director of the Department for Statistical Production at ISTAT, coordinating 937 researchers and managers. Her expertise spans small area estimation, poverty measurement, survey methodology, and official statistics. She leads the Tuscan Universities Research Centre “Camilo Dagum” and has held two Jean Monnet Chairs focusing on poverty and living conditions in the EU. She has coordinated major EU projects like INGRID-2 and MAKSWELL, advancing methodologies for inclusive growth and sustainable development. Her research integrates big data and citizen-generated data into statistical frameworks. Awards include presidencies of the Italian Statistical Society and the International Association of Survey Statisticians. Education & Roles: Full Professor of Statistics (SECS-S/01) at University of Pisa since 2012 Director, Department for Statistical Production at ISTAT (until 2024) President, Italian Statistical Society (2016-2020) President-elect, International Association of Survey Statisticians (2022-2023) Research Focus: Advanced statistical methods for poverty monitoring, small area estimation, survey design, and leveraging big data for policy impact. Key areas include multidimensional poverty, educational poverty, and sustainable development indicators. Her work emphasizes real-time data integration and policy relevance. Grants & Projects: Principal Investigator for INGRID-2 (EU H2020, 2017-2021) Principal Investigator for MAKSWELL (EU H2020, 2017-2020) Coordinator of SAMPLE (FP7) and INGRID (FP7) Labs & Teams: Active in the Societal Transitions group and contributes to the European Master in Official Statistics program. Her research center, REMARC, focuses on policy-driven statistical innovation.
Alberto Viglione is an Associate Professor at the Politecnico di Torino , Department of Environment, Land and Infrastructure Engineering (DIATI), and a member of the Interdepartmental Center SmartData@PoliTO. He has been a faculty member since 2019, following a decade as a Research Fellow at the Vienna University of Technology. University: Politecnico di Torino Department: DIATI – Department of Environment, Land and Infrastructure Engineering Rank: Associate Professor Email: alberto.viglione@polito.it His research focuses on flood hydrology, water resources, and hydro-meteorological extremes , integrating statistical analysis, climate change impacts, land use dynamics, and socio-hydrological modeling. He investigates the spatio-temporal dynamics of climatic, hydrological, and human processes in river basins and their implications for extreme event risks. His work emphasizes data integration, conceptual modeling, and risk assessment across scales. The recent publications highlight a strong trend in analyzing European flood dynamics , the impacts of climate change , and the development of socio-hydrological frameworks that incorporate human behavior and societal memory into flood risk modeling. His research spans from statistical hydrology in ungauged basins to large-scale assessments of climate-flood interactions. Scientific Awards and Honors: AMGA Award for best PhD thesis on water resources (2009) Editorial and Professional Service: Associate Editor, Water Resources Research (2014–present) Associate Editor, Hydrological Sciences Journal (2012–2018) Associate Editor, WIRES Water (2012–2020) Associate Editor, Journal of Hydrology and Hydromechanics (2019–present) Scientific Committee Member, European Geosciences Union (2019–2023) Secretary, International Commission on Water Resources Systems, IAHS (2015–present) Teaching and Advising: He teaches courses such as Bayesian Inference , Applied Hydrology , Fundamentals of Environmental Geosciences , and Hydro-meteorological Risk Assessment . He is a PhD supervisor and member of multiple PhD colleges in Civil and Environmental Engineering at Politecnico di Torino. He currently advises PhD students including Tsion Ayalew Kebede , Emanuele Mombrini , Luigi Cafiero , Luca Lombardo , and Matteo Pesce . His research is supported by grants from national (PRIN), EU, and commercial sources, including projects like Clim2FlEx , RETURN , and ATO4WATER . Research Labs and Teams: He is affiliated with the SmartData@PoliTO laboratory, focusing on big data and data science applications in hydrology and environmental systems.
Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.
Tommaso Feraco is an Assistant Professor at the University of Padova , focusing on the intersection of education , psychology , and behavioral science . His work examines how social, emotional, and behavioral skills (SEBS) influence academic performance, well-being, and developmental outcomes across diverse populations, including adolescents, adults, and individuals with specific learning disabilities. Research trends highlight his contributions to character strengths , self-regulated learning , and adaptability in educational contexts. He has also developed methodological tools to address clustering analysis limitations in data science. His recent publications investigate SEBS applications in career readiness , pro-environmental behavior , and mental health resilience .
Chiara Monfardini is a Full Professor of Econometrics at the Department of Economics of the University of Bologna. Her academic discipline is ECON-05/A Econometrics. She holds a Degree in Statistics and Economics from the University of Padova (1992) and a PhD in Economics from the European University Institute (1997). Her career includes positions as an Assistant Professor (2001), Associate Professor (2005), and Full Professor (2017) at the University of Bologna. She is affiliated with the Institute for the Study of Labor (IZA) and CHILD-Collegio Carlo Alberto. As Co-Editor of the Review of Economics of the Household , she contributes to academic publishing. Her research focuses on microeconometrics, simulation-based inference, and applied studies in labor, household, and health economics. Key themes include parental time investments, gender gaps in time use, and the intergenerational transmission of educational choices. She leads projects like MINUTO (MindUsTogether), funded by PRIN2017, exploring mindful parenting interventions and parental engagement via digital tools. Teaching responsibilities include courses such as Econometrics , Microeconometrics , and Advanced Microeconometrics . Her work integrates quantitative methods with policy analysis, addressing issues like early childhood education impacts and voting behavior. Office hours are held on Mondays at 5 PM by appointment.
Giuseppe Cavaliere is a Full Professor of Econometrics at the University of Bologna (since 2006) and a Distinguished Research Professor at Exeter Business School. He holds affiliations with the University of Copenhagen and Aarhus University. His research focuses on time series econometrics, financial econometrics, statistical inference, and empirical macroeconomics. He serves as co-editor of the Journal of Econometrics and associate editor of the Journal of Time Series Analysis. Key roles include being an Elected Fellow of the International Association for Applied Econometrics (IAAE), Fellow of the Journal of Econometrics, and Research Fellow of the Granger Centre for Time Series Econometrics. He previously served as President of the Italian Econometric Association (SIdE). His publications appear in top journals like Econometrica, Annals of Statistics, and Journal of Econometrics. Current research emphasizes bootstrap inference, cointegration, and volatility modeling in nonstationary environments. His work addresses challenges in econometric theory, financial data analysis, and macroeconomic policy evaluation. Awards and recognitions highlight his contributions to econometric methodology and its applications in finance and macroeconomics. His advisory and editorial roles reflect his influence in shaping the field's theoretical and practical advancements.
Federico Nutarelli is an Assistant Professor of Economics at the IMT School for Advanced Studies Lucca, Italy. His research focuses on the intersection of machine learning and economic analysis, particularly in international trade, health economics, and industrial organization. He holds a Ph.D. in Economics from IMT Lucca, and previously conducted postdoctoral research at Bocconi University. In 2024, he was a Visiting Scholar at MIT Sloan School of Management. Key research interests include causal machine learning methods to analyze heterogeneous firm responses to economic shocks, pharmaceutical market pricing strategies, and structural demand models. His work bridges methodological rigor with applied relevance, contributing to health economics, trade dynamics, and innovation policy. Recent publications (2020–2025) explore topics such as matrix completion for world trade analysis, machine learning applications in economic complexity, and modeling innovation ecosystems. His work often employs advanced statistical techniques like Shapley values and reinforced Bernoulli processes. No scientific awards were explicitly mentioned in the provided texts. Federico collaborates with institutions like Bocconi University and MIT Sloan, reflecting his interdisciplinary network in economics and data science.
Giuliano Bianchi is an Associate Professor of Economics at EHL Hospitality Business School, specializing in law and economics, corporate governance, and forecasting. He holds a PhD in Economics from the University of Bologna, a Master's in Economics from the University of Edinburgh, and a Master in Law from the University of Fribourg. His research focuses on hospitality economics, asset-light business models, and macroeconomic forecasting in the Swiss hotel sector. Education: PhD in Economics, University of Bologna (Italy) Master's Degree in Economics, University of Edinburgh (UK) Bachelor's in Economics, University of Lugano (Switzerland) Bachelor in Law (BLaw), UniDistance Master in Law (MLaw), University of Fribourg His research interests include empirical analysis of hotel industry dynamics, regulatory frameworks affecting hospitality operations, and corporate governance mechanisms. He pioneered the Swiss Hospitality Macroeconomics Forecasting Index (2018-2020), a project funded by HES-SO to develop demand forecasting tools for Swiss hotels. Recent work explores legal aspects of hotel rate parity, brand affiliation impacts on asset values, and the socio-economic implications of hospitality employment. Awards: Wertheim Fellowship, Harvard Law School Bologna University Economics Department Scholarship Marco Polo Fellowship Program Dr. Bianchi serves on EHL's academic board and teaches Macroeconomics and Microeconomics in the BSc International Hospitality Management program. His research has been published in Tourism Economics, Journal of Property Research, and Applied Economics.
Fabrizio Riguzzi is a Full Professor at the Department of Mathematics and Computer Science of the University of Ferrara, Italy. His academic career spans over two decades at the same institution, having served as Associate Professor (2014-2020) and Assistant Professor/Ricercatore (1999-2014). He is an active researcher in the fields of Logic Programming and Statistical Relational Artificial Intelligence with numerous publications and leadership roles in international conferences. His educational background includes: PhD in Electronic and Computer Engineering from the University of Bologna (1999) Laurea in Computer Engineering from the University of Bologna (1995) Riguzzi's research focuses on probabilistic approaches to artificial intelligence, particularly probabilistic logic programming and statistical relational AI. His work bridges symbolic reasoning with probabilistic methods, developing frameworks for uncertain knowledge representation and reasoning. He has made significant contributions to probabilistic answer set programming, neuro-symbolic integration, and applications in areas like network intrusion detection and knowledge graph completion. His research demonstrates how logical formalisms can be enhanced with probabilistic reasoning to tackle real-world problems with uncertainty. An analysis of his recent publications reveals a strong trend toward integrating neural and symbolic approaches in AI, with significant work on probabilistic answer set programming frameworks. His research spans theoretical foundations of probabilistic logic programming, practical implementations, and applications in cybersecurity, knowledge graphs, and decision-making under uncertainty. The interdisciplinary nature of his work connects computer science theory with practical AI applications. His notable awards include: Alain Colmerauer 10-Year Test-of-Time Award at ICLP 2021 Best Paper Award for "BUNDLE: A Reasoner for Probabilistic Ontologies" at RR-2013 Highly Commended Paper Award for "Probabilistic declarative process mining" at KSEM 2010 Riguzzi has supervised several PhD students to completion, including Elena Bellodi, Riccardo Zese, and Giuseppe Cota, who have gone on to win prestigious awards for their theses. He has served in numerous editorial roles, including Associate Editor of the Journal of Artificial Intelligence Research and Editor in Chief of Intelligenza Artificiale. His leadership extends to organizing major conferences like ILP 2018 and serving on program committees for top AI venues including IJCAI, AAAI, and ECAI. He is a member of the ML@unife research group and has developed several online systems including cplint, TRILL, and an Online AUC calculator. His work has fostered collaborations across the AI research community, particularly in the areas of probabilistic logic programming and neuro-symbolic AI.
