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Omiros PapaspiliopoulosView profile

Professor

• Computational Statistics
• Machine Learning
• Probability
• +5 more

Omiros Papaspiliopoulos is a Full Professor at Bocconi University’s Department of Decision Sciences. He joined Bocconi in 2021, previously serving as an ICREA Research Professor at Universitat Pompeu Fabra in Barcelona. His academic career includes roles at Warwick, Oxford, Lancaster, Berlin, Osaka, Paris, Madrid, and Lima. He has been awarded the Royal Statistical Society’s Guy Medal (2010) and the DeGroot Prize for his influential work with Nicolas Chopin. His research focuses on computational statistics, spanning Bayesian inference, machine learning, probability, and applied mathematics. Notably, he founded Europe’s first Master in Data Science at the Barcelona Graduate School of Economics (2013) and directed the Data Science Center until 2021. Since 2022, he has been Director of Bocconi’s Bachelor of Science in Economics, Management, and Computer Science (BEMACS). Papaspiliopoulos is co-Editor of Biometrika (top-4 in Statistics) and Associate Editor of the Journal of Uncertainty Quantification . He has delivered keynote talks at institutions like Chicago Booth, Duke, and LSE, emphasizing the societal impact of data science. His teaching spans courses on data science, statistics, machine learning, and quantitative methods in social sciences. Education & Academic Leadership PhD in Statistics (Implied by career trajectory) Founded Bocconi’s BEMACS program (2022) Directed the Master in Data Science (2013–2021) Executive course design for SDA Bocconi and Barcelona School of Economics Research Interests Computational Statistics & Bayesian Methods Machine Learning & Applied Mathematics Data Science in Social Sciences Hidden Markov Models & State-Space Systems High-Dimensional Inference & Scalable Algorithms Recent Work Trends His publications emphasize scalable Bayesian computation, treatment effect inference, and applications in social sciences. Recent topics include confounder importance learning, particle filtering, and computational efficiency in hierarchical models. Collaborations bridge theory and practice, addressing challenges in policy, finance, and public administration. Awards & Recognition 2010: Royal Statistical Society’s Guy Medal DeGroot Prize for Nonparametric Hidden Markov Models Onassis Foundation Scholar Grants & Outreach Directed the Barcelona Data Science Center (2016–2021) Outreach activities: Talks for high-school students, policymakers, and the European Commission Editorial roles in top journals: Biometrika , Journal of Uncertainty Quantification Labs & Teams Current affiliation with Bocconi’s Department of Decision Sciences and involvement in interdisciplinary data science initiatives.

Rosario BaroneView profile

Assistant Professor

• Statistics
• Economics
• Finance
• +2 more

Rosario Barone is an Assistant Professor of Statistics at the University of Rome Tor Vergata. His research focuses on methodological development of statistical tools with applications in economics, finance, social sciences, and medicine. He specializes in Bayesian inference, Markov processes, and copula models, with recent work addressing complex dependencies and stochastic systems. His research interests include Bayesian nonparametrics, time-interaction processes, and semi-Markov models. Applications span healthcare (e.g., preterm birth analysis) and econometrics. Barone's publications highlight advancements in MCMC methods for continuous-time models and high-dimensional data analysis. He has contributed to interdisciplinary fields, merging statistical theory with practical applications in diverse domains.

Alessio FarcomeniView profile

Professor

• Statistics
• Statistical Methodology
• Data Science
• +5 more

Alessio Farcomeni is a Professor of Statistics at the University of Rome Tor Vergata, specializing in statistical methodology development. His work bridges academia and applied research across disciplines including economics, medicine, ecology, and engineering. He has authored/co-authored over 250 peer-reviewed papers and two books, with notable contributions in hidden Markov models, Bayesian analysis, and quantitative social science measurement. His research emphasizes interdisciplinary collaboration, reflected in studies on population health, economic policy evaluation, and surgical outcome optimization. Farcomeni’s research interests span statistical theory, with a focus on methodological innovations for complex data structures (e.g., longitudinal, spatial, and high-dimensional datasets). He has pioneered approaches in quantile regression, semi-Markov processes, and latent variable modeling. His applied work addresses real-world challenges such as estimating material deprivation scales, modeling cardiovascular disease risk factors, and analyzing pandemic dynamics (e.g., COVID-19 forecasting in Italy). His recent articles highlight advancements in statistical techniques for healthcare (e.g., AI-driven dermatology diagnostics, atrial fibrillation prediction models) and socio-economic analysis (e.g., macroprudential policy impacts, cross-country material deprivation comparisons). He is recognized for developing open-source statistical software packages, contributing to reproducible research practices. Farcomeni holds the distinction of being ranked among Italy’s top scientists by VIA-Academy. His work frequently integrates Bayesian methods, machine learning, and big data analytics to address pressing questions in public health, environmental science, and economic policy.

• Statistics
• Bayesian Inference
• Nonparametric Inference
• +3 more

Catia Scricciolo is an Associate Professor in the Department of Economics at Università di Verona since 2015. Previously, she served as Assistant Professor at Università Bocconi di Milano. She earned a degree in Statistics from Università La Sapienza di Roma and a Ph.D. in Statistics from Università di Padova. Her research focuses on Bayesian Nonparametric Inference Nonparametric curve estimation Mixture models Empirical Bayes procedures Bayesian inverse problems with theoretical work on asymptotic properties and likelihood-based procedures. She is actively involved in academic governance as a member of the Faculty Board of Ph.D. in Economics and Finance Collegio Didattico di Economia Aziendale e Management Commissione Paritetica Docenti-Studenti Research Advisory Board (RAB) and contributes to the Verona Experimental Lab in Economics (VELE).

Matteo IacopiniView profile

Research Fellow

• Bayesian Statistics
• Tensor Calculus
• Network Analysis
• +1 more

Matteo Iacopini is a Research Fellow in Economics at Ca' Foscari University of Venice, specializing in Bayesian statistics with applications to complex data structures. His methodological research develops tensor-based approaches for multidimensional data analysis, dynamic network modeling, and functional data interpretation. Current projects focus on hierarchical models, Bayesian nonparametrics, and constrained functional time series. He has created computational tools for distribution visualization (Half-Disk Density Strips) and tensor regression (ZIL-T-MS), published in applied statistics and machine learning venues.

• Statistics
• Environmental Science
• Climate Change
• +4 more

Isabella Morlini is an Associate Professor at the Department of Communication and Economics, University of Modena and Reggio Emilia. Her academic roles include teaching courses such as Multidimensional Data Analysis, Statistical Inference, and Statistics for Law students. She specializes in statistical methodologies applied to environmental science, education, and public health. Research Interests: Climate modeling, pandemic impact analysis, educational data challenges, dyslexia measurement, and fuzzy composite indicators. Teaching: Focuses on data analysis techniques for business, statistical inference, and environmental data interpretation. Her research highlights include analyzing urban climate changes in Modena, assessing risk factors for COVID-19 outcomes, and developing online health promotion programs. Collaborations span environmental scientists, public health experts, and educational technologists. Key methodologies involve nonlinear regression, cluster analysis, and fuzzy set theory. Publications emphasize statistical applications in diverse fields, with recent work addressing pandemic impacts and climate change mitigation strategies.

Sandra FortiniView profile

Associate Professor

• Bayesian Statistics
• Machine Learning
• Predictive Inference
• +2 more

Dr. Sandra Fortini is Associate Professor of Statistics in the Department of Decision Sciences at Bocconi University. With research spanning Bayesian statistics, machine learning, and nonparametric models, she investigates asymptotic properties of statistical procedures and predictive inference frameworks. Her methodological work has applications in biostatistics, neural network theory, and clinical trial design. Core research domains include: Asymptotic behavior of Bayesian and machine learning algorithms Deep neural network theory and infinite-width limits Exchangeability principles in predictive modeling Non-asymptotic approximations in statistical learning Bayesian nonparametric methods Recent publications demonstrate strong focus on neural network theory (60% of 2023-2024 works) and Bayesian clinical trial methodologies (40%). Her 2024 Biostatistics article introduces innovative uncertainty-directed factorial designs, while 2023-2024 neural network publications establish fundamental limit theorems for deep learning architectures. Significant recognitions include fellowship status in four prestigious societies: Institute of Mathematical Statistics, International Society for Bayesian Analysis, Italian Statistical Society, and Italian Mathematical Society. Teaching responsibilities encompass probability theory and stochastic processes for undergraduate and graduate programs at Bocconi.

Rebecca GrazianiView profile

Researcher

• Bayesian Statistics
• Computational Methods
• Social Sciences Applications
• +2 more

Rebecca Graziani is a Researcher with tenure at Bocconi University's Department of Decision Sciences. She earned her PhD in Methodological Statistics from Università di Trento and conducted postdoctoral research at Bocconi University. Graziani specializes in Bayesian methods and computational statistics with applications in social sciences, particularly demographic forecasting and healthcare analytics. Her research focuses on developing Bayesian approaches for complex statistical problems including stochastic population projections, network meta-analysis of clinical outcomes, and nonparametric biomarker modeling. She has collaborated with institutions like ISTAT to develop Italy's first probabilistic population forecasts. Graziani teaches courses in Quantitative Methods, Statistics, and Computational Statistics using SPSS, STATA, R, and Python at both undergraduate and graduate levels across Bocconi's social science programs.

Igor PruensterView profile

Professor

• Bayesian Nonparametrics
• Probabilistic Machine Learning
• Bayesian Asymptotics
• +7 more

Igor Pruenster is a Professor at Bocconi University and Director of the Ph.D. in Statistics and Computer Science. He is a Fellow of the Bocconi Institute of Data Science & Analytics (BIDSA) and holds editorial positions as Editor-in-Chief of Bayesian Analysis and Associate Editor of Annals of Statistics . His research spans Bayesian statistics , machine learning , and stochastic processes , with specialization in nonparametric methods, asymptotic theory, and probabilistic modeling. His work has influenced computational statistics and data science methodologies. Notable recognitions include fellowships from three premier statistical societies. He actively organizes international conferences on Bayesian nonparametrics and mentors award-winning doctoral students.

• Econometrics
• Computational Statistics
• Financial Risk Management
• +5 more

Marco Bee is a Full Professor at the Department of Economics and Management, University of Trento. His expertise spans applied econometrics, computational statistics, finance, and risk modeling. He focuses on methodologies for handling heavy-tailed distributions, extreme value theory, and machine learning applications in financial risk assessment. Education details are available in his CV (CVeng.pdf). His research interests include developing statistical models for operational risk, volatility forecasting, and credit scoring, often employing mixture models, copula-based approaches, and indirect inference techniques. He has contributed significantly to the analysis of spatial econometrics and the application of extreme value theory to financial crises and insurance analytics. His recent work emphasizes tail risk estimation, with over 150 publications since 2006. Notable contributions include methodologies for Value-at-Risk (VaR) forecasting, distribution fitting for skewed data, and the use of machine learning to predict defaults in small businesses. His research bridges theoretical statistics and practical financial applications, with a focus on high-frequency data and scenario-based risk analysis. Awards and grants are not explicitly listed in the provided data, but his extensive publication record reflects recognition in quantitative finance and econometrics. He advises students on topics related to computational econometrics and risk modeling, though specific advisee names are not documented here.

• Bayesian Statistics
• Computational Statistics
• Statistical Inference
• +3 more

Sara Kathryn Wade is a Teaching Professor at the University of Trento, holding roles across multiple departments: the Department of Sociology and Social Research, Department of Cellular, Computational and Integrative Biology (CIBIO), Department of Mathematics, and Department of Information Engineering and Computer Science. Her primary teaching responsibilities include Bayesian statistics courses, focusing on both parametric and nonparametric frameworks. She designs curricula emphasizing foundational principles of Bayesian inference, computational methods, and practical applications using tools like R. Her research interests revolve around advanced statistical methodologies, including Dirichlet and Pitman-Yor processes in nonparametric mixture models, hierarchical modeling, and simulation techniques. She emphasizes interdisciplinary approaches, bridging theoretical statistics with real-world data analysis challenges. Students under her guidance learn to apply Bayesian frameworks to diverse fields such as computational biology, informatics, and social sciences. No scientific awards or grants are explicitly mentioned in the provided materials. Her academic contributions are centered on pedagogy and statistical education, with a focus on equipping students with computational skills and critical reasoning in probabilistic modeling.

Luca RossiniView profile

Associate Professor

• Econometrics
• Statistics
• Bayesian Methods
• +6 more

Luca Rossini is an Associate Professor in Statistics at the Department of Economics, Management and Quantitative Methods, University of Milan (Statale), and a Senior Researcher at Fondazione Eni Enrico Mattei (FEEM). His research focuses on Bayesian econometrics, time series analysis, and network theory applications in economics. He leads major grants such as 'Modelling Non-standard data and Extremes in Multivariate Environmental Time series (MNEMET)' and 'Mapping and Pricing of Methane Emissions from the European Electricity Sector (MAP-of-MeLEES)'. Rossini is also involved in organizing the Milan Time Series Seminars across multiple institutions. Grants: Co-PI for EUR 285k MAP-of-MeLEES (2023-2025) and EUR 250k MNEMET (2023-2025) Editorial Role: Associate Editor for Annals of Applied Statistics since 2024 His work bridges methodological advancements in statistics with practical applications in energy economics, financial systems, and environmental data analysis.

Lea PetrellaView profile

Professor

• Quantile regression
• Graphical models
• Random Forest
• +3 more

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

• Statistics
• Kernel Density Estimation
• Multimedia Quality Assessment
• +3 more

Sergio Pezzulli serves as a Researcher in the Department of Methods and Models for Economy, Territory, and Finance at Sapienza University of Rome, where his work bridges statistical theory with practical applications across multiple disciplines. His research focuses on advanced statistical methodologies including kernel density estimation for economic modeling, longitudinal data analysis for medical sciences, and innovative quality assessment frameworks for multimedia systems. He develops parametric and nonparametric techniques that address complex real-world data challenges in finance, healthcare, and telecommunications. Recent publications demonstrate consistent contributions to statistical theory with practical implementations, particularly in high-order kernel methods and multimedia quality scoring beyond traditional MOS approaches. His work shows strong interdisciplinary connections between econometrics, biostatistics, and multimedia engineering. Scientific Awards: No awards or fellowships mentioned in available sources. Advising and Grants: No information available regarding graduate students, research grants, or funded projects. Labs and Teams: No specific laboratory affiliations or research team memberships documented in current institutional records.

Roberto CASARINView profile

Professor

• Econometrics
• Computational models
• Macroeconomics
• +6 more

Roberto CASARIN is a Full Professor at the Department of Economics, Ca' Foscari University of Venice. His roles include Member of the Board of the Department of Economics, Erasmus Delegate, and Deputy Director of the International Master in Economics, Finance and Data Science. He is affiliated with the European Center for Living Technology and the Research Institute for Complexity. His research focuses on computational econometrics, dynamic models for economic analysis, stochastic volatility, and Bayesian methods applied to finance and energy markets. Key projects include Discrete Random Structures for Bayesian Learning (MUR Grant 2023-2025), EeDaPP Energy Efficiency Data Portal (EU H2020 2018-2020), and GRINS Sustainable Finance (MIUR 2022-2024). His work spans over 150 publications, with recent contributions on tensor regression, Markov switching models, and antithetic sampling techniques. He serves as Associate Editor for Bayesian Analysis , Econometrics , and Econometrics and Statistics . Awards and recognitions are not explicitly listed in the text, but his extensive editorial roles and funding indicate significant academic impact.