جست‌وجوی استاد و پژوهشگر

Camilla Mastromarcoمشاهده پروفایل

استاد

• Applied Econometrics
• Nonparametric Econometrics
• Bayesian Econometrics
• +۵ مورد دیگر

Camilla Mastromarco is a Full Professor of Econometrics at the University of Calabria's Department of Economics, Statistics and Finance 'Giovanni Anania' - DESF. She holds a Ph.D. from the University of Glasgow and has held visiting positions at institutions including Université Catholique de Louvain and Toulouse University. Education: Laurea in Economia e Commercio, Master Coripe (1994), Master's in Economics (University of Essex, 1995), MSc in Economics (Scottish Doctoral Programme, 1997), Dottorato di Ricerca (University of Bari, 1999), Ph.D. (University of Glasgow, 2005) Academic Career: National Scientific Qualification as Full Professor (2017), Associate Editor for Empirical Economics (since 2018), President of Italian Econometric Association (2024) Her research spans econometric methodologies and applications, focusing on: Applied parametric and nonparametric econometrics Bayesian econometric modeling Efficiency and productivity analysis via frontier methodologies Business cycle dynamics and economic growth Sports economics and environmental regulation impacts Recent publications highlight conditional frontier analysis applications in corporate efficiency, healthcare, and regional productivity networks. She has contributed to journals like the Journal of Applied Econometrics and Econometric Journal. Scientific accolades include: Marie Curie Fellowship (Ludwig Maximilian University of Munich, 2003–2004) Italian Econometric Association (SIdE) Presidency (2024) She serves as a reviewer for top journals and has taught econometrics, panel data analysis, and frontier modeling at the University of Calabria.

Botond Szaboمشاهده پروفایل

دانشیار

• Bayesian Statistics
• High-dimensional Statistics
• Nonparametric Methods
• +۳ مورد دیگر

Botond Szabo is an Associate Professor in the Department of Decision Sciences at Bocconi University. He serves as a Fellow of the Bocconi Institute for Data Science and Analytics (BIDSA) and is faculty at the Bayesian Learning Laboratory (BayesLab). Additionally, he leads the ERC Starting Grant project "BigBayesUQ" (2022-2027). His research primarily focuses on the theoretical understanding of Bayesian high-dimensional and nonparametric methods, along with scalable machine learning techniques. His work bridges theoretical statistics with practical machine learning applications, emphasizing mathematical foundations and computational efficiency. As an academic leader, Szabo serves as Associate Editor for Annals of Statistics (since 2022) and Bayesian Analysis (since 2016), contributing significantly to the statistical research community. Fellow of Bocconi Institute for Data Science and Analytics (BIDSA) Project leader of ERC Starting Grant "BigBayesUQ" (2022-2027) Szabo maintains active editorial roles in leading statistical journals and conducts research at the intersection of theoretical statistics and machine learning. His office is located at 3-e2-12, via Roentgen 1, 20136, Milano, Italy.

• Robust Statistics
• Data Analysis
• Regression Analysis
• +۳ مورد دیگر

Prof. Aldo Corbellini is an Associate Professor at the Department of Economic and Business Sciences , University of Parma, Italy. He specializes in robust statistical methods, data analysis, and their applications in economics, business, and health research. Teaching: Courses in Statistics and Statistical Methods for Decision Making (2014–2025) for Economics and Management programs, and Food System Management. Research: Focus on robust regression, outlier detection, data transformations, and ESG modeling for SMEs. Collaborations: Long-term partnership with researchers like Marco Riani and Anthony C. Atkinson. Key Contributions: Development of tools for robust regression monitoring, the fsdaSAS and FSDA4R libraries, and applications in credit risk, fraud detection, and labor market analysis. Publications: Over 15 recent articles spanning robust regression, correspondence analysis, data science, and methodological extensions of the Box-Cox transformation. Historical Work: Contributed to studies on elderly frailty and health status prediction (SMILES Study, 2000–2002).

Michele Bernasconiمشاهده پروفایل

استاد

• Public Finance
• Tax Evasion
• Income Inequality
• +۲ مورد دیگر

Michele Bernasconi is a Full Professor at Ca' Foscari University of Venice's Department of Economics. He serves on the Management Committee of the University Scientific Instrumentation Service Center. His research focuses on public finance, tax evasion, income inequality, and behavioral economics. Bernasconi's publications demonstrate expertise in experimental economics methods applied to tax compliance and distributive justice. Recent work analyzes psychological aspects of inequality perception, welfare system design, and behavioral responses to tax policies. His research frequently incorporates microsimulation modeling and cross-disciplinary approaches combining economics with psychology and political science. His body of work shows consistent focus on Italian fiscal policy challenges, with methodological contributions to experimental design and nonparametric estimation techniques in behavioral research.

• Algebraic statistics
• Experimental design
• Industrial statistics
• +۳ مورد دیگر

Roberto Fontana is a Full Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin. He is a member of the Interdepartmental Centre R3C (Responsible Risk Resilience Centre) and actively involved in the College of Management and Production Engineering and the Planning and Design Board. His research focuses on algebraic statistics, experimental design, industrial statistics, and multivariate statistics, with an emphasis on statistical analysis of physical and computer experiments. Fontana holds leadership roles in academic societies, including being an Effective Member of ERCIM (European Research Consortium for Informatics and Mathematics), ENBIS (European Network for Business and Industrial Statistics), and SIS (Italian Statistical Society). He serves as an Associate Editor for the Journal of Applied Statistics since 2017. His teaching spans multiple levels, including PhD courses on statistical modeling and machine learning, as well as undergraduate and graduate courses in statistics across disciplines like Management Engineering and Territorial Planning. He leads the ELBA project (2019–2022), establishing training centers for Intelligent Big Data analysis. Fontana’s recent publications emphasize advancements in contingency tables, Bernoulli distributions, and the integration of design of experiments with machine learning. He currently supervises PhD student Davide Ronco in the Mathematical Sciences program. His work bridges theoretical statistics with industrial applications, emphasizing robust experimental design and computational methods. Fontana actively collaborates on interdisciplinary projects, including environmental and material science applications.

Marcello Restelliمشاهده پروفایل

استاد

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Marcello Restelli is a Professor at the Department of Electronics, Information, and Bioengineering (DEIB) of Politecnico di Milano, affiliated with the School of Industrial and Information Engineering. His research focuses on Reinforcement Learning (RL), Machine Learning, Robotics, and Control Systems. He explores challenges in real-world RL applications, multi-agent systems, and optimization under uncertainty. Key contributions include developing scalable algorithms for sparse-reward environments, POMDPs, and safe robotics. He leads projects like the EU-funded I3LUNG initiative, integrating AI for personalized lung cancer care. His work bridges theory and practice, addressing topics such as exploration strategies, regret minimization, and causal feature selection. He has contributed to frameworks like Gym4ReaL for benchmarking real-world RL systems and Storehouse for warehouse optimization. Recent efforts emphasize energy systems, financial decision-making, and explainability in extreme event prediction. His publications highlight advancements in inverse RL, online learning, and transfer learning. Collaborations span academia and industry, reflecting his impact on both theoretical and applied AI domains.

• Statistical Modeling
• Biostatistics
• Environmental Data Analysis
• +۴ مورد دیگر

Prof. Vito Michele Rosario Muggeo is a Full Professor in the Department of Economics, Business and Statistics at the University of Palermo. His research focuses on statistical modeling, biostatistics, environmental data analysis, and regression techniques. He teaches courses such as Statistical Inference/Statistics 2 and Statistical Methods. Active in academic evaluations, his work spans over two decades with publications in statistical methodologies, public health, and environmental science. Notable contributions include advancements in segmented regression, quantile regression, and applications in healthcare and ecology. His research often involves collaboration with international journals and conferences, addressing topics like cardiovascular health in cancer survivors and pandemic modeling. He is also involved in software development for statistical analysis, contributing R packages like 'segmented' and 'quantregGrowth'. Education & Professional Experience: - Full Professor at University of Palermo (STAT-01/A) - Active in teaching and research since at least 2000, evidenced by publications from 2000s onward. Research Interests: - Development of statistical methodologies for complex data structures. - Applications in public health, oncology, and environmental studies. - Quantile regression, penalized regression, and segmented modeling techniques. Recent Article Trends: - Focus on pandemic analysis (Covid-19 monitoring), cardiovascular outcomes in leukemia survivors, and innovative statistical tools for predictive modeling. Grants & Advising: - No explicit grants listed, but extensive collaboration in research projects across disciplines. - Advises on statistical methodologies via software development (R packages).

• Microeconometrics
• Spatial and Network Econometrics
• Health Economics
• +۴ مورد دیگر

Federico Belotti is an Associate Professor in the Department of Economics and Finance at the University of Rome Tor Vergata. His research focuses on microeconometrics, spatial and network econometrics, and health economics, with a particular emphasis on stochastic frontier models, healthcare productivity, and spatial econometric methods. Institution: University of Rome Tor Vergata Department: Economics and Finance Research Areas: Microeconometrics, Health Economics, Spatial Econometrics His recent publications highlight interdisciplinary work bridging econometric methodology and health policy analysis. He has developed specialized Stata software packages for frontier estimation, spatial analysis, and statistical diagnostics, contributing significantly to applied econometric research. Scientific awards include the prestigious Stata Journal Editors’ Prize (2018). His empirical studies span topics such as poverty measurement, obesity-related healthcare costs, pandemic impacts on drug prescriptions, and technology adoption in public services. Scientific Awards: Stata Journal Editors’ Prize (2018)

Roberto Di Mariمشاهده پروفایل

استادیار

• Model-Based Clustering
• Finite Mixture Models
• Latent Variable Models
• +۴ مورد دیگر

Roberto Di Mari is an Assistant Professor at the Department of Economics and Business , University of Catania, Italy. His research focuses on advanced statistical methodologies, particularly model-based clustering, finite mixture models, latent variable models, and latent Markov models. He holds a Ph.D. in Economics and Finance from the University of Rome Tor Vergata (2017) and has conducted research at Tilburg University (Netherlands) as a visiting scholar. Education: Ph.D. in Economics and Finance (2017), University of Rome Tor Vergata M.Sc. in Economics (2013), University of Rome Tor Vergata Bachelor's Degree in Economics (2011), University of Catania Erasmus Exchange, University of Lille 1 (France), 2009–2010 Research Interests: Di Mari specializes in developing and applying statistical models to analyze complex data structures. His work bridges methodological advancements in latent variable modeling (e.g., mixture models, latent Markov approaches) with applications in economics, business, and social sciences. Key themes include robustness in modeling outlier risk, multilevel latent class analysis, and measurement invariance across longitudinal studies. Publications: His research emphasizes methodological contributions to latent variable modeling, including advancements in bias correction, R package development (e.g., multilevLCA ), and applications to high-frequency financial data and SME risk assessment. His work is published in journals like Journal of Econometrics , Structural Equation Modeling , and International Journal of Approximate Reasoning . Grants & Collaborations: Di Mari’s academic trajectory includes collaborations with Tilburg University and the Einaudi Institute for Economics and Finance. His research often integrates computational tools (e.g., LASSO penalization, dynamic mixtures) to address real-world challenges in data analysis.

• Econometrics
• Finance
• Asset Pricing
• +۲ مورد دیگر

Roberto Renò is a Professor at the Information Systems, Data Analytics and Operations (IDO) Department at ESSEC Business School. He has held visiting roles at institutions including Johns Hopkins University, Collegio Carlo Alberto, and the European University Institute. Previously, he served as a Full Professor at the University of Verona and held roles at the University of Siena and LUISS. He earned a PhD in Financial Mathematics from Scuola Normale Superiore and a Master in Physics from the University of Pisa. His research focuses on econometrics and finance, with expertise in asset pricing, high-frequency financial econometrics, and nonparametric statistics. Recent work includes presentations on 'Local Edgeworth expansions', 'Pure Momentum', and '0DTE Option Pricing' at global conferences like IAAE, TSE, and SoFiE. He co-founded the CAPIRE data repository to support research in financial econometrics. Renò has delivered over 20 invited talks across Europe, Asia, and the Americas since 2023, addressing topics such as volatility forecasting, market microstructure, and trend detection in high-frequency data. His research bridges theoretical advancements with practical applications in financial markets. Education: PhD in Financial Mathematics, Scuola Normale Superiore (Pisa) Master in Physics, University of Pisa Awards: None explicitly stated Grants and Advising: Details not provided in text Labs/Teams: Co-developed CAPIRE data repository

Daniele Duranteمشاهده پروفایل

دانشیار

• Network Science
• Bayesian Methods and Computation
• Categorical Data
• +۵ مورد دیگر

Roles and Affiliations: Associate Professor of Statistics at Bocconi University's Department of Decision Sciences. Research Affiliate at Bocconi Institute for Data Science and Analytics (BIDSA), DONDENA Centre, and Laboratory for Coronavirus Crisis Research. Former Post-Doctoral Fellow at University of Padova and Visiting Scholar at Duke University. Education: Ph.D. (2016) and M.Sc. (2012) in Statistics from University of Padova. Research Interests: Bayesian methodology, network science, categorical data, latent variable models, criminology, and demography. Focus on statistical models for complex data, including criminal networks and cause-of-death dependencies. Grants and Awards: ERC Starting Grant (NEMESIS, 2024–2029), PRIN-MUR Grant (CARONTE), COPSS Emerging Leader Award, Leonardo da Vinci Medal, Mitchell Prize (twice), Laplace Prize. Editor for Biometrika , Journal of Computational and Graphical Statistics , and Journal of Multivariate Analysis . Teaching and Service: Courses on Machine Learning, Data Science, and Statistical Methods. Organized Stats under the Stars hackathons, Bocconi Data Science Challenge platform. Served on committees for ISBA, ASA, and others. Labs/Teams: Leads NEMESIS and CARONTE projects. Collaborates with interdisciplinary teams in quantitative criminology and demography.

• Multivariate Time Series Analysis
• Non-Parametric Statistics
• Financial Networks
• +۵ مورد دیگر

Lorenzo Frattarolo is a Tenure-track Assistant Professor at the Department of Economics of the University of Verona since 2023. His academic journey includes positions as a researcher at the Joint Research Center of The European Commission in the Economy and Finance unit, and as a postdoctoral researcher at Ca' Foscari University of Venice and Goethe University of Frankfurt. Dr. Frattarolo holds a joint PhD in Applied Mathematics/Economics from Paris 1 and Ca' Foscari, and a MSc in Theoretical Physics from the University of Rome La Sapienza. His academic credentials reflect a strong quantitative background that informs his research approach. His research focuses on advanced statistical and econometric methodologies with applications in finance and economics. Key areas include Multivariate Time Series Analysis, Non-Parametric Statistics, Financial Networks, and Forecasting. His work bridges theoretical statistical development with practical financial and economic applications, particularly in risk modeling and economic forecasting. Frattarolo's publications span top journals in econometrics and statistics, demonstrating expertise in copula methods, financial network analysis, and economic forecasting techniques. His recent work has addressed timely issues including pandemic-related economic analysis, climate risk, and European financial markets. Econometrics Statistical Methods Financial Networks Time Series Analysis Forecasting Within the Department of Economics at the University of Verona, Frattarolo contributes to various academic committees including the Economics Department Council and teaches courses across multiple degree programs, including Econometrics, Statistical and Econometric Analysis, and Machine Learning for Economics.

• Machine Learning
• Nonparametric Methods
• Applied Statistics
• +۵ مورد دیگر

Simone Borra is an Associate Professor in the Department of Statistics at the University of Rome Tor Vergata, affiliated with the Faculty of Economics and Law. His research focuses on Machine Learning, Nonparametric Methods, Applied Statistics, Big Data Analytics, and Management Consultancy Analysis. He teaches courses such as Business Analytics (Master’s), Big Data Analytics (Bachelor), and Statistical Methods for Management (Master’s). His work bridges statistical methodologies with practical applications in environmental science, public administration, and business strategy. Research interests include developing advanced classification techniques, remote sensing applications using satellite data (e.g., Sentinel), and analyzing socio-economic phenomena through statistical modeling. Recent studies explore solar power plant identification via CNNs and vegetation monitoring using spectral bands. He also investigates industry trends in management consulting and public procurement practices in Italy. His publications span over two decades, with notable contributions to survey methodology, neural network performance evaluation, and multilevel modeling. While no scientific awards are explicitly mentioned, his extensive academic output reflects sustained contributions to statistical science and its interdisciplinary applications. In advising and grants, Borra has not listed formal advisees but contributes to educational activities through course leadership. His work on public administration contracting and consultancy sector reports highlights engagement with policy-relevant research.

Eugenio Melilliمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Confidence Distributions
• Population Forecasting
• +۴ مورد دیگر

Eugenio Melilli is an Associate Professor of Statistics in the Department of Decision Sciences at Università Commerciale Luigi Bocconi in Milan. He holds a degree in Mathematics (cum laude) from Università degli Studi di Milano and previously served as Assistant Professor (1996-2005) before his current appointment. His international teaching includes positions at Lobachevsky State University (Russia) and PhD instruction at Bocconi. Research specializes in Bayesian nonparametrics, confidence distributions, and stochastic population forecasting. He develops theoretical frameworks for exchangeability, Dirichlet processes, and expert-based demographic projections, with applications to psychometrics and social statistics. Key Contributions: Developed methods for confidence distributions in Rasch models, advanced Bayesian approaches to population forecasting, and extended de Finetti's theorem for partially exchangeable sequences.

Isadora ANTONIANO VILLALOBOSمشاهده پروفایل

دانشیار

• Statistics
• Probability Theory
• Bayesian Inference
• +۵ مورد دیگر

Isadora Antoniano Villalobos is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. Her research focuses on statistical models and methods for complex data structures, particularly Bayesian inference for intractable models, extreme value theory, and risk analysis. She leads projects such as RISE (Rethinking and Innovating Statistics for Extremes) and SMARTMUS-E, addressing environmental and energy challenges. Her teaching spans courses like Applied Probability and Statistics for Empirical Research in Management across various degree programs. She collaborates internationally, speaks English, Spanish, and Italian fluently, and contributes to editorial boards in statistical journals. Research Interests : Bayesian methods, extreme value analysis, statistical modeling of spatio-temporal data, risk assessment, and applications in environmental science, hydrology, and social sciences. Her work emphasizes flexible models for complex datasets and interdisciplinary collaborations. Recent Projects : RISE: PRIN-funded project on extreme value analysis (2023-2026) SMARTMUS-E: EU Horizon Europe project on museum energy management (2023-2025) Bayesian Nonparametric Mixture Models for the Dependence of Extremes (BayMEx, 2019-2021) Teaching : Courses include Applied Probability for Computer Science , Statistics for Language Sciences , and Statistics for Empirical Research in Management . She has taught at undergraduate and graduate levels since 2019.