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Moreno BevilacquaView profile

Associate Professor

• Spatial Statistics
• Covariance Modeling
• Geostatistics
• +3 more

Moreno Bevilacqua is an Associate Professor in the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. His research focuses on spatial statistics, covariance modeling, and geostatistical methods, with applications in environmental and multivariate analysis. He has contributed to advancing covariance functions, composite likelihood approaches, and spatial data modeling techniques. Recent publications emphasize statistical methodologies for spatial and spatiotemporal data, including extensions of the Matérn model, composite likelihood for massive datasets, and copula-based approaches for bounded support data. His work bridges theoretical advancements and practical applications in environmental science and statistical computing. No scientific awards or grants are explicitly mentioned in the provided texts. Teaching activities include courses in statistics for undergraduate programs in Business Economics, covering foundational and advanced statistical methods. Current office location: Assegnisti A1 office (Zeta A building, 1st floor), Campus via Torino.

Fabio PresaghiView profile

Associate Professor

• Developmental Psychology
• Clinical Psychology
• Social Psychology
• +9 more

Prof. Fabio Presaghi is an Associate Professor of Psychology at Sapienza University of Rome, affiliated with the Department of Psychology of Developmental and Socialization Processes. He holds roles such as Editor-in-Chief of Psychology Hub , Vice-President of the Master's Program in Psychology of Communication and Marketing, and Academic Director of International Mobility for the same program. His research focuses on advanced statistical methods (e.g., SEM, multilevel modeling, meta-analysis) applied to developmental, clinical, and social psychology. Key interests include self-injury measurement, alexithymia in youth, and the psychosocial impact of digital technology addiction. Presaghi has led projects funded by grants such as the 2008 initiative for adapting the Children's Somatization Inventory. His work spans studies on emotion regulation, moral development, and the effects of mindfulness interventions on adolescents. Current projects include developing non-clinical and clinical self-injury assessment tools for publication in Psychiatry Research and European Psychiatry . He teaches courses like Statistical Research Methods for medical students and Multivariate Analysis Techniques for psychology students, using online platforms like Moodle and Google Classroom. His institutional roles include serving on the Ethical Committee for Transdisciplinary Research and the Quality Board of the Psychology of Communication and Marketing program.

Marco CorneliView profile

Professor

• Static and dynamic network analysis
• Spectral clustering
• Stochastic block models
• +16 more

Marco Corneli is a Professor at the University of Côte d'Azur , where he focuses on developing AI models for archaeology and history. He collaborates with the CEPAM laboratory , is a member of the INRIA research team MAASAI , and part of the LJAD laboratory. His research spans network analysis, optimal transport, and 3D shape analysis. Research Interests : Static/Dynamic Network Analysis (Spectral Clustering, SBMs, HRGs) Optimal Transport for Machine Learning Image and 3D Shape Analysis Probabilistic and Deep Learning Methods Clustering in High-dimensional and Sparse Data Advising : Davide Adamo (2023 - ongoing, co-advised with E. Vila and M. Vuillien) Seydina Ousmane Niang (2023 - ongoing, co-advised with C. Bouveyron and P. Latouche) Giulia Marchello (2020-2023, co-advised with C. Bouveyron) Cédric Vincent-Cuaz (2020-2023, co-advised with R. Flamary) Dingge Liang (2019-2022, co-advised with C. Bouveyron and P. Latouche) Software Development : Co-developed CpDyna (R/C++ package for change point detection in dynamic graphs) Contributed to ordinalLBM (R package for co-clustering ordinal data) Teaching : Delivered courses in statistical learning, graph theory, and data science at University of Côte d'Azur (M2 MA IM), Université Paris 1 (Licence MIASHS), and University of Côte d'Azur (MSc Data Science).

• Robust Statistics
• Data Analysis
• Regression Analysis
• +3 more

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).

• Statistics
• Environmental Statistics
• Time Series Analysis
• +3 more

Vincenzo Gioia is a Research Fellow at the University of Trieste's Department of Economics, Business, Mathematics and Statistics (DEAMS). She specializes in statistical methodologies for environmental and multivariate data analysis, with a focus on extreme value theory and Markov-switching models. Her work addresses climate change impacts (heat wave detection), energy demand modeling, and urban pollution analysis. Current Projects: Joint DEAMS research project analyzing public/telecom data for environmental applications Third-party transport data analytics funded by Trieste Trasporti (€33k) Research Interests: Bayesian statistics, time series modeling, spatial-temporal analysis, and algorithmic approaches to high-dimensional data. Active in environmental and energy sector applications.

• Statistics
• Econometrics
• Robust Estimation
• +3 more

Anna Clara Monti is a Full Professor in the Department of Law, Economics, Management and Quantitative Methods (DEMM) at the University of Sannio. Her research focuses on statistical inference, asymptotic theory, robust estimation, and time series analysis. Key research areas: Statistics, Econometrics, Robust Estimation, Asymptotic Theory, Time Series Analysis Notable contributions: Methodological advancements in portmanteau tests, skew normal distribution estimation, M-estimator confidence limits, and concentration ratio bias correction Contact: acmonti@unisannio.it

Brunero LiseoView profile

Professor

• Bayesian Statistics
• Copulas
• Stochastic Processes
• +10 more

Brunero Liseo is a Full Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance (MEMOTEF), Sapienza University of Rome. He teaches Basic Statistics and Advanced Statistical Methods in Economics and Finance programs. His research focuses on: Bayesian Statistics (objective priors, hierarchical models) Copula Theory (conditional copulas, vine copulas) Stochastic Processes (skew-Brownian motion, long-memory processes) Statistical Modeling (Gaussian extensions, data integration) Recent work includes applications in financial risk analysis , population estimation , and data linkage . He is involved in the research project New Statistical Methods for the Analysis of Human Migration .

• Mathematical Finance
• Probability Theory
• Statistics
• +7 more

Patrizia Semeraro is a Full Professor at the Department of Mathematical Sciences "GL Lagrange" (DISMA), Polytechnic University of Turin. She is a member of the Interdepartmental Center R3C - Responsible Risk Resilience Center and the Master's and Continuing Education School. Her research and teaching are centered in mathematical finance, probability, and statistics, with applications in financial modeling and risk analysis. Polytechnic University of Turin, Department of Mathematical Sciences (Current) Her primary research interests include mathematical finance, stochastic processes, multivariate modeling, and statistical methods in economics and finance. She focuses on advanced topics such as multivariate Lévy models, tempered stable processes, subordination, and high-dimensional Bernoulli distributions. Her work bridges theoretical probability with practical applications in credit risk, derivative pricing, and financial engineering. Recent publications demonstrate a strong trend in developing and calibrating multivariate financial models, analyzing dependence structures, and applying probabilistic methods to real-world economic problems. Her work spans journals in operations research, statistics, probability, and financial economics, reflecting interdisciplinary expertise. Scientific recognitions include: Outstanding paper awarded by Emerald Literati Network, Italy (2016) Fellow - INDAM, Italy (2019–present) Fellow - AMASES, Italy (2017–present) She supervises PhD students including Chen Zhang, Alessandro Mutti, and Giovanni Amici. She has been involved in nationally funded research projects such as PRIN and SUP-CHAIN-DIS (2023–2025), focusing on supply chain disruptions and financial losses. She teaches in the Financial Engineering program and has previously taught courses such as Complements of Mathematics and Fundamentals of Physics and Mathematical Principles. She is a member of the Probability and Applications research group and contributes to the Mathematical Models in Finance research area at DISMA.

Enzo D'InnocenzoView profile

Assistant Professor

• Econometrics
• Statistics
• Data Science
• +6 more

Enzo D'Innocenzo is a Junior Assistant Professor in Econometrics, Statistics, and Data Science at the Department of Economics, University of Bologna. His primary research focuses on advanced econometric methodologies, including time series analysis, statistical inference, asymptotic theory, score-driven models, and financial econometrics with applications to option pricing. Previously, he served as an Associate Researcher at the School of Business and Economics, Vrije Universiteit Amsterdam. Research Interests: Time Series Econometrics Volatility Modeling (GARCH, Score-Driven) Financial Markets Analysis Option Pricing Theories Extreme Value Theory Dynamic Pricing Strategies Notable Publications: His recent work includes advancements in score-driven models for spatio-temporal data (JASA 2023), dynamic partial correlation networks in financial markets (Journal of Econometrics 2024), and analysis of last-minute hotel pricing strategies (2024). His research bridges theoretical econometric innovation with practical applications in finance and hospitality sectors. Professional Activities: Presenter at NESG Conference 2022 (University of Groningen) Active contributor to econometric journals and conferences Maintains personal academic webpage with research links

Paola FerrettiView profile

Associate Professor

• Decision Theory
• Mathematical Methods in Economics
• Actuarial Sciences
• +6 more

Paola Ferretti is an Associate Professor at the Department of Economics, Ca' Foscari University of Venice. Her research focuses on Mathematical Methods in Economics, Actuarial Sciences, and Finance , with specific expertise in Decision Theory, Stochastic Orderings, and Insurance modeling. She serves as the Department Delegate for Teacher Training and contributes to editorial boards like Mathematical Methods in Economics and Finance . Teaching: Courses in Mathematics for Economics, Optimization, Risk Theory, and Decision Theory at undergraduate, graduate, and PhD levels Research Grants: Funded by MIUR (PRIN 2007) and Dipartimento di Management (2011) Collaborations: Works with Andrea Ellero, Giovanni Fasano, Daniela Favaretto, and Raluca Elena Buia Her publications analyze sustainability dimensions, Bayesian confirmation measures, and reinsurance contracts using mathematical frameworks. She applies Data Mining techniques to tourist demand forecasting and studies risk attitudes in multi-period contexts. Key conferences organized include MAF 2008/2012/2016 (Mathematical Finance), RUD 2001 (Risk Uncertainty Decision), and EURO Working Group 1997 (Financial Modeling).

Alexander Simon MayerView profile

Assistant Professor

• Econometrics
• Time Series Analysis
• Panel Data
• +3 more

Alexander Simon Mayer serves as a Researcher (RTDa) in the Department of Economics at Ca' Foscari University of Venice. He holds a PhD in Econometrics from WHU--Otto Besheim School of Management (2020), with prior graduate studies at University of Bonn and undergraduate education at University of Cologne. His academic appointment is based at the San Giobbe campus in Venice. His educational background includes: BSc in Economics (2012) - University of Cologne MSc in Economics (2014) - University of Bonn PhD in Econometrics (2020) - WHU--Otto Besheim School of Management Postdoctoral Research (2020-2022) - Institute of Econometrics and Statistics, University of Cologne Mayer's research focuses on advanced econometric methodologies with particular expertise in time series analysis, panel data structures, and causal inference frameworks. His work bridges theoretical econometrics with practical applications in financial and economic modeling, frequently addressing challenges related to nonstationarity, structural breaks, and nonlinear dependencies in complex datasets. His publications demonstrate consistent contributions to methodological advancements in econometric theory. Analysis of his recent publications reveals strong thematic continuity in developing robust econometric techniques for handling complex data structures. His work spans nonstationary panel models, quantile-based causality testing, endogeneity corrections, structural break detection, and copula-based dependence modeling - all addressing fundamental challenges in modern econometric analysis. Mayer teaches graduate-level econometrics courses across multiple programs including Economics, Finance and Sustainability; Engineering Physics; and doctoral programs. His teaching spans from 2022 through the upcoming 2025/2026 academic year, consistently delivering 6-7 credit Econometrics courses at the master's level.

• Machine Learning
• Computer Vision
• Data Visualization
• +8 more

Antonio Costantino Marceddu is a Research Fellow in Computer Engineering at the Polytechnic University of Turin's Department of Control and Computer Science (DAUIN). His research focuses on machine learning, computer vision, data fusion, and advanced data visualization techniques with applications in aerospace and environmental monitoring. Research Fellow at DAUIN PhD in Computer Engineering Focus on sensor data visualization and analysis Research interests span multiple domains: Augmented reality applications Fiber Bragg Grating sensor systems Quantum computing implementations Computer vision and image analysis Structural health monitoring His recent publications (2021-2025) demonstrate expertise in: Developing visualization systems for aerospace sensor data Innovative approaches to mask detection challenges Quantum adaptations for gaming applications Thermal property monitoring using optical sensors Soil moisture prediction with neural networks Marceddu has collaborated extensively with Professor Bartolomeo Montrucchio and colleagues across multiple research lines, contributing to advancements in real-time data monitoring systems and multivariate analysis applications.

• Medical Statistics
• Epidemiology
• Bayesian Networks
• +7 more

Mariaclelia Serio Stefania is a Full Professor at the Vita-Salute San Raffaele University , affiliated with the Faculty of Psychology and Faculty of Medicine and Surgery . She serves as Director of the University Center for Statistics in the Biomedical Sciences (CUSSB) and holds key roles in academic governance. Her international appointments include Professor of Statistics at the University of Italian Switzerland, Lugano, since 2007.

Paolo Carmelo COZZUCOLIView profile

Associate Professor

• Statistics
• Statistical Quality Control
• Multivariate Statistical Processes
• +3 more

Paolo Carmelo COZZUCOLI is an Associate Professor of Statistics (scientific-disciplinary sector SECS-S/01) at the Department of Economics, Statistics and Finance "Giovanni Anania" at the University of Calabria, where he has been in service since December 2006. Previously, he worked as a Statistics Researcher at the Faculty of Economics and Political Sciences of the University of Calabria from June 1992 to December 2006. Professor Cozzucoli's research focuses on statistical methodologies with particular emphasis on multivariate statistical processes, Bayesian statistics, and applications in quality control. His work spans various domains including social media analysis, environmental sustainability, financial forecasting, and chemical engineering. He has developed innovative approaches for hate speech detection, service quality assessment, and statistical process monitoring. His recent publications demonstrate a consistent focus on multivariate analysis techniques applied across diverse fields. The research shows a progression from fundamental statistical methodology development toward increasingly applied interdisciplinary work, with strong emphasis on practical implementation of statistical models in real-world settings. His work frequently employs Bayesian approaches, permutation tests, and multivariate process control methods. Professor Cozzucoli teaches courses including "Statistics for decisions," "Statistica ed elementi di inferenza," and "Deep learning Techniques and Neural Networks" for programs in Public Administration Sciences, Economics, and Data Science for Business Analytics. He is an active member of the "Statistica & Demografia" research group, which focuses on developing new statistical and demographic methodologies for data collection, analysis, description, and interpretation. Within this group, his specific interests include: multivariate statistical process control, Bayesian networks, sampling theory, and applications in quality management.

• Statistics
• Randomized Response Techniques
• Survey Sampling
• +5 more

Pier Francesco Perri is a Full Professor of Statistics (SSD STAT-01/A) at the Department of Economics, Statistics, and Finance 'Giovanni Anania' (DESF) of the University of Calabria (Unical). He has held academic positions since 2002 and coordinates institutional activities such as the National Plan for Scientific Degrees (L-41, Statistics) and editorial roles in journals like Metron and Model Assisted Statistics and Applications . His research focuses on survey methodology, sensitive data estimation, and statistical modeling. Education: Ph.D. in Methodological Statistics (University of Trento, 2002), Honors Degree in Statistical and Actuarial Sciences (University of Calabria, 1997) Perri’s research spans randomized response techniques for sensitive populations, copula modeling for financial data, and inequality index linearization. He explores indirect questioning methods to detect truthfulness and improve survey accuracy, particularly for stigmatized behaviors like abortion, migration, and non-heterosexual identities. His work integrates auxiliary information and complex sampling designs to enhance estimation efficiency. Recent publications emphasize innovations in sample survey traditions, including the 2025 editorial on methodological advancements and 2024 studies on adjusting self-protective behaviors in prevalence estimation. His 2023–2021 research includes logistic regression extensions for randomized response models, prevalence estimates for health behaviors, and crossed model applications for sensitive topics. Earlier work (2020–2000) covers item sum techniques, financial copula modeling, and environmental sensitivity analysis. He serves on editorial boards and institutional committees, including the Italian Statistical Society’s Council and the Ph.D. Teaching Board at Unical. The Laboratorio Statistico Informatico supports his research in statistical computing and survey design.