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Isadora ANTONIANO VILLALOBOSView profile

Associate Professor

• Statistics
• Probability Theory
• Bayesian Inference
• +5 more

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.

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).

• 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

• 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.

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.

• Multivariate Data Analysis
• Tensor Analysis
• Compositional Analysis
• +2 more

Michele Gallo is a Full Professor of Statistics at the Department of Human and Social Sciences , University of Naples L'Orientale, where he has taught statistical methods to master's students since 2020. His academic career includes roles as Associate Professor (2005-2011) and Researcher in Statistics. He currently serves on the Board of Directors at his university and the Steering Committee of the Italian Statistical Society. His research focuses on Multivariate Data Analysis , Tensor Analysis , Compositional Analysis , and Rasch Models , with applications in quality management and social sciences. Recent publications address robust multiway data analysis, sustainable sports metrics, and statistical software development in R. Key trends in his 15 most recent articles (2023-2025) include: Advancements in tensor decomposition methods for multidimensional data Applications of compositional data analysis in sustainability research Development of open-source statistical software packages Integration of machine learning with classical statistical techniques Evaluation frameworks for service quality and innovation

Giovanni TrappoliniView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +9 more

Giovanni Trappolini is an Assistant Professor in the Department of Computer, Control, and Management Engineering at Sapienza University of Rome, where he conducts research in the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He earned his PhD in Machine Learning from Sapienza in 2022, focusing on Geometric Deep Learning under Prof. Emanuele Rodolà. Trappolini has collaborated with leading institutions such as Stanford, Technion, Meta, Amazon, TII, and the University of Pisa, contributing to cutting-edge research in AI and information retrieval. His research interests include machine learning, deep learning, geometric deep learning, multimodal AI, information retrieval, and large language models. He has pioneered work on Retrieval-Augmented Generation (RAG), Neural Databases, and the development of Fauno, the leading Italian LLM. His teaching experience includes courses on Python for Data Science, Advanced Data Mining, and Statistical Learning at both Sapienza and Luiss Guido Carli. Trappolini's recent publications span top-tier venues like SIGIR, NeurIPS, ECCV, and IEEE Transactions, with a focus on graph neural networks, federated learning, anomaly detection in 5G networks, and multimodal systems. His work shows a strong trend toward robust, privacy-preserving, and multimodal AI systems with real-world applications in language, vision, and network security. Sapienza honor graduate IELTS 8.0 He has advised several teaching roles and delivered courses on Python, data science, and statistical learning. His research is supported through collaborations with industry leaders like Meta and Amazon, and he leads innovative projects such as Fauno and Neural Databases. Trappolini is actively involved in the AI research community and continues to push the boundaries of language and retrieval systems. He is a core member of the RSTLess Lab and has been instrumental in advancing geometric and multimodal deep learning at Sapienza University. His work bridges theoretical innovation with practical implementation, particularly in the domains of Italian language modeling and secure, efficient information retrieval systems.

• Statistics
• Econometrics
• Biostatistics
• +7 more

Professor Alessio Farcomeni is a distinguished statistician at the University of Rome Tor Vergata, affiliated with the Department of Economics and Finance. His work spans statistical methodology, biostatistics, and applied econometrics. He teaches advanced courses such as Statistical Learning and Big Data Analysis for Economics and Finance , reflecting his expertise in modern data science. His research focuses on latent Markov models, quantile regression, and population size estimation, with applications in healthcare, epidemiology, and policy analysis. Farcomeni has contributed significantly to statistical methodology through publications in high-impact journals. Notable projects include modeling the Italian response to the Covid-19 pandemic, analyzing atrial fibrillation risk prediction, and developing Bayesian approaches for complex datasets. His work often bridges theoretical statistics with real-world challenges, such as optimizing treatment protocols for cancer patients and evaluating macroprudential policies. He maintains active collaborations with institutions like the Italian National Health Service and has advised on international studies, including the CALIPSO transplant infection project. His research has addressed critical issues like material deprivation measurement in Europe and the prognosis of metastatic prostate cancer. Farcomeni is recognized for pioneering methods in latent variable modeling and robust statistical inference. His teaching and mentorship are complemented by software development contributions, such as the Qtools package for quantile regression analysis. Despite prolific output, he emphasizes interdisciplinary collaboration, evidenced by his work in cardiology, oncology, and public health.

Floriana EspositoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Programming Languages
• +3 more

Floriana Esposito is a Full Professor and current Director of the Department of Computer Science at the University of Bari, Italy. With a career spanning over 50 years since her 1974 appointment, she has held pivotal leadership roles including Dean of the Faculty of Computer Science (1997-2002) and Director of the Interdepartmental Center for Logic and Applications (1997-2001). She teaches core courses including Algorithms and Data Structures, Knowledge Engineering, and Machine Learning. Education: Degree in Electronic Physics from University of Bari (1970) First Academic Appointment at University of Bari (1974) Research Focus: Her work centers on Artificial Intelligence and Machine Learning with emphasis on logical/algebraic foundations. Initial research in statistical pattern recognition evolved into Intelligent Tutoring Systems and Machine Learning, now focusing on similarity-based learning, multistrategy approaches, and integration of symbolic/numeric methods. Major applications target Document Analysis and Understanding for paper-based systems, with significant contributions to symbolic data analysis and knowledge discovery. Publication Trends: Recent work shows strong continuity in multistrategy and incremental learning techniques applied to document processing and symbolic data analysis. Her research consistently bridges theoretical foundations (logical theories, concept drift handling) with practical implementations in spatial data mining, e-learning systems, and urban planning, demonstrating a 30-year trajectory from knowledge acquisition fundamentals to real-world AI deployment. Scientific Awards: No specific awards were mentioned in the source text. Advising & Grants: As founder of LACAM Laboratory (1986), she has mentored numerous PhD students and researchers. Her group secured extensive EU funding through ESPRIT, IST, and Framework programs including MLNet, KDNET, and DELOS Networks of Excellence, focusing on machine learning applications in document analysis, digital libraries, and spatial data mining across 15+ European collaborative projects. Labs & Teams: She chairs LACAM Laboratory (2 full professors, 3 associate professors, 5 assistant professors, multiple PhD students), which serves as a European hub for AI research. The lab has been integral to ESPRIT Intrepid (document processing), CONCERTO (digital document retrieval), COLLATE (historical archive systems), and SODAS (symbolic data analysis), maintaining continuous involvement in EU Networks of Excellence since the 1990s.