Ilaria Lucrezia Amerise is an Associate Professor in the Department of Economics, Statistics and Finance 'Giovanni Anania' (DESF) at the University of Calabria (UNICAL). Her research focuses on multivariate analysis, time series, nonparametric statistics, and statistical methods for complex/high-dimensional data including functional and spatial data. Editor-in-Chief of JP Journal of Biostatistics (ANVUR Area 13) Editorial Board Member of International Journal of Statistics and Systems (ANVUR Area 13) Recent research involves: Statistical preprocessing of crowdsourced data for Nigerian food prices Quantile regression with heteroskedasticity and non-crossing constraints Electricity demand forecasting via Reg-SARMA models Exchange rate prediction using simultaneous prediction intervals Time series outlier detection and smoothing techniques She contributes to academic governance through the Laboratorio Statistico Informatico (Statistical Informatics Lab) within DESF. Teaching includes undergraduate and graduate courses in Statistics, with materials available in both Italian and English.
Anthony Cossari is a University Researcher in Statistics (SECS-S/01) at the Department of Economics, Statistics, and Finance "Giovanni Anania" (DESF) at the University of Calabria, where he has been employed since September 1, 2003. He holds a Laurea in Scienze Statistiche ed Attuariali from the University of Calabria (1995) with highest honors. Cossari is a member of the Italian Statistical Society (SIS) and the European Network for Business and Industrial Statistics (ENBIS). His research interests center on statistical experimental design methodologies: Screening designs for identifying significant factors in preliminary experimental phases Supersaturated designs for studying numerous factors with limited experimental runs Follow-up designs to enhance initial experimental analyses Robust designs for minimizing variability from environmental factors Cossari maintains active research collaborations across disciplines, particularly with medical researchers from the Catholic University of the Sacred Heart in Rome and mechanical engineering researchers from the University of Calabria. His publication record shows a transition from purely statistical experimental design research to increasingly collaborative medical research, especially in sepsis prognosis, alcoholic cardiomyopathy, and dental health in patients with alcohol use disorders. Recent publications (2021-2025) demonstrate strong interdisciplinary work while maintaining his core statistical expertise. His academic service includes membership on the Departmental Board (2004-2006) and the Scientific-Technical Committee of the Interdepartmental Library of Economic and Social Sciences "E. Tarantelli" (2007-present). He has served as thesis advisor for numerous undergraduate and graduate students across various statistical topics and was a member of the Doctoral Committee for the PhD program in "Economic History, Demography, Institutions and Society in Mediterranean Countries" (2007-2009).
Barbara Pacini is a full professor of Statistics in the Department of Political Science at the University of Pisa. She coordinates the collection and processing of institutional statistical data as the University's Delegate for Statistics, supporting policy evaluation and strategic decision-making. Her academic journey includes roles at the University of Florence and University of Bologna. Born in Pistoia, 1964 PhD in Applied Statistics (1994) Full Professor at University of Pisa since 2016 Her research focuses on causal inference , policy evaluation , and nonparametric methods , with applications in public administration, welfare systems, and financial markets. She has taught advanced statistical methods to undergraduate, graduate, and doctoral students. Her recent work spans deceptive advertising regulation , small area estimation , and sustainable development , reflecting interdisciplinary applications of statistical theory. Articles highlight collaborations with European institutions and methodological innovations in policy analysis.
Giuseppe De Luca is a Research Fellow at the Department of Physics and Chemistry - Emilio Segrè, University of Palermo. His work spans interdisciplinary domains including theoretical physics, biophysics, machine learning, and social dynamics. Research Themes : Model averaging techniques, liquid-liquid phase separation, quantum complexity, holography, and social exclusion effects in education. Methodologies : Bayesian inference, multiscale analysis, and machine learning optimization. Recent publications focus on weighted-average least squares estimation, structural degradation in biomaterials, and virtual reality applications in psychological research. He contributes to the SHARE project with statistical expertise in survey weighting and imputation strategies.
Stefano Vigogna is an Associate Professor in the Department of Mathematics at the University of Rome Tor Vergata with significant contributions to theoretical machine learning. He is affiliated with the Rome Center on Mathematics for Modeling and Data Sciences (RoMaDS), focusing on the mathematical foundations of learning algorithms. His research expertise spans: Machine Learning Statistical Learning Theory Harmonic Analysis Professor Vigogna's publication record demonstrates deep theoretical work connecting advanced mathematics to machine learning. His research investigates the spectral properties, geometric structure, and convergence behavior of neural networks using functional analysis and harmonic analysis techniques. Notable publications include his 2022 ICML paper on multiclass learning with exponential convergence rates and numerous works exploring the mathematical properties of deep learning systems through reproducing kernel spaces. He teaches Statistica for the Master's program in Environmental Biology and Statistical Learning for the Master's program in Pure and Applied Mathematics, reflecting his dual expertise in mathematical theory and practical data science applications. Professor Vigogna maintains active collaborations with leading researchers including Lorenzo Rosasco and Ernesto De Vito, advancing our fundamental understanding of learning algorithms through rigorous mathematical analysis. His work represents an essential bridge between pure mathematics and the theoretical foundations of modern artificial intelligence.
Hugo Lavenant is an Assistant Professor in the Department of Decision Sciences at Bocconi University in Milan, Italy. His academic journey includes a PhD in mathematics from Université Paris-Sud under Filippo Santambrogio (2016-2019) and a postdoctoral fellowship at the University of British Columbia (2019-2020) working with Young-Heon Kim, Brendan Pass, Geoffrey Schiebinger, and Dave Schneider. Professor Lavenant's research spans theoretical and applied aspects of mathematical analysis, with a focus on optimal transport theory , calculus of variations , and Bayesian statistics . His work explores the geometry of the Wasserstein space, numerical solutions to dynamical optimal transport problems, and applications to biological data analysis. He has made significant contributions to understanding harmonic mappings in the Wasserstein space, connections between optimal transport and nonlinear elasticity, and the application of optimal transport to trajectory inference in biological systems. Analysis of Professor Lavenant's recent publications reveals a strong trend toward interdisciplinary applications of optimal transport, particularly in statistics and biology. His work bridges pure mathematical theory with practical computational methods, with increasing focus on developing tractable statistical tools based on optimal transport distances. The research spans theoretical mathematics, computational methods, and applications to real-world data analysis problems. Professor Lavenant actively supervises graduate students, currently advising PhD candidates George Kanchaveli and Francesco Mascari (both co-advised with Marta Catalano), as well as Master's students Mathis Hardion and Niccolò Bargellini. His teaching portfolio includes Mathematical Analysis 2, Real Analysis I, and Advanced Analysis and Optimization 1 at Bocconi University. His scholarly contributions demonstrate a consistent focus on advancing both the theoretical foundations and practical applications of optimal transport, with growing emphasis on statistical methodology and biological applications in recent years.
Matteo Dellacasagrande serves as a Researcher at the Department of Mechanical, Energy, Management and Transport Engineering (DIME) within the Polytechnic School of the University of Genoa. His academic appointments include membership on the Joint Teacher-Student Commission and teaching responsibilities for advanced courses in aircraft propulsion systems. His research focuses on fluid dynamics in turbomachinery , particularly low-pressure turbine optimization, separated flow modeling, and aircraft engine design. Key methodologies include computational fluid dynamics, experimental validation using large databases, and statistical modeling techniques like Bayesian Lasso for flow prediction. His work bridges theoretical fluid mechanics with practical aerospace engineering applications. Recent publications demonstrate consistent focus on turbine blade aerodynamics (2024-2025), with significant contributions to loss mechanism analysis in low-pressure turbines and novel approaches to modeling separation bubbles. His research integrates experimental data with advanced statistical methods to improve prediction accuracy in complex flow scenarios. Teaching activities: AIRCRAFT ENGINES (Master's Degree in Mechanical Engineering - Energy and Aeronautics) AIRCRAFT PROPULSION (Master's Degree in Mechanical Engineering - Energy and Aeronautics) DESIGN OF MACHINES AND ENERGY SYSTEMS Professional engagement: Member of the Joint Teacher-Student Commission at the Polytechnic School, with office hours by appointment via institutional email.
Antonio Parisi serves as a Researcher in the Department of Economics and Finance at the Faculty of Economics, University of Rome Tor Vergata. His academic work centers on economic statistics within the SECS-S/03 sector, contributing to both research and teaching missions of the institution. For the 2025-2026 academic year, he instructs "Coding for Economic Applications" for Master's students and "Quantitative Methods I" for undergraduate programs. His research portfolio emphasizes Economic Statistics as the foundational discipline, with specialized expertise in Bayesian Inference for probabilistic modeling, Statistical Theory and Methods for econometric frameworks, and Computational Methods for algorithmic implementation. This triad of interests positions him at the intersection of theoretical statistics and practical economic data analysis. Available for student consultations on Thursdays at 11am during class sessions or by prior arrangement, Dr. Parisi maintains office presence in Room 59. Direct communication channels include email antonio.parisi@uniroma2.it and telephone extension 5914, facilitating academic collaboration and mentorship within the department's vibrant scholarly community.
Consuelo R. Nava is Associate Professor of Economic Statistics at the University of Aosta Valley (University of Valle d'Aosta) in the Department of Economic and Political Sciences. She also serves as a visiting professor at the Catholic University of the Sacred Heart in Milan and the University of Turin. Additionally, she is a member of the Cross-Border Center on Tourism and Mountain Economies. Dr. Nava earned her undergraduate degree (laurea triennale) in Business Economics from the University of Aosta Valley and Economics from the University of Turin, both with highest honors (110 e lode). She completed her PhD in Economics with a focus on applied mathematics and statistics at the University of Turin's Vilfredo Pareto Doctoral School in 2014. Following her doctorate, she worked as a research fellow at the Department of Economics and Statistics Cognetti de Martiis at the University of Turin (2014-2016) and as a research grant recipient with the Department of Translational Medicine at the University of Eastern Piedmont (2016-2017). Dr. Nava's research focuses on econometrics, time series analysis in the frequency domain, price indices, Bayesian inference, and discrete choice models. Her work bridges theoretical statistical methods with practical applications in economics, energy markets, tourism, and public health. She has developed innovative approaches to price index construction, switching behavior analysis in electricity markets, and Bayesian methods for modeling complex economic phenomena. Her publications demonstrate a strong interdisciplinary approach, with research spanning economic statistics, public health applications, tourism economics, and energy market analysis. Recent work shows an increasing focus on applying machine learning techniques alongside traditional econometric methods, particularly in analyzing startup survival during economic crises and modeling consumer behavior in liberalized markets. Dr. Nava has received several prestigious awards and recognitions: TEM Summer School Grant, Aosta, Italy Civil Society Talents Fellowship, Giovanni Goria Foundation, Asti, Italy Junior DESPINA Fellow, Big Data Lab, Turin, Italy Google Early Career Researchers Travel Grant, ISBA Socialis Prize, 7th edition, for her thesis on Corporate Social Responsibility Honor Student Scholarship, Collegio Carlo Alberto, Moncalieri, Italy As an educator, Dr. Nava has taught extensively across multiple institutions including Statistics, Quantitative Methods for Management, Econometrics, and Bayesian Statistics. She has served as a reviewer for several academic journals including Economics of Innovation and New Technology, Italian Journal of Applied Statistics, L'Industria, and Bayesian Analysis. Her research projects include studies on cruise ship impacts on destinations, illegal gambling analysis, switching behavior in European electricity markets, and Bayesian methods for analyzing asbestos exposure effects on mesothelioma risk. Her work with the Cross-Border Center on Tourism and Mountain Economies reflects her commitment to regional economic development research.
Giulia Cereda serves as Associate Professor in the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) at the University of Florence since 2025. Her academic journey includes prior roles as Fixed-term Researcher (RTD-b, 2022-2024), Research Fellow (2021-2022), and Swiss National Science Foundation Postdoc Mobility Fellow (2019-2021) at Leiden University and University of Florence. She holds a Joint PhD in Statistics from Leiden University and University of Lausanne (2011-2016), complemented by Master's and Bachelor's degrees in Mathematics from the University of Milan. Her research spans forensic statistics with focus on rare type match problems in DNA evidence evaluation, medical statistics applied to SARS-CoV-2 pandemic modeling, and machine learning implementations for biogeographical ancestry prediction. Recent publications demonstrate methodological innovations in Bayesian approaches for forensic evidence, compartmental modeling of epidemic dynamics, and supervised learning applications in population genetics. Analysis of her 14 most recent publications (2020-2025) reveals dual research thrusts: (1) forensic statistics addressing DNA mixture interpretation and rare haplotype matching through Bayesian frameworks, and (2) epidemiological modeling of smoking dynamics and SARS-CoV-2 transmission using compartmental models with uncertainty quantification. Her work bridges theoretical statistics with practical public health and forensic applications, frequently employing machine learning for complex prediction tasks. Supported by the Swiss National Science Foundation for postdoctoral research (2019-2021), she has contributed to pandemic response through Tuscan regional modeling and school-based screening strategies. Current office hours are Thursdays 3:00-4:00 PM by appointment, with ongoing research in forensic identification systems and epidemic forecasting methodologies.
Emanuela Dreassi is a Full Professor of Statistics at the University of Florence, where she currently serves as Director of the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) from November 1, 2024, to October 31, 2028. She is affiliated with the School of Economics and Management and has held various institutional roles including Director of the Bachelor's Program in Statistics (2014-2022) and Vice-President of the School of Economics and Management (2019-2022). Her research spans hierarchical Bayesian models and spatial statistics, with methodological advancements in specifying and estimating models for multilevel data, missing data, and latent variable models. She has made significant contributions to robust analysis in small area estimation, compatibility of conditional distributions, semicontinuous data modeling, Bayesian predictive inference, and knockoffs construction. Her work bridges theoretical statistics with practical applications in epidemiology, environmental studies, and medical research. Dreassi's recent publications (2020-2025) reveal a strong focus on methodological innovations in Bayesian statistics, spatial analysis, and knockoff filters for variable selection. She has published extensively in top statistical journals while also collaborating on interdisciplinary research in medical fields including plastic surgery and epidemiology. Her work demonstrates a consistent trajectory of advancing statistical methodology while maintaining strong connections to real-world applications across multiple domains. As an active referee for numerous prestigious journals including Biometrics, Journal of the Royal Statistical Society, and Statistics in Medicine, Dreassi contributes significantly to the scholarly community. She has coordinated research units for PRIN and HORIZON2020 projects and participated in numerous national and international conferences as organizer, scientific committee member, and session chair.
