Find Academic Supervisors

Diego Regruto TomalinoView profile

Associate Professor

• System Identification
• Robust Control
• Convex Relaxation Techniques
• +5 more

Diego Regruto Tomalino is an active Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Polytechnic University of Turin, Italy, with contact details +39 0110907077 and diego.regruto@polito.it. He holds leadership roles in the IEEE Control Systems Society as Scientific Committee member of the Technical Committee on System Identification and Adaptive Control (2008-present) and served as its President (2013-2015). His work spans academic research, teaching, and international collaboration within control engineering. Education : Laurea in Electronic Engineering, Politecnico di Torino (2000) Ph.D. in Systems Engineering, Politecnico di Torino (2004) Research Interests : System Identification : Specializing in set-membership, sparse, and LPV techniques for nonlinear systems with applications in automotive and biomedical domains. Optimization & Control : Developing convex relaxation methods for model predictive control, robust control algorithms, and electrified vehicle propulsion systems. Control Education : Innovating through remote laboratories and industrial platforms to enhance engineering pedagogy globally. Publication Trends : Recent work (2021-2024) focuses on sparse optimization for cyber-physical security and electrified vehicles, extending set-membership identification to continuous-time systems, and educational frameworks. Dominant fields include control theory, optimization, and automotive applications with increasing emphasis on data-driven methods and sparse computing. Scientific Awards : No specific prizes, medals, or fellowships were documented in the provided texts. Advising and Grants : PhD Supervision : Alice Re (2024-present, sparse optimization for identification), Francesco Ripa (2023-present, electric vehicle propulsion management), Simone Pirrera (2021-2025, dynamical systems learning). Research Projects : Principal investigator for OpThermEV (2021-2022) on thermal management of electric vehicles, plus corporate-funded automotive research initiatives. Labs and Teams : Core member of the SIC (System Identification & Control) research group and LAB 11 at DAUIN. Leads international control education collaborations, notably a remote laboratory partnership between Asian and European institutions.

Cristiano VarinView profile

Professor

• Statistical Methodology
• Composite Likelihood Inference
• Copula Regression
• +3 more

Cristiano Varin is a Full Professor in Statistics at the Ca' Foscari University of Venice , affiliated with the Department of Environmental Sciences, Computer Science and Statistics (DAIS). He works at the Scientific Campus in via Torino and maintains the DAIS website for research and teaching updates. His research focuses on: Composite likelihood inference - A key methodological contributor with seminal papers in Biometrika and Statistica Sinica Copula regression - With practical implementations in R software Meta-analysis - Including improved likelihood inference techniques Spatial statistics - With applications to environmental data Paired comparison modeling - Applied to sports analytics and behavioral studies Recent publications demonstrate expertise in crossed random effects models (2025), ridge regression for paired comparisons (2024), and thermal comfort range analysis (2023). Scientific recognition includes: Royal Statistical Society Read Paper (2015) on journal citation modeling Gumbel Lecture (2006) by German Statistical Society Ca' Foscari Teaching Award (2020) He has collaborated with institutions including: Swiss National Science Foundation Natural Sciences and Engineering Research Council of Canada Norwegian Council of Research

• Econometrics
• Time Series Analysis
• Bootstrap Inference
• +3 more

Iliyan Georgiev is an Associate Professor of Econometrics at the Faculty of Economics, University of Bologna, since 2018. Previously, he held roles at Nova School of Business and Economics (2011–2015) and the Agency for Economic Analysis and Forecasting in Sofia (1995–1998). He obtained a PhD in Economics from the European University Institute (2004), and degrees in Mathematics and International Economic Relations from Sofia University and Karl Marx Institute of Economics. His research focuses on econometric theory, time series analysis, bootstrap inference, and spurious correlations. He teaches Econometrics and Mathematics, including differential calculus and stochastic calculus. Georgiev’s work emphasizes robust statistical methods for non-stationary processes and rare events, with applications in financial and economic forecasting. His recent publications explore predictive regression, canonical correlation analysis, and unit root testing under heavy-tailed distributions. He has contributed to methodologies addressing parameter instability and spurious correlations in time series data. No explicit scientific awards are listed, but he achieved National Scientific Qualification for Full Professor in Econometrics (MIUR, 2018). No grants or lab affiliations are mentioned in the provided texts.

Augusto FerranteView profile

Professor

• Control Theory
• Quantum Computing
• Linear Algebra
• +5 more

Augusto Ferrante is a Full Professor at the Department of Information Engineering, University of Padova. His research spans control theory, quantum computing, and linear algebra, with a focus on optimal control, spectral estimation, and matrix analysis. University: University of Padova Department: Information Engineering Research Interests: Ferrante's work addresses theoretical and applied problems in control systems, including quantum control, Riccati equations, and covariance matrix estimation. His contributions bridge mathematical rigor with engineering applications, particularly in spectral analysis and optimization. Publications Trends: His recent work (2014-2012) emphasizes quantum channel estimation, entropy-based spectral methods, and generalized Riccati equations, reflecting a synthesis of control theory and quantum information science. Book Chapters: He has co-authored works on modeling, estimation, and control, including a 2007 Modeling, Estimation and Control volume dedicated to Giorgio Picci.

• Statistical Methodology
• Machine Learning
• Parametric Inference
• +7 more

Claudia Di Caterina is an Associate Professor of Statistics at the University of Verona's Department of Economics. Her research focuses on statistical methodology, including likelihood asymptotics, pseudo-likelihoods, anytime-valid inference, and bias reduction techniques. She holds a PhD in Statistical Sciences from the University of Padova and has conducted research at University College London and the University of Toronto. She teaches courses such as Machine Learning for Economics and Statistics for Business Analysis across various programs. Her academic roles include membership in the Economics Department Council and involvement in committees like the Comitato per le Ricerche con Dati sull'Individuo (CORDI) and Commissione Paritetica Docenti-Studenti. She contributes to research laboratories like VELE (Verona Experimental Lab in Economics) and collaborates with centers such as CRIEP and CIDE. Her publications span topics in high-dimensional data analysis, confidence sequences, and causal inference. She actively engages in public events and third mission activities, including workshops on climate risk and cyber security survey methodologies.

Fulvio CorsiView profile

Professor

• Financial Economics
• Financial Econometrics
• Empirical Finance
• +4 more

Fulvio Corsi is a Full Professor of Econometrics at the Department of Economics and Management, University of Pisa. He holds a PhD in Finance (2005) from the University of Lugano and earlier degrees from the University of Pisa and Venice International University. His research focuses on financial econometrics, volatility modeling, and systemic risk analysis. Education: Ph.D. in Finance (Hons), University of Lugano, 2005 MSc in Economics and Finance (Hons), Venice International University, 1999 Diploma in Economics and Business (Hons), University of Pisa, 1998 Research Interests: Volatility dynamics modeling (HAR-RV model) High-frequency financial data analysis Financial bubbles and systemic risk Score-driven models for time-varying parameters Covariance and correlation measurement Derivative pricing and risk management His work has been recognized with the 2010 Engle Prize for the influential HAR-RV model. Recent research emphasizes structural VAR identification, nonlinear dynamics, and macroeconomic nowcasting applications. Over 20 years of academic contributions include 40+ peer-reviewed articles in top journals like Journal of Financial Economics and Journal of Econometrics.

Roberto Di MariView profile

Assistant Professor

• Model-Based Clustering
• Finite Mixture Models
• Latent Variable Models
• +4 more

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.

Daniele DuranteView profile

Associate Professor

• Network Science
• Bayesian Methods and Computation
• Categorical Data
• +5 more

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.

• Financial Mathematics
• Derivatives Pricing
• Risk Management
• +3 more

Gianluca FUSAI is a Professor at the University of Eastern Piedmont 'Amedeo Avogadro', affiliated with the Department of Economics and Business Studies. His research focuses on financial mathematics, derivatives pricing, and risk management. He has contributed to sustainable development goals related to economic risk modeling. Key research interests include stochastic processes, option pricing, and econometric modeling. His work often addresses practical challenges in financial engineering, such as currency risk conversion and jump diffusion processes in markets. Recent publications (2024) explore topics like covariance matrix conversion across currencies, jump counting methodologies in financial data, and novel risk-free rate pricing models. He leads projects on climate-transition risk management and insurance capital requirements under Solvency II regulations. Notable collaborations include projects on indirect climate risk measurement (2023-2025) and counterparty risk management in derivatives, funded by the Italian Ministry of Universities and Research.

Emanuele BorgonovoView profile

Professor

• Sensitivity Analysis
• Decision Analysis
• Risk Assessment
• +6 more

Emanuele Borgonovo is a Full Professor and Director of the Department of Decision Sciences at Bocconi University. He holds a PhD from MIT and an MSc in Nuclear Engineering from Politecnico di Milano. As a Research Affiliate at MIT's Department of Nuclear Science and Engineering, he contributes to interdisciplinary research. His roles include Co-editor-in-Chief of the European Journal of Operational Research and former President of the Decision Analysis Society of INFORMS. His research focuses on sensitivity analysis methodologies, including the development of Differential Importance Measures adopted by NASA. Applications span risk assessment (e.g., space systems, climate models), machine learning, and optimal transport theory. Key contributions include methods for feature importance, interaction quantification, and probabilistic sensitivity measures. Education: PhD, Massachusetts Institute of Technology, 2001 MSc in Nuclear Engineering (Mathematics/Physics focus), Politecnico di Milano Recipient of the 2020 Bocconi Teaching Innovation Award, Borgonovo teaches courses in quantitative methods, analytics, and computer experiments across undergraduate, graduate, and PhD programs. He pioneered the Bachelor in Economics, Management, and Computer Science at Bocconi. His work bridges theoretical advancements with practical applications in sustainability, business analytics, and policy, leveraging collaborations with institutions like NASA and the Silvio Tronchetti Provera Foundation.

Eugenio MelilliView profile

Associate Professor

• Bayesian Statistics
• Confidence Distributions
• Population Forecasting
• +4 more

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.

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
• 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

Wolfgang ErbView profile

Associate Professor

• Multivariate approximation
• Lissajous curves
• Uncertainty principles on manifolds and graphs
• +5 more

Wolfgang Erb is an Associate Professor in Numerical Analysis at the Department of Mathematics “Tullio Levi-Civita”, University of Padua (Italy). He specializes in multivariate approximation and computational methods for inverse problems, with applications in biomedical imaging. Current Position: Associate Professor, University of Padua Previous Positions: Postdoctoral Fellow at University of Lübeck, Assistant Professor at University of Hawaii at Manoa Education: PhD in Mathematics, Technical University of Munich (2010) Research Interests: Wolfgang Erb's research focuses on mathematical models for signal processing, including: Lissajous curve-based approximation techniques Uncertainty principles applied to manifolds and graphs Kernel methods for learning on networks Fast reconstruction algorithms for inverse problems Applications in Magnetic Particle Imaging and biomedical imaging Recent Publications Trends: His work centers on numerical analysis and computational mathematics, particularly in polynomial interpolation frameworks and biomedical imaging applications. Facilities & Collaborations: Wolfgang engages with advanced university facilities including High Density EEG systems, MRI & PET scanners, and computational infrastructure at University of Padua. Teaching: He teaches “Mathematical Models and Numerical Methods for Big Data” in the Data Science master’s program.

• Numerical Analysis
• Approximation Theory
• Quadrature and Cubature Formulas
• +5 more

Alvise Sommariva is an Associate Professor with Full Professor habilitation in Numerical Analysis (MATH-05/A) at the Department of Mathematics, University of Padua. He maintains an active research program in approximation theory and numerical analysis while contributing significantly to academic service through conference organization and editorial work. Department of Mathematics, Tullio Levi-Civita, University of Padua Visiting Professor at Jagiellonian University, Kraków Member of multiple scientific committees for international conferences Co-managing editor of Journal of Approximation Software His research focuses on Numerical Analysis and Approximation Theory, particularly Quadrature and Cubature Formulas, Multivariate Polynomial Approximation, and Meshless Methods. He develops practical computational tools for numerical integration and approximation across various geometric domains including intervals, triangles, squares, disks, spheres, and tetrahedrons. Professor Sommariva actively contributes to the approximation theory community through leadership in the RITA (Ricerca Italiana in Teoria dell'Approssimazione) research network and editorial work, including special issues dedicated to prominent researchers in the field. Editor of Special Issue Dedicated to Len Bos (2024) Scientific committee member for multiple international conferences through 2026 Regular presenter at major approximation theory conferences He teaches in the Doctoral Program in Mathematical Sciences and supervises bachelor students in the Laurea degree program at University of Padua, with particular recognition for his Numerical Analysis courses in Energy Engineering. Professor Sommariva maintains an extensive online presence with computational resources for approximation theory, including MATLAB and Python implementations of numerical methods, and actively collaborates with researchers across Europe and beyond.