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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.

• 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.

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

• 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.

• Numerical Analysis
• Multivariate Approximation Theory
• Polynomial Interpolation
• +5 more

Marco Vianello is a Full Professor of Numerical Analysis at the Department of Mathematics of the University of Padova, Italy. He has held this position since May 2017, following his tenure as Associate Professor (2001-2016) and Lecturer (1993-2000) at the same institution. Professor Vianello is Co-Editor-in-Chief of the Journal of Approximation Software (2024-) and was previously Managing Editor of Dolomites Research Notes on Approximation (2008-2022). He is the co-founder of the CAA: Padova-Verona Research Group on Constructive Approximation and Applications and an active member of the national research networks RITA (Rete ITaliana di Approssimazione) and SIMAI ANA&A Activity Group. Professor Vianello's research focuses on Multivariate Approximation Theory, with particular emphasis on Polynomial Interpolation, Numerical Quadrature and Cubature, Meshless Approximation, and Polynomial Optimization. His work includes the development of special point sets such as Padua Points, Approximate Fekete Points, Subperiodic Trigonometric Points, and CATCH (Caratheodory-Tchakaloff) Points. His research has significant applications in numerical methods for PDEs, computational mechanics, and scientific computing. His research has been recognized with the Optimization Letters Best Paper Award (2020), and his work has accumulated approximately 3,700 citations on Google Scholar. His algorithms have been implemented in several major software packages including ZEMAX OpticStudio, CP2K for molecular dynamics, and the Chebfun package. Optimization Letters Best Paper Award (2020) for 'Quadrature Based Polynomial Optimization' Approximately 3,700 citations on Google Scholar Approximately 1,200 citations by about 700 authors on MathSciNet Implementation of algorithms in ZEMAX OpticStudio, CP2K, Chebfun, and other scientific software Professor Vianello has advised 3 Ph.D. students to completion and approximately 60 B.Sc. and Master's dissertations. As a research coordinator from 2003-2025, he has secured approximately 150 kEuros in funding, supporting an average of 5 researchers per year. He has served on numerous hiring and evaluation committees, including the ASN (Italian National Scientific Habilitation) Committee in Numerical Analysis (2024-26) and hiring committees for professorships at Padova and Calabria Universities. His research group, the CAA: Padova-Verona Research Group on Constructive Approximation and Applications, maintains an active international collaboration network with researchers from institutions including Lawrence Livermore National Laboratory, Jagellonian University, University of Calgary/Verona, University of Toulouse, Colorado School of Mines, ANU Canberra, Indiana University, and several others across Europe, North America, and Asia.

Stefano De MarchiView profile

Professor

• Numerical Analysis
• Approximation Theory
• Radial Basis Functions
• +3 more

Stefano De Marchi is a Full Professor of Numerical Analysis at the University of Padua, currently on sabbatical leave from October 1, 2024, through September 2025. He is affiliated with the Department of Mathematics "Tullio Levi-Civita" and has strong connections with multiple research centers including the Padova Neuroscience Center and Padova Center Network Medicine. His research focuses on Numerical Analysis , particularly Approximation Theory , Radial Basis Functions (RBF) , and Multivariate Polynomial Approximation . He has developed significant expertise in kernel methods, interpolation techniques, and computational mathematics with applications in scientific computing. De Marchi serves on the editorial boards of multiple prestigious journals including Axioms , Mathematics , BIT Numerical Mathematics , and Frontiers in Applied Mathematics and Statistics . His academic leadership extends to co-founding the CAA research group and participating in the Gruppo Nazionale di Calcolo Scientifico GNCS-INdAM. As an educator, he has taught numerous courses including Numerical Calculus for Mechanical Engineering and specialized courses on Approximation Theory and Applications. He has authored important educational resources including "Introduzione al Calcolo Numerico" and "Exercises of Numerical Calculus With Solutions in Matlab/Octave" as well as comprehensive lecture notes on multivariate polynomial approximation and radial basis functions. Beginning October 1, 2025, he will transition to the Department of Medicine (DIMED) at the University of Padua, reflecting the interdisciplinary nature of his work which bridges mathematical theory with medical applications.

• Econometrics
• Computational Statistics
• Financial Risk Management
• +5 more

Marco Bee is a Full Professor at the Department of Economics and Management, University of Trento. His expertise spans applied econometrics, computational statistics, finance, and risk modeling. He focuses on methodologies for handling heavy-tailed distributions, extreme value theory, and machine learning applications in financial risk assessment. Education details are available in his CV (CVeng.pdf). His research interests include developing statistical models for operational risk, volatility forecasting, and credit scoring, often employing mixture models, copula-based approaches, and indirect inference techniques. He has contributed significantly to the analysis of spatial econometrics and the application of extreme value theory to financial crises and insurance analytics. His recent work emphasizes tail risk estimation, with over 150 publications since 2006. Notable contributions include methodologies for Value-at-Risk (VaR) forecasting, distribution fitting for skewed data, and the use of machine learning to predict defaults in small businesses. His research bridges theoretical statistics and practical financial applications, with a focus on high-frequency data and scenario-based risk analysis. Awards and grants are not explicitly listed in the provided data, but his extensive publication record reflects recognition in quantitative finance and econometrics. He advises students on topics related to computational econometrics and risk modeling, though specific advisee names are not documented here.

• Mathematical Analysis
• Calculus of Variations
• Partial Differential Equations
• +3 more

Menita Carozza is an Associate Professor in the Department of Engineering at Università del Sannio, Italy, specializing in Mathematical Analysis (MAT/05). Her research spans calculus of variations, Sobolev spaces, convex geometry, and function approximation. She has taught advanced mathematics courses to engineering students and collaborated extensively with researchers like Antonia Passarelli di Napoli and Jan Kristensen. Key Research Areas : Mathematical Analysis, Calculus of Variations, Convex Geometry, Sobolev Spaces, Function Approximation Her recent publications analyze polyconvex integrals, bi-Sobolev mappings, and geometric estimates, reflecting trends in mathematical analysis and applications to engineering. Despite extensive contributions, no formal awards or student advising lists are documented in public records. She can be contacted at carozza@unisannio.it.