جستجوی استاد راهنمای دانشگاهی

Sonia Petroneمشاهده پروفایل

استاد

• Bayesian Statistics
• Predictive Modeling
• Bayesian Nonparametrics
• +۱ مورد دیگر

Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).

Hugo Georges Victor Lavenantمشاهده پروفایل

استادیار

• Optimal Transport
• Calculus of Variations
• Convex Analysis
• +۳ مورد دیگر

Hugo Georges Victor Lavenant serves as Assistant Professor in the Department of Decision Sciences at Bocconi University, Milan, where he has held a faculty position since 2020. Previously, he completed a postdoctoral fellowship at the University of British Columbia (2019-2020) under the Pacific Institute of Mathematical Sciences and earned his PhD in Mathematics from Université Paris-Sud (2016-2019) under Filippo Santambrogio's supervision. His academic foundation includes: PhD in Mathematics, Université Paris-Sud (2016-2019) Studies at École Normale Supérieure (2012-2016) covering mathematics, physics, history, and philosophy of science Classes préparatoires in mathematics and physics (2010-2012) Lavenant's research centers on optimal transport theory and its applications across mathematical disciplines. He investigates geometric structures in Wasserstein spaces, develops numerical methods for dynamical optimal transport, and bridges theoretical advances with Bayesian statistics. His work demonstrates particular innovation in trajectory inference for biological data and dependence measures for random measures, connecting pure mathematics with computational statistics. Recent publications reveal accelerating interdisciplinary impact, with 2024-2025 works extending optimal transport to machine learning (kernel methods, variational inference) and data science (opinion dynamics, single-cell analysis). This trajectory shows increasing methodological sophistication in handling measure-valued mappings and non-smooth geometries while maintaining computational tractability. Award recognition includes: Pacific Institute of Mathematical Sciences Postdoctoral Fellowship Lavenant actively mentors early-career researchers through formal advising relationships and collaborative projects. He currently supervises two PhD candidates (George Kanchaveli and Francesco Mascari, co-advised with Marta Catalano) and has guided Master's students including Mathis Hardion and Niccolò Bargellini. His teaching portfolio spans advanced analysis, optimization, and real analysis courses at Bocconi, reflecting his commitment to mathematical rigor in education. He operates within Bocconi's Decision Sciences ecosystem while maintaining international collaborations with researchers at UBC, Université Paris-Sud, and statistical groups worldwide. Current projects focus on entropy-based transport methods and geometric approaches to nonparametric statistics, positioning his work at the intersection of theoretical mathematics and data-driven applications.

Silvia Polettiniمشاهده پروفایل

دانشیار

• Bayesian Statistical inference
• Official Statistics
• Statistical Disclosure Risk Estimation
• +۵ مورد دیگر

Silvia Polettini is an Associate Professor in Statistics at the Department of Social and Economic Sciences, University of Rome "La Sapienza". She has been with the university since 2011, achieving the rank of Associate Professor in 2020. Prior to this, she served as a Researcher at the University of Naples Federico II from 2005 to 2011, and worked at the National Institute of Statistics (ISTAT) from 2000 to 2005. Her academic journey began with a Statistical Officer position at the Italian Ministry of Health from 1998 to 2000. Her educational background includes: 1994: Bachelor's Degree with honors in Statistical and Demographic Sciences, University of Rome "La Sapienza" 1999: Ph.D. in Methodological Statistics, University of Rome "La Sapienza" Silvia Polettini's research spans several critical areas in modern statistics. Her work in Bayesian Statistical inference has produced innovative approaches for handling complex data structures. She has made significant contributions to Official Statistics , particularly in methodologies for data protection and disclosure risk assessment. Her expertise in Statistical Disclosure Risk Estimation and Microdata Protection has positioned her as a leading researcher in data privacy. The development of Bayesian Semi-parametric Modeling for Small Area Estimation represents another major strand of her work, with applications across social and economic domains. Her research on Measurement Error Models addresses fundamental challenges in data quality, while her work on Latent Variable Models and Survival Analysis extends into demographic and health applications. An analysis of her recent publications reveals a clear trajectory focusing on addressing underreporting issues in sensitive social phenomena, particularly violence against women, using sophisticated Bayesian methods. Her work bridges theoretical statistical innovation with practical applications in social policy. The integration of small area estimation techniques with measurement error models represents another significant theme, enabling more accurate regional and demographic analyses despite data limitations. Professor Polettini has been actively involved in numerous research projects, including several PRIN (Progetti di Rilevante Interesse Nazionale) initiatives and international collaborations. Her leadership in projects like "ESSnet on Statistical Disclosure Control" funded by Eurostat and "Data Linkage and Anonymisation" supported by the Isaac Newton Institute demonstrates her standing in the international statistical community. She has served as principal investigator for multiple research projects focused on disclosure risk estimation, small area modeling, and addressing underreporting in social data. Her teaching responsibilities include Statistics for Political Science and International Relations students, as well as Statistics for Economic Applications for International Economic and Financial Relations students. Office hours are held on Thursdays from 10am to 12pm, available both in person and online by appointment.

Antonio Lijoiمشاهده پروفایل

استاد

• Bayesian Nonparametrics
• Completely Random Measures
• Distribution Theory
• +۴ مورد دیگر

Antonio Lijoi is Professor of Statistics and Director of the PhD program in Statistics and Computer Science at Bocconi University. His research focuses on Bayesian nonparametrics, completely random measures, mixture models, and predictive inference. A Fellow of both the Institute of Mathematical Statistics and International Society for Bayesian Analysis, he serves on editorial boards for Bayesian Analysis and Electronic Journal of Statistics. Lijoi's recent publications examine network strategies within organizations, moral influence dynamics, and social network applications ranging from corporate misconduct analysis to the papal conclave selection process. His methodological contributions include developing frameworks for understanding how social networks create competitive advantages through brokerage and trust mechanisms.

Bruno Scarpaمشاهده پروفایل

استاد

• statistical models for big data
• text mining
• Bayesian nonparametrics
• +۳ مورد دیگر

Bruno Scarpa serves as Full Professor of Statistics and Data mining in the Department of Statistical Sciences at the University of Padua, holding a permanent faculty appointment with no indication of part-time status or former/retired designation. His research program centers on advanced statistical methodologies, with primary focus areas including statistical models for big data environments, text mining algorithms, and Bayesian nonparametric frameworks. Additional expertise spans skew-symmetric distributions theory, business statistics applications, and specialized statistical techniques for marketing research problems.

Daniela Marellaمشاهده پروفایل

استاد

• Statistical matching
• Bayesian networks
• Survey sampling
• +۴ مورد دیگر

Daniela Marella is a Professor at the Department of Social and Economic Sciences, Sapienza University of Rome. She teaches Statistics and holds office hours on Tuesdays from 10:00 AM to 12:00 PM. Her email address is daniela.marella@uniroma1.it . Teaching: Statistics (Sociology, Economics, Development Studies) Research: Statistical matching, Bayesian networks, survey sampling, uncertainty quantification Publications: 15+ articles on non-probability sampling, measurement error, and interrater agreement Her research focuses on statistical matching methodologies, Bayesian network applications in survey data, and handling selection bias in non-probability samples. She explores measurement error modeling, empirical likelihood approaches, and resampling techniques for complex survey designs. Recent publications emphasize uncertainty analysis in statistical matching, graphical models for data integration, and Bayesian structural learning. Key areas include non-ignorable sampling, ordinal categorical data agreement, and pseudo-population resampling frameworks.

• Bayesian Statistics
• Statistical Modeling
• Functional Data Analysis
• +۴ مورد دیگر

Raffaele Argiento is a Full Professor of Statistics at the Department of Economics, University of Bergamo since September 2021. His academic career focuses on advanced statistical methodologies with applications across various domains including environmental science, public health, and data analysis. His research is prominently featured in high-impact statistical journals and conference proceedings. Argiento's research interests center around Bayesian statistical methods, particularly in functional data analysis, nonparametric Bayesian modeling, and clustering techniques. His work demonstrates expertise in developing innovative statistical approaches for complex data structures, including spatio-temporal data, categorical variables, and high-dimensional datasets. His research has significant applications in environmental monitoring (particularly air pollution analysis), public health (obesity rate modeling), and seismic monitoring through crowdsourced data. His methodological contributions include advancements in mixture models, partition models, and computational algorithms for statistical inference. His recent publication record shows a strong trend toward developing computationally efficient Bayesian methods for real-world applications. The research spans from theoretical developments in nonparametric Bayesian statistics to practical implementations for environmental monitoring, health data analysis, and functional data processing. His work demonstrates a consistent focus on bridging theoretical statistical advancements with practical applications across multiple scientific domains. Professor Argiento teaches several advanced statistical courses at the University of Bergamo, including Applied Statistical Modelling , Probability and Statistics , and Statistical Models for both undergraduate and graduate programs in Economics and Data Analysis. His teaching reflects his research expertise, emphasizing modern statistical methodologies and computational approaches.

• Bayesian Statistics
• Statistical Modeling
• Data Science
• +۴ مورد دیگر

Gianluca Mastrantonio is an Associate Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin , Italy. He is a member of the Interdepartmental Center SmartData@PoliTO and actively contributes to the Statistics and Data Science research group. His academic roles include teaching in PhD programs (Mathematical Sciences, 2023-2025) and master's courses such as Statistical Methods in Data Science and Statistical Models/Statistical Learning . His research focuses on Bayesian Statistics and Hierarchical Models , with applications spanning Biostatistics , Environmental Monitoring , Machine Learning , and Computational Statistics . Key keywords include RNA Velocity , Sea Climate Analysis , Animal Movement Modeling , and Bayesian Software Development . Selected Scientific Awards Steering Committee Member, Royal Statistical Society (Emerging Applications, 2021-) Effective Member, International Statistical Institute (ISI, 2019-) Effective Member, Graspa (Italy, 2017-) Associate Editor, Journal of Statistical Computation and Simulation (2020-) Key Research Themes Bayesian Modeling of RNA Dynamics Environmental and Wildlife Behavior Analysis Statistical Software Development (Julia/R packages) Climatic Change-Point Detection Integration of Linear/Circular Data in Ecology Applications in Prostate Cancer Diagnostics

Gianluigi Pillonettoمشاهده پروفایل

استادیار

• System Identification
• Stochastic Systems
• Deconvolution Problems
• +۳ مورد دیگر

Gianluigi Pillonetto is an Assistant Professor at the Department of Information Engineering , University of Padova , where he has been employed since 2005. His academic career focuses on system identification, stochastic systems, and nonparametric regularization techniques. Born: January 21, 1975 in Montebelluna, Italy Education: Doctoral degree (1998) and PhD (2002) in Computer Science/Engineering Research roles: Visiting scholar (2000), Visiting scientist (2002), Research Associate (2002-2005) His research spans system identification, stochastic processes, and deconvolution problems, with a particular emphasis on Bayesian methods and kernel-based regularization. He has contributed to areas like distributed Gaussian regression, sparse system identification, and nonlinear stochastic modeling in physiological systems. Recent publications (2016-2021) examine Gaussian regression techniques for distributed systems, entropy-based kernel design, and nonlinear stochastic deconvolution. These works incorporate machine learning principles into control theory, focusing on applications in wireless communications, robotics, and biomedical engineering.

Mattia Zorziمشاهده پروفایل

دانشیار

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +۸ مورد دیگر

Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.

• Bayesian inference
• Machine learning
• Nonparametric methods
• +۵ مورد دیگر

Francesca Panero is an Assistant Professor (RTT) in Statistics at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. She also serves as a Visiting Fellow at the Department of Statistics of the London School of Economics and Political Science, where she was previously an Assistant Professor until May 2024. Additionally, she maintains an affiliation with the Grantham Research Institute on Climate Change and the Environment at LSE. Dr. Panero earned her PhD in Statistics from the University of Oxford in 2022, following undergraduate and master's studies at the Department of Mathematics of the University of Turin and Collegio Carlo Alberto. Her academic journey reflects a strong foundation in mathematical and statistical theory applied to complex real-world problems. Her research centers on Bayesian statistical methodologies with applications across multiple domains. She specializes in complex networks analysis using Bayesian nonparametric approaches, disclosure risk assessment for privacy preservation, spatio-temporal Gaussian process modeling for food insecurity prediction, and fair machine learning techniques. Her work demonstrates a consistent thread of developing rigorous statistical methods with practical societal applications, particularly in network science, privacy protection, and algorithmic fairness. Dr. Panero's publications reveal a strong trajectory in high-impact statistical research, with recent work focusing on sparse network modeling, fairness constraints in machine learning, and privacy-preserving statistical techniques. Her methodological contributions bridge theoretical statistics with applications in social sciences, public policy, and computational challenges. 2025 Visiting Lecturer at Faculty of Informatics, University of the Italian Switzerland (USI) Elected as j-ISBA Chair Elect for 2025-2026 Recipient of IMS junior research travel grant for ICSDS 2024 Awarded LSE Research and Impact Support Fund (RISF) grant for food insecurity research As an educator, Dr. Panero teaches Probability and Stochastic Processes at Sapienza University and has previously instructed Deep Learning and Artificial Intelligence courses at LSE. She actively mentors students and encourages PhD candidates interested in her research areas to contact her. She is also a core member of GENIAL, an LSE focus group examining how students utilize generative AI tools for learning. Beyond her technical work, she has been engaged in equality, diversity, and inclusion initiatives since 2020 and maintains an active academic blog discussing data visualization, research projects, and academic life.

Andrea Tancrediمشاهده پروفایل

استاد

• Bayesian Inference
• Bayesian Computational Methods
• Biostatistics
• +۹ مورد دیگر

Andrea Tancredi is a Professor at the Department of Methods and Models for Economy, Territory, and Finance, Sapienza University of Rome. His research focuses on Bayesian inference, computational statistics, and their applications in biostatistics, population size estimation, and health economics. He has coordinated multiple research projects funded by the University, including studies on record linkage, approximate Bayesian computation (ABC), and stochastic frontier models. Research Interests: Bayesian Inference Computational Methods (MCMC, ABC) Population Size Estimation Record Linkage and Data Integration Extreme Value Theory Biostatistics and Health Economics Teaching: He teaches courses such as Basic Statistics, Advanced Statistical Methods, and Extreme Value Theory at the Faculty of Economics and doctoral programs in Economics. Publications and Citations: With 47 publications (23 journal articles), his work spans Bayesian modeling for microbial diversity, climate-conflict risk forecasting, and multi-state health models. His research is cited 682 times on Google Scholar (h-index 12) and 401 times in Scopus (h-index 9).

Fabio Previdiمشاهده پروفایل

استاد

• Control Systems
• Mechatronics
• Aerospace Engineering
• +۵ مورد دیگر

Fabio Previdi is a Full Professor in Control Systems at the Department of Management, Computer and Industrial Engineering, University of Bergamo. His career spans roles from Research Assistant at the University of Glasgow (1999-2000) to Associate Professor (2006-2017) before assuming his current position. He holds a PhD in Control Systems from Politecnico di Milano (1999), MSc in Electronic Engineering (1993) and Physics (2008) from Politecnico di Milano and University of Milan. Research focuses on control systems, mechatronics, and aerospace applications. Key areas include electromechanical actuators, fault detection, system identification, and biomedical engineering. Over 150 peer-reviewed papers highlight contributions to topics like robust control, machine learning for industrial systems, and pandemic modeling. Publications (2023-2020) address cutting-edge topics such as jamming detection filters for aircraft actuators, agent-based pandemic simulations, and kernel-based system identification. Awards: Not explicitly mentioned but notable contributions recognized through extensive publication record.

Igor Prünsterمشاهده پروفایل

استاد

• Bayesian Nonparametrics
• Bayesian Asymptotics
• Foundations of Statistical Inference
• +۷ مورد دیگر

Igor Prünster is a Professor of Statistics at Bocconi University, where he serves as Director of the Ph.D. Program in Statistics and Computer Science. He is a Fellow of the Bocconi Institute of Data Science & Analytics (BIDSA) and other distinguished societies. Bayesian Nonparametrics Bayesian Asymptotics Probabilistic Machine Learning Random Measures His editorial roles include Editor-in-Chief of Bayesian Analysis and Associate Editor of Annals of Statistics . He has mentored students who have won competitive awards, such as the Blackwell-Rosenbluth Award (2024) and Lawrence D. Brown Ph.D. Student Award (2024). Scientific Awards Fellow, American Statistical Association (2019) Fellow, International Society for Bayesian Analysis (2018) Fellow, Institute of Mathematical Statistics (2015)

• Calculus of Variations
• Partial Differential Equations
• Mathematical Analysis
• +۳ مورد دیگر

Edoardo Mainini is an Associate Professor at the University of Genova, specializing in mathematical analysis and applied mathematics. His research focuses on calculus of variations, partial differential equations, elasticity theory, optimal transport, and nonlinear dynamics. He has contributed to studies on fractional equations, material science, and stochastic processes. His work often bridges pure and applied mathematics, addressing problems in mechanics, probability, and geometric analysis. Mainini has collaborated extensively with researchers such as M. Kružík, D. Percivale, and U. Stefanelli, producing influential papers in journals like Calc. Var. Partial Differential Equations and Arch. Ration. Mech. Anal. . His recent articles (2020–2025) address topics ranging from fractional linear equations to Bayesian nonparametric models. Education includes a PhD in Mathematical Analysis from the University of Genova (2010) and a thesis on Infinite-dimensional porous media equations and optimal transportation . He has organized international conferences and seminars on variational methods and geometric structures. His research emphasizes rigorous mathematical frameworks for physical phenomena, including the linearization of elasticity models and the study of ground states in diffusion-dominated systems. Mainini’s contributions to carbon nanotube geometries and optimal transport theory have been recognized through invited talks at major events, such as the International School of Mathematics “Guido Stampacchia” . His work often explores the interplay between discrete and continuous models, with applications to materials science and geometric optimization.