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George KarabatsosView profile

Professor

• Bayesian Statistics
• Statistical Modeling
• Psychometrics
• +6 more

George Karabatsos is a Professor at the University of Illinois at Chicago (UIC), affiliated with the Department of Educational Psychology. His research focuses on developing and applying Bayesian statistical models, computational methods, and psychometric frameworks for complex data analysis. He has contributed to areas such as Bayesian nonparametrics, meta-analysis, causal inference, and educational measurement. His work has been supported by grants from the National Science Foundation, the Spencer Foundation, and the National Institutes of Health. Karabatsos has authored a menu-based statistical software package for analyzing data using over 100 models and served as an Associate Editor for journals like Psychometrika and Computational Statistics and Data Analysis . Education: PhD in Measurement, Evaluation, and Statistical Analysis (MESA) from the University of Chicago, 1998. Research interests include Bayesian modeling, computational statistics, psychometrics, and applications in education and biostatistics. His recent work emphasizes Bayesian methods for addressing issues like hidden bias, sensitivity analysis, and local dependence in statistical inference. Publications span topics such as nonparametric mixture models, meta-analytic techniques, and software development. His articles reflect a focus on methodological advancements and their practical implementation in real-world problems. Grants and advising: Karabatsos has led numerous research projects funded by major institutions. While specific student advisees are not listed, his software contributions indicate involvement in training researchers through accessible tools. Labs/Teams: He is the lead developer of a Bayesian statistical software package, fostering collaboration in applied statistical research.

Elif AcarView profile

Associate Professor

• High-dimensional dependence
• Statistical genetics
• Nonparametric estimation
• +4 more

Dr. Elif Acar is an Associate Professor in the Department of Statistics at the University of Guelph. Her research focuses on high-dimensional data analysis, multivariate modeling strategies, and meta-analysis in biomedical applications. She holds a PhD in Statistics from the University of Toronto (2010), an MSc in Mathematics from the University of New Hampshire (2006), and a BSc in Statistics from Middle East Technical University (2003). Her research themes include high-dimensional dependence modeling and research synthesis in high-throughput studies, with applications in genome-wide association studies, microbiome research, and phenotyping experiments. She emphasizes methodological developments in handling complex data structures such as longitudinal and incomplete datasets. Dr. Acar has received multiple awards including the MATRIX-Simons Travel Award (2024) and NSERC Discovery Grants (2020–2027). Her recent publications span copula-based models, longitudinal data analysis, and interdisciplinary applications in genetics and neuroscience. She mentors postdoctoral fellows and accepts PhD/MSc students interested in her research areas. Current grants include NSERC funding for statistical method development and collaborative research initiatives.

Vilda PurutçuoğluView profile

Professor

• Bioinformatics
• Systems Biology
• High Dimensional Data
• +2 more

Prof. Vilda Purutçuoğlu is a Professor in the Department of Statistics at Middle East Technical University (METU). Her academic career includes roles as Assistant Professor (2010–2012), Associate Professor (2012–2018), and Professor since 2018. She holds a Ph.D. from Lancaster University (2007) and has affiliations with the Department of Biomedical Engineering, Institute of Applied Mathematics, and Informatics Institute at METU. Her research focuses on bioinformatics, systems biology, high-dimensional data analysis, neuroscience, and social statistics . Key professional roles include Head of the Department of Statistics (since 2025), Chair of the Award Fund Committee at the International Biometric Society (since 2022), and Editorial roles in journals like BMC Bioinformatics. She has led projects on topics ranging from family dynamics to outlier detection in biomedical data, funded by TÜBİTAK and METU. Prof. Purutçuoğlu has been recognized with multiple Academic Performance Awards (2011–2022 excluding 2020) and was a finalist for the Sabri Ülker Science Award (2015). She collaborates internationally, including a Global Engagement Fellowship (2024–2025) at University College London and research at Università Della Svizzera Italiana (2024). Her publications span books on operations research and bioinformatics, and she actively supervises projects and students in interdisciplinary areas like biomedical engineering and computational statistics.

• 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.

• Econometrics
• Bayesian Statistics
• Stochastic Volatility
• +3 more

Martina Zaharieva is a Research Fellow at the Erasmus School of Economics, Erasmus University Rotterdam, specializing in the field of Econometrics. Her research focuses on advanced statistical modeling of financial and economic time series, particularly through Bayesian and stochastic volatility frameworks. Her research interests lie primarily in econometric theory and its applications to financial markets. She employs Bayesian semiparametric methods to model multivariate stochastic volatility, contributing to improved understanding of financial risk dynamics and interdependencies in asset returns. Her work bridges theoretical econometrics with practical financial modeling. The available publication highlights a strong emphasis on Bayesian inference, multivariate volatility modeling, and semiparametric techniques in econometrics. Her research contributes to the broader domains of financial econometrics and quantitative risk analysis, demonstrating methodological rigor and empirical relevance. Currently, no scientific awards or honors are listed in the provided information. Martina Zaharieva is actively engaged in research, though there is no mention of student supervision or grant funding in the available text. Her academic contributions are centered on methodological advancements in econometric modeling. She is part of the research network at the Erasmus School of Economics, a leading institution in economics and econometrics, where she collaborates on high-impact research in quantitative economics.

Shamsunnahar YasminView profile

Researcher

• Road Safety Engineering
• Transportation Planning
• Traffic Incident Management
• +3 more

Dr. Shamsunnahar Yasmin is a Senior Research Fellow (Road Safety Engineer) at the Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Queensland University of Technology (QUT). She holds a PhD in Civil Engineering (Transportation) from McGill University, an M.Sc. from the University of Calgary, and a B.Sc. from Bangladesh University of Engineering and Technology (BUET). Her research focuses on road safety, travel behavior, transportation planning, and advanced econometric modeling, addressing issues such as crash severity analysis, real-time crash risk assessment, and road user behavior. Education: PhD in Civil Engineering (Transportation, McGill University), M.Sc. (University of Calgary), B.Sc. (BUET). Professional memberships include the Editorial Advisory Board of the Journal of Analytic Methods in Accident Research and roles in TRB committees. She has supervised multiple PhD students and led projects like the 'Novel Real-Time Risk Assessment System for Vulnerable Road Users' funded by Australian Competitive Grants. Research Interests: Road safety engineering, travel behavior modeling, sustainable urban transportation, and application of advanced statistical methods (e.g., copula-based models, latent segmentation approaches) to address complex safety challenges. Notable projects include analyzing crash injury severity and evaluating road safety policies. Publications: Over 20 peer-reviewed articles in journals like Accident Analysis & Prevention and Transportation Research Part A, focusing on econometric frameworks, crash risk analysis, and transportation policy impacts.

Sven OttoView profile

Assistant Professor

• Time Series Analysis
• Functional Data Analysis
• Econometric Theory
• +3 more

Dr. Sven Otto is an Assistant Professor of Econometrics at the University of Cologne, Faculty of Management, Economics and Social Sciences, where he has been employed since 2023. His academic work focuses on advanced econometric methods, particularly in time series analysis and functional data. He is affiliated with the Institute of Econometrics and Statistics at the university. His educational background includes: 2019: Dr. rer. pol., University of Cologne 2014: MSc in Economics, University of Bonn 2012: BSc in Mathematics and Economics, University of Bonn Dr. Otto's research centers on developing and applying sophisticated econometric techniques to analyze complex time-dependent data. His work bridges theoretical econometrics with practical applications, particularly in financial and economic time series analysis. He has made significant contributions to the understanding of structural breaks, unit root testing, and functional data models. His research has important implications for economic forecasting, financial risk assessment, and policy evaluation, especially in volatile periods such as the COVID-19 pandemic. Otto collaborates with researchers across institutions, including Jörg Breitung at the University of Cologne and Florian Stark. Dr. Otto's recent publications demonstrate a clear trajectory toward increasingly sophisticated modeling of time-dependent phenomena, with a growing emphasis on functional data approaches and real-time monitoring techniques. His work shows a progression from theoretical foundations to practical applications, particularly in economic and financial contexts. Current research projects include: Approximate Factor Models for Functional Time Series (with Narazii Salish) Functional Factor Regression with an Application to Electricity Price Curve Modeling (with Luis Winter) Combining Concurrent and Historical Functional Linear Regression (with Alois Kneip and Dominik Liebl) DFG Project No. 511905296 (2024-2026): Modeling Functional Time Series with Dynamic Factor Structures and Points of Impact

• Biostatistics
• Medical Biometrics
• Epidemiology
• +3 more

Prof. Dr. rer. nat. Annika Hoyer is a leading Professor in Biostatistics and Medical Biometrics at the Medical Faculty OWL of the University of Bielefeld. She serves as a member of multiple critical committees including the Faculty Conference, Doctoral Committee, and Research Commission, and contributes to the Center for Statistics . Her work integrates advanced statistical methodologies with public health applications. Academic affiliation: University of Bielefeld Primary role: Professor for Biostatistics and Medical Biometrics Committee memberships: Faculty Conference, Habilitation Committee (Deputy), Doctoral Committee, APL Committee (Deputy), Research Commission Research Interests focus on: Methodological developments in meta-analysis and ROC curve modeling Diabetes epidemiology with emphasis on incidence trends, mortality projections, and socioeconomic factors Chronic disease modeling using illness-death models and copula-based approaches Clinical trial statistics and medical data synthesis Public health surveillance for early-onset cancer and long-term care demand Publications highlight methodological innovations in time-to-event analysis , diagnostic accuracy studies , and diabetes burden estimation . Her collaborative work spans neurosurgery , rheumatology , and pediatric emergency care . Key Projects include ongoing methodological-epidemiological research on type 2 diabetes dynamics (funded by private third-party support until 2025) and statistical models for chronic disease surveillance in low-resource settings.

• Financial Econometrics
• Time Series Analysis
• Volatility Modeling
• +6 more

Walter Distaso is a Professor of Financial Econometrics at Imperial College Business School, affiliated with the CFM-Imperial Institute of Quantitative Finance. He joined in 2006 after positions at the University of Exeter and Queen Mary, University of London, and has served as a visiting professor at the IMF. His research spans financial volatility modeling in continuous-time frameworks, long-memory macroeconomic analysis, copula-based multivariate financial time series, and market microstructure noise. He also explores interdisciplinary topics like diabetes and metabolic regulation through collaborations. Educations: PhD in Economics from the University of York Research interests include estimating financial volatility, macroeconomic determinants of stock-market volatility, and evaluating trading strategies. His recent work integrates econometric methods with health economics, addressing pandemic impacts on mental health and diabetes management. He has contributed to policy-relevant studies, such as analyzing Italy's economic growth linked to political debates, and financial misconduct around market fixes. Publications span risk management, volatility forecasting, and biomedical applications. His work balances theoretical econometric innovation with practical applications in finance and health. Awards: None explicitly mentioned. Grants/Advising: No grants listed; no student advisees documented. Labs/Teams: Affiliated with CFM-Imperial Institute but no specific lab/team details provided.

• Statistical Methods
• Machine Learning
• Materials Science
• +5 more

Matthias Neumann is an Assistant Professor at the Institute of Statistics, Graz University of Technology . He completed his PhD in 2020 at Ulm University under Prof. Volker Schmidt, earning the PhD prize of Ulm University . His research focuses on stochastic 3D modeling and statistical analysis of micro- and nanostructures for functional materials, including battery electrodes , fuel cells , and paper-based materials . He has received start-up funding from ProTrainU (2020-2022) and served as principal investigator in the POLiS Cluster of Excellence (2022-2023). Research Interests: His work integrates mathematical morphology , machine learning , and spatial statistics to develop methods for microstructure quantification , estimation of geometrical descriptors (e.g., tortuosity, constrictivity), and data-driven models linking morphology to effective physical properties . He utilizes random fields , point processes , and copulas for virtual microstructure generation and parameter estimation. Teaching: He lectures on Applied Statistics , Statistical Modeling , and Mathematical Statistics at Graz University of Technology, with prior teaching experience at Ulm University in Multivariate Stochastic Modeling , Point Processes , and Spatial Statistics . Scientific Achievements: PhD prize of Ulm University (2020) ProTrainU start-up funding (2020-2022) POLiS Cluster of Excellence grant (2022-2023) Publications: His 15 most recent articles (2023-2025) emphasize machine learning techniques for microstructure segmentation , stochastic 3D modeling of nanoporous materials , and data-driven quantification of transport-property relationships . Key topics include random forests , neural networks , and R-vine copulas applied to fuel cells , sodium-ion batteries , and polymer electrolytes .

Lea PetrellaView profile

Professor

• Quantile regression
• Graphical models
• Random Forest
• +3 more

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

• Dependence modeling (copulas)
• Credit risk
• Risk management
• +2 more

Jean-David FERMANIAN is a Full Professor of Finance and Statistics at ENSAE Paris (Institut Polytechnique de Paris) and a permanent researcher at CREST . He holds a PhD in Statistics from Université Paris 6 and graduated from École Normale Supérieure and ENSAE. Research Interests: His work focuses on dependence modeling (copulas), credit risk, risk management, multivariate dynamic models, and machine learning applications in finance/insurance. He previously held positions at INSEE and in the financial industry. Professional Background: Combines academic expertise with industry experience in statistical analysis and financial risk modeling. Actively contributes to CREST's research initiatives in quantitative finance.

• Risk Analysis
• Actuarial Science
• Insurance Economics
• +3 more

Montserrat Guillén Estany is a Professor of Quantitative Methods for Economics and Business at the University of Barcelona's Faculty of Economics and Business since 2001. She serves as Vice-Secretary of the Governing Board at the Royal Academy of Economic and Financial Sciences (RACEF) and directs the consolidated Risk in Finance and Insurance research center (UBriskcenter), which comprises 25 permanent researchers and 25 international affiliates. She also holds an Honorary Visiting Professorship at City, University of London. Bachelor of Science in Mathematics (Mathematical Statistics), University of Barcelona (1987) PhD in Economics and Business Administration, University of Barcelona (1992) Master's in Data Analysis, University of Essex, UK Visiting researcher: University of Texas at Austin, University of Paris II, University of California-Berkeley, University of Cambridge Her research focuses on risk analysis and actuarial science , particularly developing statistical methods for insurance applications. She pioneered work in operational risk modeling and quantitative approaches to insurance economics, establishing herself as the top-cited woman in Actuarial Sciences and second in Insurance. Her work bridges theoretical statistics with practical insurance industry applications, emphasizing computational methods and real-world data analysis. The 15 most recent publications demonstrate consistent leadership in actuarial methodology innovation , with increasing integration of machine learning techniques since 2020. Her research spans cyber risk, climate risk, longevity modeling, and regulatory compliance, maintaining strong connections to industry needs while advancing theoretical frameworks. Recent work shows particular emphasis on non-traditional data sources and computational approaches to complex risk scenarios. ICREA Academia distinction (2012, 2019) - only social sciences researcher to receive twice Casualty Actuarial Society honor (USA) Julio Castelo International Insurance Prize Honorary member, College of Actuaries of Catalonia Professor Guillén has supervised 17 doctoral theses at the University of Barcelona and co-supervised two internationally. She serves on numerous doctoral committees globally and participates in professor selection committees across Europe and North America. Her research has secured funding from Mapfre Foundation, BBVA Foundation, European Commission H2020, and other major institutions. She leads the UB-Zurich Chair of Insurance and UB-Mutualidad de la Abogacía Chair, focusing on actuarial innovation and aging economics research. As director of UBriskcenter, she leads a multidisciplinary team of 50 researchers conducting cutting-edge work in financial and insurance risk. The center maintains strong industry partnerships and international academic collaborations, particularly through the European Group of Risk and Insurance Economists which she previously presided over.

• Financial Econometrics
• Financial Risk Measurement
• Investment Portfolio Management
• +10 more

Angel Manuel León Valle is a Professor in the Department of Fundamentals of Economic Analysis at the Faculty of Economic and Business Sciences, University of Alicante. He has been a faculty member since 2000 and is actively involved in teaching, research, and academic administration. He has taught a wide range of courses including Mathematics, Econometrics, and Financial Management across various undergraduate and dual-degree programs. PhD in Business Administration and Management, University of Alicante (1998) Master's in Economics and Finance, CEMFI Graduate in Economics and Business Administration, University of Alicante (1991) His research focuses on financial econometrics, particularly on financial risk measurement, investment portfolio management, and valuation of financial and real derivatives. He has made significant contributions to modeling asset returns using semi-nonparametric distributions, copulas, and higher-order moments such as skewness and kurtosis. His work bridges theoretical econometric modeling with practical financial risk applications. The recent articles highlight a consistent research trajectory in financial risk modeling, with a focus on improving Value-at-Risk and Expected Shortfall estimation, analyzing energy market risks, and extending classical models like the Solow growth model to contemporary crises. His publications appear in top journals including the Journal of Banking and Finance and Journal of Empirical Finance, reflecting a strong empirical and methodological orientation. He has been awarded multiple competitive public research grants as principal investigator, funded by the Ministry of Science and Innovation and the Valencian Government. His leadership in research projects underscores his role as a key figure in quantitative finance and econometrics in Spain. Directed or co-directed 12 doctoral theses Supervised 8 master’s or final degree projects in the last five years He has coordinated several research groups and projects, including the Macroeconomics and Financial Econometrics (MacroMetrics) group. His ongoing projects extend into 2029, indicating sustained research activity and institutional support.

Aaron TenenbeinView profile

Professor

• Statistics
• Actuarial Science
• Risk Management
• +6 more

Aaron Tenenbein is a Professor of Statistics and Actuarial Science at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 1969. He serves as the Faculty Director of the Executive MBA Program and Director of the Actuarial Science Program. His academic home is in the Department of Statistics and Operations Research. Ph.D. in Statistics, Harvard University, 1969 M.A. in Statistics, Harvard University, 1967 B.S. in Mathematics & Actuarial Science, University of Manitoba, 1965 Professor Tenenbein's research spans statistics, actuarial science, risk management, and financial modeling. His work emphasizes sampling techniques , simulation methodology , mortality estimation , and statistical applications in insurance and finance . He has made significant contributions to the modeling of select and ultimate mortality, bivariate distributions, and multivariate categorical data analysis with misclassification. His publications appear in leading journals such as the Journal of the American Statistical Association , Transactions of the Society of Actuaries , and Journal of Applied Probability . The recent articles reflect a strong trend toward integrating statistical theory with practical actuarial and financial problems, particularly in risk assessment, mortality forecasting, and data reliability. Scientific awards and honors include: New York University Distinguished Teaching Medal Stern Excellence in Teaching Award Stern Executive MBA Award for Excellence in Teaching Stern Undergraduate Professor of the Year Fellow of the American Statistical Association Associate of the Society of Actuaries Member of the American Academy of Actuaries Professor Tenenbein has served as a consultant to major financial and insurance institutions, including J.P. Morgan, IBM, KPMG, and Metropolitan Life. He has provided expert witness testimony in legal cases involving age discrimination, television ratings, and software sales forecasting. He has also authored a problem handbook and a case study on sales forecasting. While specific advisees are not listed, his leadership in graduate programs indicates extensive student mentorship. He has not been associated with a named lab or research team, but his work is deeply integrated into the academic and professional practice of actuarial science at NYU Stern.