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Filip LindskogView profile

Professor

• Actuarial Mathematics
• Insurance Mathematics
• Financial Mathematics
• +5 more

Filip Lindskog is a Professor of Insurance Mathematics at Stockholm University (SU) , where he heads the Mathematical Statistics division within the Department of Mathematics . With a background in financial mathematics and actuarial science, his research focuses on quantitative risk management, non-life insurance pricing, and applications of biostochastics and biostatistics. He has co-authored the textbook Risk and Portfolio Analysis: Principles and Methods (Springer, 2012) and supervises PhD students in actuarial mathematics. Education \n \n MSc in Engineering Physics, KTH Royal Institute of Technology (2000) \n PhD in Mathematical Statistics, ETH Zürich (2004) \n Research Interests Filip's work spans actuarial mathematics , financial risk modeling , and insurance analytics . He investigates stochastic processes in regime-switching environments, capital requirements for insurers, and mathematical frameworks for claims reserving. His recent publications emphasize machine learning applications in risk adjustment, asymptotic analysis of Poisson models, and regulatory compliance under IFRS 17.\n Scientific Contributions \n \n Editor, Scandinavian Actuarial Journal (2018–present) \n Director of SU's Master's Program in Actuarial Mathematics (2016–present) \n Head of SU's Mathematical Statistics Division (2018–present) \n \n Students and Collaborations Current and former PhD students include Nils Engler , Lina Palmborg , Jonas Alm , and Johan Nykvist . Former postdocs include Julie Thøgersen , Abhishek Pal Majumder , and Kristoffer Lindensjö . His research group explores discrete random structures, financial applications of biostatistics, and insurance modeling under capacity constraints.\n

Ekaterina KazakView profile

Associate Professor

• Financial Econometrics
• Statistical Learning
• Econometrics
• +2 more

Dr. Ekaterina Kazak is an Associate Professor in Econometrics at the Department of Economics , University of Birmingham . She joined the department in September 2024 after serving as a Lecturer in Econometrics at the University of Manchester. PhD in Econometrics (2019) and MSc in Econometrics (2015) from the University of Konstanz BSc in Economics (2013) from Lomonosov Moscow State University Research Interests: Financial Econometrics: Portfolio choice, Value-at-Risk, Expected Shortfall Statistical Learning: Bagging, decision trees Econometrics: Robust inference, bootstrap, pretesting, forecast evaluation Teaching: Applied Econometrics and Policy Analysis (compulsory MSc Economics course) Advanced Quantitative Methods for Data Analytics (elective MSc Money, Banking and Finance course)

Henryk ZähleView profile

Professor

• Stochastics
• Quantitative Risk Management
• Statistical Robustness
• +4 more

Henryk Zähle is a Full Professor of Stochastics at Saarland University's Department of Mathematics, where he has held a W3 position since 2014. He previously served as a W2 Professor (2013-2014) and W1 Junior Professor (2010-2012) at Saarland, and earlier at TU Dortmund University (2007-2010). He earned his Ph.D. in Mathematics from Technical University Berlin (2004) and a Diploma in Mathematics from University of Göttingen (2000). His research focuses on statistical robustness of risk measures asymptotic theory for empirical processes quantitative risk management Markov decision models insurance and financial mathematics with methodological contributions to bootstrapping, quasi-Hadamard differentiability, and sensitivity analysis. Article trends show sustained engagement with stochastic process theory nonparametric estimation robust statistical functionals applications to insurance and finance asymptotic error distributions time series analysis spanning both theoretical and applied domains. Scientific awards include Marie Curie Fellowship (University of Warwick, 2001) DFG Fellowship (2000-2003) He has supervised numerous Ph.D., Master's, and Bachelor's theses on topics like risk measure asymptotics empirical process convergence copula robustness Markov decision sensitivity nonparametric risk estimation statistical bootstrap methods and serves as Associate Editor for Metrika .

Jörg BreitungView profile

Professor

• Econometrics
• Statistics
• Panel Data Analysis
• +3 more

Jörg Breitung is a Professor of Econometrics and Statistics at the Institute of Econometrics and Statistics within the Faculty of Management, Economics and Social Sciences (WiSo Faculty) at the University of Cologne since 2014. He also serves as a Research Professor of the German Bundesbank in Frankfurt since 2002. Research Focus: Panel Data Analysis Time Series Analysis Forecasting Financial Econometrics Scientific Contributions: Developed advanced GMM estimators for spatial regression models Innovative approaches for assessing causality in frequency domains Created robust tests for slope homogeneity in panel data Pioneered methods for serial correlation testing in fixed effects models Contributed to nonlinear panel data modeling and bootstrap techniques Honors and Editorial Roles: Associate Editor of International Journal of Forecasting (2019-) Associate Editor of Journal of Business and Economic Statistics (2017-) Associate Editor of Econometric Reviews (2014-) Contributed to leading journals like Econometrica and Journal of Econometrics

• Time Series Analysis
• Applied Statistics
• Biostatistics
• +4 more

Ying Zhang is a Professor in the Department of Mathematics and Statistics at Acadia University, maintaining an office in Huggins Science Hall, Room 151. She earned her BSc from Shandong Normal University and advanced degrees (MA, MSc, PhD) from Western University, complemented by P.Stat. certification (Certificate #78) from the Statistical Society of Canada. Her educational background includes: BSc from Shandong Normal University MA, MSc, PhD from Western University Professor Zhang's research centers on Time Series Analysis and Applied Statistics , extending to Statistical Computing, Symbolic Algebra Computing, and Statistical Consulting in Biostatistics, Survey Design, and Research Methodology. Her work demonstrates significant applications in environmental science (water resources trend analysis), health sciences (drug safety and utilization studies), and ecological modeling (wildlife population dynamics), with methodological innovations in nonparametric testing and hierarchical modeling. Analysis of her 2013-2018 publications reveals a dominant focus on developing novel time series methodologies for environmental and health contexts, particularly seasonal trend detection, medication utilization patterns, and ecological data analysis. Her work consistently bridges theoretical statistics with practical applications across disciplines. She actively contributes through the Statistical Consulting Centre and the CANSSI Maritime Statistical and Health Sciences Collaborating Centre , holding P.Stat. designation from the Statistical Society of Canada. While her collaborative publications indicate interdisciplinary engagement, specific details of grant funding and student advising are not documented in available sources.

• Survey Statistics
• Small Area Estimation
• Non-probability Sampling
• +3 more

Dr. Andrius Čiginas is a Senior Researcher at the Interdisciplinary Statistical Research Group , part of the Institute of Data Science and Digital Technologies (VU DMSTI) at Vilnius University . His work focuses on advancing survey statistics, small area estimation, and the integration of non-probability samples into official statistics. Research Focus Dr. Čiginas specializes in developing statistical methodologies for improving the accuracy and efficiency of population estimates, especially in small domains. His research includes: Composite estimation techniques Non-probability sample integration Real-time prediction using social media and administrative data Bootstrap and Edgeworth approximations for finite populations Doctoral Supervision He currently supervises two doctoral students: Akvilė Vitkauskaitė – Informatics (2024–2028): Parameter estimation and forecasting in population domains using non-probability samples. Ieva Burakauskaitė – Mathematics (2022–2026): Use of additional information in estimating parameters in population domains. Scientific Contributions Dr. Čiginas has published extensively in top-tier journals such as Journal of Official Statistics , Statistics , and Nonlinear Analysis: Modelling and Control . His recent work explores the integration of non-probability data sources into official statistics, consumer confidence estimation, and small area estimation techniques.

• Statistics
• Biostatistics
• Survival Analysis
• +3 more

Takumi Saegusa is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, specializing in Statistics and Biostatistics. His work bridges theoretical and applied statistical methods. Rank: Associate Professor Department: Department of Mathematics University: University of Maryland, College Park His research focuses on advanced statistical techniques for survival analysis, two-phase sampling, and data integration. He develops methods to handle longitudinal covariates, heterogeneous populations, and merged datasets from multiple sources. Recent publications address confidence bands for survival curves, variable selection in threshold regression, and computational approaches for statistical packages like osDesign and tpsDesign . These works span biostatistical applications, computational methods, and theoretical foundations. He contributes to statistical software development, including R packages for two-phase study design and analysis. His work has applications in epidemiology, clinical research, and precision medicine.

• Changepoint Detection
• High-dimensional Time Series
• Statistical Inference
• +3 more

Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.

• Genetics
• Statistics
• Psychology
• +6 more

Stéphanie M. van den Berg is an Associate Professor at the University of Twente , affiliated with the Digital Society Institute and TechMed Centre . Her research spans interdisciplinary domains including Psychology , Genetics , Statistics , and Artificial Intelligence , with a focus on educational achievement, mental health assessment, and data-driven methodologies. Key Themes : Heritability analysis, machine learning applications, educational technology, longitudinal data modeling, and personalized medicine. Recent Work : In 2025, she investigated measurement invariance in child behavior phenotypes, while 2024 saw her contribute to personalized medicine through intensive longitudinal data. Methodologies : Expertise in item response theory, text mining, and statistical model selection, as demonstrated in her 2017 publications. She actively collaborates across disciplines, with recent work involving biomedical informatics and behavioral genetics. Her research outputs (97 total) reflect a sustained focus on integrating data science with psychological and health-related applications.

• Statistical Network Analysis
• Non-parametric Statistics
• Causal Inference
• +7 more

Dr. Alexander Fuchs-Kreiß is a Junior Professor for Statistics at the Institute of Mathematics, Faculty of Mathematics and Computer Science at Leipzig University. He joined the university in April 2022 as a member of the Stochastics research group. He is also a scientific member of the International Max Planck Research School for Mathematics in the Sciences and affiliated with the HKMetrics Network. His research focuses on the statistical analysis of network data, particularly semi- and non-parametric methods for dynamic interaction networks. He has developed methodologies for relational event models using counting processes like Hawkes processes, and has contributed to causal inference, high-dimensional statistics, and quantile regression. His work often bridges multiple statistical areas, creating innovative approaches to complex data structures. His recent publications show a strong trend toward developing statistical methods for network data with applications in economics, epidemiology, and social sciences. His research combines theoretical developments with practical implementations, as evidenced by the R packages accompanying his publications. As an educator, he teaches Mathematical Statistics, Statistical Network Analysis, and Causal Inference at Leipzig University. He supervises PhD students working on network models and advises diploma theses in mathematics and business mathematics. Dr. Fuchs-Kreiß maintains active research collaborations with institutions including Heidelberg University, UC Davis, KU Leuven, and LSE. He co-organizes academic seminars and participates in international conferences on statistical learning and probability.

• Nonparametric predictive inference
• Nonparametric statistics
• Modeling dependence
• +3 more

Professor Tahani Coolen-Maturi is a faculty member in the Department of Mathematical Sciences at Durham University and Fellow of the Durham Research Methods Centre. Her research focuses on advanced statistical methodologies with applications across multiple domains. Her primary research interests include: Nonparametric predictive inference and statistics Reliability and survival analysis using survival signatures Diagnostic accuracy and ROC analysis Modeling dependence structures Uncertainty quantification through imprecise probability Statistical reproducibility frameworks Analysis of her recent publications reveals a strong trend toward statistical reproducibility research, particularly in hypothesis testing and educational trial methodologies. Her work bridges theoretical statistics with practical applications in reliability engineering, medical diagnostics, and educational assessment. A significant portion of her recent output develops nonparametric predictive inference frameworks for complex systems and censored data. Professor Coolen-Maturi actively supervises doctoral students, with ten current supervisees including Azza Alzahrani, Fatemah Alammar, and Hadeer Ghonem. Her collaborative research includes major educational trials funded by the Education Endowment Foundation, focusing on statistical power calculations and meta-analysis of intervention effectiveness.

Yeonwoo RhoView profile

Associate Professor

• Time Series Analysis
• Econometrics
• Bootstrap/Resampling
• +4 more

Dr. Yeonwoo Rho is an Associate Professor in the Department of Mathematical Sciences at Michigan Technological University. His academic credentials include a PhD in Statistics from University of Illinois at Urbana-Champaign, an MS in Statistics from Seoul National University, and a BS/BA in Mathematics/Economics from the same institution. His research focuses on advanced statistical methodologies including: Time series modeling and econometrics Bootstrap/resampling techniques for inference Unit root testing for non-stationary data Spatio-temporal analysis of complex systems Statistical methods for p-value combination Modeling of heavy-tailed distributions Office: Fisher Hall 226C | Email: yrho@mtu.edu | Phone: 906-487-2220

Jesper Lykke JacobsenView profile

Professor

• Statistical Physics
• Disordered Systems
• Conformal Field Theory
• +6 more

Jesper Lykke Jacobsen is a Professor of Theoretical Physics at the Laboratoire de Physique Théorique at École Normale Supérieure in Paris, with additional affiliation at Université Pierre et Marie Curie (now part of Sorbonne University). He is also a member of the prestigious Institut Universitaire de France, which recognizes outstanding researchers in France. His research focuses on statistical physics, particularly disordered systems, conformal field theory, and exactly solvable models. His work spans critical phenomena, phase transitions, loop models, and the Potts model, with significant contributions to our understanding of two-dimensional systems. He has made important advances in understanding the critical behavior of the Potts model with random couplings and the statistical properties of compact polymers. Analysis of his recent publications reveals a strong focus on critical loop models, conformal field theory applications, and exactly solvable systems. His work often bridges mathematical physics with condensed matter theory, exploring the deep connections between symmetry groups, critical exponents, and universal behavior in statistical systems. Many of his papers involve collaborations with leading researchers in the field, demonstrating his central role in the theoretical physics community. Institut Universitaire de France membership (prestigious research distinction) Professor Jacobsen has advised numerous PhD students, including Romain (thesis defense 2013) and Etienne (thesis defense 2019), as evidenced by group photos from their dissertation defenses. He has organized and participated in significant research conferences and workshops, such as those at Les Houches on exact methods in low-dimensional statistical physics and conformal field theory applications. His research has been supported by various academic institutions and likely research grants, though specific grant details aren't provided in the source material. He leads or participates in research teams focused on statistical physics, as shown by his regular seminars and the group photos featuring multiple researchers working in related areas. His informal statistical physics seminar series provides a venue for ongoing research discussion and collaboration.

• Markov processes
• Ruin theory
• Extreme value theory
• +5 more

Romain Biard is a Senior Lecturer in Mathematics at the University of Franche-Comté, holding dual research affiliations with the Besançon Mathematics Laboratory (UMR 6623, LMB) and the Research Center on Economic Strategies (UR 3190, CRESE). His career bridges theoretical mathematics with practical applications in economics and healthcare systems. His primary research interests span Markov processes, Ruin theory, Extreme value theory, Optimal allocations, and Game theory. These areas converge in his work on modeling complex systems with random elements, particularly focusing on risk assessment and resource allocation problems. His research demonstrates how mathematical frameworks can address real-world challenges in healthcare management and economic competition. Analysis of his recent publications reveals a strong trend toward applied research with immediate societal relevance. His 2023-2024 work on healthcare resource allocation (nursing shortages, ventilator availability) applies sophisticated Markov process modeling to critical hospital management problems. Earlier work on economic competition models demonstrates how random entry of firms affects market equilibrium, with specific applications to the taxi industry facing competition from ride-hailing services. Active researcher with publications spanning 2008-2024 Primary collaborator network includes Marc Deschamps, Mostapha Diss, Alexis Roussel, and Stéphane Loisel Publications appear in high-quality journals across mathematics, statistics, economics, and actuarial science Biard's research approach consistently combines rigorous mathematical theory with practical applications, making significant contributions to both theoretical developments and real-world problem solving. His current work suggests continued focus on applying stochastic modeling to healthcare resource challenges and economic competition dynamics.

• stochastic approximation
• reinforcement learning
• sampling
• +4 more

Sergey Vladimirovich Samsonov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he also serves as Head of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within the Institute of Artificial Intelligence and Digital Sciences. He is affiliated with the Department of Big Data and Information Retrieval and the Joint Department of the A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences. Samsonov began his tenure at HSE in 2018 and has accumulated 7 years of scientific and teaching experience. His educational background includes a PhD from HSE (2024) and a Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2017). Samsonov's research focuses on stochastic approximation, reinforcement learning, sampling techniques, Markov chain Monte Carlo (MCMC) methods, and multivariate statistics . His work bridges theoretical mathematics with practical machine learning applications, particularly in developing algorithms with strong theoretical guarantees. He has made significant contributions to understanding convergence properties of stochastic algorithms and developing variance reduction techniques. Analysis of his recent publications reveals a consistent focus on the mathematical foundations of machine learning, particularly in stochastic approximation methods, reinforcement learning theory, and generative modeling. His work often combines rigorous theoretical analysis with practical applications, demonstrating expertise in both pure mathematics and applied machine learning. The publication venues (including top conferences like NeurIPS, ICLR, and AISTATS) reflect the high impact and quality of his research in the machine learning community. Young Scientist Badge (December 2024) Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of gratitude from the Faculty of Computer Science at HSE (September 2021) Rector's personal allowance (2022-2023) Numerous bonuses for high-impact publications (2021-2027) Best Teacher award (2024-2025, 2022, 2020) Segalovich Scientific Prize (2022) National Prize 'Leaders in AI - 2024' Samsonov teaches advanced courses including Markov Chains, Sampling and Generative Modeling, and Matrix Computations. His laboratory work focuses on developing stochastic algorithms for machine learning applications. He has been involved in HSE's collaboration with Sber, which has resulted in 19 successfully implemented AI projects since 2021. His research has gained significant recognition, with multiple papers accepted at top-tier conferences including 12 papers presented at NeurIPS in recent years.