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Taehan BaeView profile

Associate Professor

• Actuarial Science
• Risk Management
• Mathematical Finance

Taehan Bae is an Associate Professor in the Department of Mathematics and Statistics at the University of Regina's Faculty of Science. His research focuses on actuarial science, risk management, and mathematical finance, with applications in insurance modeling and environmental data analysis. He teaches advanced courses such as STAT 858: Statistical Modeling of Dependence and Extremes. Research interests emphasize statistical methods for loss modeling, heavy-tailed data analysis, and algorithm development for actuarial applications. His work bridges theoretical statistics with practical challenges in insurance and environmental risk assessment. Recent publications (2022–2024) highlight contributions to crack distribution extensions, precipitation modeling in Canadian Prairies, and robust iteration algorithms for loss reserving. These studies reflect a focus on combining probabilistic methodologies with real-world risk quantification. No scientific awards are listed, though his active publication record indicates sustained academic engagement. Advising and grant activities are not explicitly detailed in the provided information. No specific laboratories or research teams are mentioned in the profile.

George TzougasView profile

Associate Professor

• Actuarial Science
• Applied Statistics
• Computational Statistics
• +5 more

George Tzougas is an Associate Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University, Edinburgh. He serves as Academic Director of the Scottish Financial Risk Academy and Academic Chair of the IFoA’s Data Science Research working party on Statistical Learning in Actuarial Applications. Previously, he held a tenured position at the London School of Economics (LSE), where he was Programme Director for the BSc in Actuarial Science and a member of the Royal Statistical Society. His research focuses on applying statistical learning and computational methods to non-life insurance, finance, and climate change impact analysis. Key areas include insurance ratemaking, optimal reinsurance, green bond pricing, and climate-related risk modeling. He has published extensively on topics such as multivariate claim count models, machine learning for carbon price forecasting, and zero-inflated regression techniques. Education: Tenured position at LSE Department of Statistics (previously Programme Director for BSc Actuarial Science) Lecturer at University College Cork, Ireland Notable awards include multiple Excellence in Education Awards from LSE (2017–2020) and the Best Class Teacher Award (2017). His work contributes to UN Sustainable Development Goals related to climate action and sustainable finance. He advises on PhD projects involving climate change hazards in insurance and has taught courses such as Time Series and Machine Learning. Research collaborations span institutions globally, with a focus on interdisciplinary applications of actuarial science and data science.

• Nonparametric Statistics
• Bootstrap Methods
• Smoothing Methods
• +5 more

Alan M. Polansky is an Associate Professor in the Department of Statistics and Actuarial Science at Northern Illinois University (NIU), where he has maintained a continuous faculty appointment since 1995. His research program centers on advancing nonparametric statistical methodologies with significant contributions to bootstrap theory, smoothing techniques, and observed confidence levels. His academic credentials include a Ph.D. from Southern Methodist University and M.S./B.S. degrees from the University of Texas at San Antonio. As an Honors Faculty Fellow (2022-2023), he developed and taught an innovative seminar on Data and Social Justice for the University Honors Program. Polansky's research spans several interconnected domains: Foundational work on bootstrap methodology and confidence interval construction Development of observed confidence levels as an alternative to multiple comparison techniques Asymptotic theory for statistical limit theorems Emerging research on network data analysis and Bayesian inference for stochastic processes His 25+ year publication record shows consistent evolution from core nonparametric methods toward contemporary applications, with recent work focusing on network statistics and Bayesian approaches while maintaining theoretical rigor. Publications appear in premier journals including the Journal of the Royal Statistical Society, Technometrics, and Computational Statistics and Data Analysis. Key recognitions include: Honors Faculty Fellowship (2022-2023) Authorship of two influential monographs: 'Observed Confidence Levels' (2007) and 'Introduction to Statistical Limit Theory' (2011) As an educator, Professor Polansky maintains regular office hours in DuSable Hall and has advised students across statistics and actuarial science programs. His research demonstrates sustained methodological innovation with practical applications in insurance, manufacturing, and emerging network-based domains, reflecting both deep theoretical expertise and commitment to real-world problem solving.