Find Academic Supervisors

• Actuarial Science
• Insurance
• Risk Management
• +3 more

Gee Lee is an Associate Professor in the Department of Statistics & Probability and the Department of Mathematics at Michigan State University. His work bridges actuarial science with advanced statistical and machine learning methodologies, focusing on practical applications for insurance risk modeling. PhD, University of Wisconsin-Madison Associate (ASA), Society of Actuaries His research centers on insurance loss modeling for rate-making and loss reserving, multivariate insurance coverage optimization, dependence structure analysis, and integrating machine learning into actuarial frameworks. Current projects include deep neural networks for claim prediction, unstructured data analysis, and multivariate coverage optimization. Recent publications highlight trends in crop insurance modeling (2025), regularization techniques (2024), multivariate risk retention strategies (2023), textual data analysis (2022), copula regression (2022), and reinsurance game theory (2022). Earlier works explore shrinkage methods, word embeddings, longitudinal claims, healthcare data, and deductible ratemaking. Gee Lee supervises MS and PhD students in actuarial science. Former advisees include Leonard Korreshi (2024), Qiaozhen Qian (2023), and Scott Manski (2020, co-advised). He also supports undergraduate research through MSU’s REU program and directed study courses.

Dimitris KarlisView profile

Professor

• Computational Statistics
• Mixture Models
• EM Algorithm
• +12 more

Dimitris Karlis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), within the School of Information Sciences and Technology. He has been a key academic figure since earning his BSc and PhD in Statistics from AUEB in 1992 and 1999, respectively, and was promoted to Associate Professor in 2012 before advancing to full Professor. Education: BSc in Statistics, AUEB (1992) PhD in Applied Statistics, AUEB (1999) His research spans computational statistics, mixture models, EM algorithms, copulas, multivariate discrete data, and applications in sports, insurance, and seismicity. He has published extensively in top-tier statistical journals such as the Journal of the Royal Statistical Society and Statistics in Medicine . The 15 most recent publications reveal a strong focus on multivariate count data, integer-valued time series, model-based clustering using copulas, and applications in actuarial science and health. His work frequently involves mixture models, Bayesian inference, and innovative extensions of Poisson-based frameworks. Scientific Service and Recognition: Associate Editor: Metron, Communications in Statistics, IMA Journal of Management Mathematics, Stochastic Environmental Research and Risk Assessment Editor: Biometrics Bulletin of IBS Member: American Statistical Society, International Statistical Institute, International Association of Statistical Computing, Hellenic Statistical Institute Publicity Officer: Eastern Mediterranean Region, International Biometric Society Advising and Grants: He has supervised 4 completed PhDs and 18 Master’s theses, with several more in progress. He has led and participated in research projects funded by the European Union and EUROSTAT, particularly in official statistics. His advising spans methodological and applied topics in statistics. Labs and Teams: While no formal lab is named, he collaborates extensively with researchers in actuarial science, transportation, biostatistics, and environmental risk, often through joint projects and publications.

• Actuarial Science
• Insurance Loss Modeling
• Rate-Making
• +6 more

Gee Y. Lee is an Associate Professor with Tenure in the Department of Statistics and Probability and the Department of Mathematics at Michigan State University. Lee holds a PhD from the University of Wisconsin-Madison and is an Associate of the Society of Actuaries (ASA). Their research focuses on applying advanced statistical and machine learning methods to solve complex problems in actuarial science and insurance. Dr. Lee's educational background includes: PhD from the University of Wisconsin-Madison Associate (ASA) designation from the Society of Actuaries Dr. Lee's research spans several critical areas in modern actuarial science. Their primary focus includes insurance loss modeling for rate-making and loss reserving applications, optimization of multivariate insurance coverage, and dependence modeling. A significant portion of their recent work applies machine learning methods, particularly deep neural networks, to traditional actuarial problems. They are also pioneering research in analyzing unstructured data for insurance applications, which represents an emerging frontier in the field. Their work bridges theoretical statistical methods with practical insurance industry needs. Dr. Lee's publication record demonstrates a clear evolution from traditional actuarial methods toward more sophisticated and interdisciplinary approaches. Early work focused on fundamental aspects of insurance pricing and modeling, while more recent publications incorporate machine learning techniques, natural language processing, and advanced optimization methods. A notable trend is the increasing integration of unstructured data analysis into actuarial science, reflecting broader industry shifts. Their research consistently addresses both theoretical advancements and practical applications in insurance risk assessment and management. While specific awards aren't detailed in the available information, Dr. Lee's recognition includes: Associate (ASA) designation from the Society of Actuaries Michigan State University recognized by the Society of Actuaries as granting MS and PhD degrees focused on actuarial science (as of 2023) Dr. Lee actively mentors students at multiple levels, supervising undergraduate research through REU programs, directed studies (STT 490, MTH 490, MTH 491B), and graduate research for MS and PhD candidates. They have advised numerous students who have presented at UURAF (Undergraduate Research Assistant Fellowship) conferences. For graduate students, Dr. Lee supports research leading to MS degrees in Statistics, Applied Statistics, and Industrial Mathematics with actuarial science focus, as well as PhD dissertations in Statistics. Beyond direct student supervision, Dr. Lee has organized significant academic events including the Simon Conference for Young Researchers in Risk Management and Insurance (2019, 2023) and contributed to other workshops, demonstrating leadership in the actuarial research community. While specific lab names aren't mentioned, Dr. Lee appears to lead a research group focused on actuarial science and insurance analytics at Michigan State University. Their collaborative work with researchers like Scott Manski, Taps Maiti, Peng Shi, and others suggests an active research team working at the intersection of statistics, machine learning, and actuarial applications. The research group seems particularly focused on bridging traditional actuarial methods with modern data science techniques.

Andrei L. BadescuView profile

Professor

• Risk and Ruin Theory
• Matrix Analytic Methods
• Stochastic Claim Reserving
• +6 more

Andrei L. Badescu is a Professor of Actuarial Science and Director of the Master of Financial Insurance in the Department of Statistical Sciences at the University of Toronto. His academic leadership spans editorial roles at Insurance: Mathematics and Economics and program direction for graduate actuarial programs. His educational foundation includes: BSc in Mathematics and Economics from Bucharest University of Economic Studies (1998) MSc in Mathematics and Economics from Bucharest University of Economic Studies (2000) PhD in Actuarial Science from Western University (2004) He completed postdoctoral training at the University of Waterloo (2006) before joining the University of Toronto faculty in 2006. Research interests evolved from foundational work in Risk and Ruin Theory using Matrix Analytic Methods to contemporary applications in Stochastic Claim Reserving, Dependence Modelling, and Predictive Analytics. Current emphases include Telematics risk assessment and Insurance Data Science, leveraging advanced statistical techniques for real-world insurance challenges. Recent publications (2021-2025) reveal a strategic shift toward data-driven insurance solutions. Key trends include micro-level claim reserving via inverse probability weighting, telematics-based driving risk modeling using unsupervised learning, and mixture-of-experts frameworks for portfolio ratemaking. These works bridge traditional actuarial science with machine learning, particularly in handling censored data and operational risk. Professor Badescu mentors five doctoral students (Spark Tseung, Sebastian Calcetero, Ian Weng, Sophia Chan, Hassan Abdelrahman) and two master's students (Kaihua Sun, Yifeng Ge). His administrative leadership includes directing the Master of Financial Insurance program and previously overseeing the Data Science concentration in the Master of Applied Computing. His research group develops practical tools like the LRMoE.jl software package for actuarial loss modeling, while future work targets telematics integration and insurance-specific artificial intelligence applications.

• Risk Management
• Actuarial Statistics
• Financial Econometrics
• +7 more

Professor Jens Perch Nielsen is a Professor of Actuarial Science at Bayes Business School, City St George's, University of London. With a background as an actuary from Copenhagen and a statistician from UC-Berkeley, he has extensive experience in both academia and industry, having served as research director of RSA and worked as an entrepreneur. His educational background includes: PhD, University of California, Berkeley, United States (1988-1990) Actuary, University of Copenhagen, Denmark (1982-1988) Professor Nielsen's research focuses on three main areas: In-Sample Forecasting, Defined benefit advantages adapted to defined contribution products, and Asbestos mortality forecasting. His work spans risk management, actuarial statistics, financial econometrics, and simulation methods, with applications in insurance, pension funds, and broader financial markets. He has made significant contributions to statistical methodology in actuarial science, particularly in developing new approaches to forecasting and risk modeling. His recent publications demonstrate a strong trend toward machine learning applications in forecasting stock returns and insurance risk, with particular emphasis on telematics data in motor insurance. This reflects his ability to bridge theoretical statistics with practical industry applications. Professor Nielsen has received several professional recognitions: Honorary Professor (Adjungated professor) from University of Copenhagen (2006) Associated member, Institute of Actuaries, London (since 2014) Fellow, Royal Society of Statistics, London (since 2013) Full qualified member and former board member, Danish Institute of Actuarial Science (since 1993) As an advisor, Professor Nielsen has supervised numerous PhD students including Stephan Bischofberger, Peter Vodicka, and Parastoo Mousavi. His research has been supported by various grants related to actuarial science, risk modeling, and statistical methodology, with applications spanning insurance, finance, and pension systems. Professor Nielsen is actively involved in editorial work for multiple journals and frequently presents at major conferences, demonstrating his leadership in advancing actuarial science methodology while maintaining strong industry connections through his roles with ScienceFirst and Emergent.

Spyridon VrontosView profile

Professor

• Actuarial Science
• Financial Data Science
• Predictive Modelling
• +4 more

Spyridon Vrontos is a Professor and Head of the School of Mathematics, Statistics and Actuarial Science at the University of Essex. He holds a PhD in Statistics from Athens University of Economics and Business (2005), alongside MSc and BSc degrees in Statistics from the same institution. His expertise lies in Actuarial and Financial Data Science, with a focus on predictive modeling, risk management, and asset-liability management. He has received the Charles A. Hachemeister Prize from the Casualty Actuarial Society and has secured funding from organizations like the Society of Actuaries and Innovate UK. His research spans actuarial science, financial mathematics, and quantitative finance, addressing topics such as bonus-malus systems, volatility modeling, and pension fund performance evaluation. Professor Vrontos has extensive consulting experience in areas like pension fund valuation, insurance ratemaking, and financial planning tools. His work combines theoretical advancements with practical applications, bridging academic research and industry needs. He has supervised numerous projects funded by entities including the Hellenic Foundation for Research and Innovation and various insurance companies. His contributions to the field are evident through publications in top journals such as the International Journal of Forecasting and Quantitative Finance. His research portfolio reflects interdisciplinary collaboration, integrating statistical methodologies with financial and actuarial challenges. Recent work includes modeling the economic impacts of the pandemic using dynamic panel models and exploring machine learning approaches for volatility forecasting. He actively engages in teaching and curriculum development, having led the BSc and MSc Actuarial Science programs at Essex.

A. Nii-Armah OkineView profile

Assistant Professor

• Actuarial Science
• Loss Reserving
• Dependence Modeling
• +5 more

A. Nii-Armah Okine is an Assistant Professor in the Department of Mathematical Sciences at Appalachian State University. His research focuses on actuarial science, spanning loss reserving , dependence modeling , machine learning , ratemaking , and micro-insurance . He holds a Ph.D. in Actuarial Science from the University of Wisconsin-Madison and master’s degrees from Illinois State University and the University of Cape Coast-Ghana. Research Trends : His work integrates advanced statistical techniques with insurance analytics, emphasizing Individual-level loss reserving Ratemaking under environmental changes Climate risk modeling Machine learning applications Dependence structures in claims data Scientific Awards : 2025 CAS Individual Grant Competition 2022 CAS Summer Grant 2020 ASTIN Best Paper 2016-2019 SOA Hickman Scholar 2015 JII Best Paper Professional Recognition : He earned the ASA designation in 2021 and passed SOA exams including FSA (Introduction to General Insurance). His grants include the 2022 CAS Summer Grant and 2025 CAS Individual Grant Competition entry.

• Credibility Premium Estimation
• Robust Estimation
• Ratemaking and Reserving
• +6 more

Georgios Pitselis serves as an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Piraeus' School of Finance and Statistics. With academic appointments dating back to 2001, he obtained tenure in 2011 and has maintained continuous academic engagement through teaching, research, and professional service. His career includes visiting positions at prestigious institutions including Katholieke Universiteit Leuven and Concordia University. Dr. Pitselis specializes in credibility theory, robust estimation methods, and mortality modeling, with research interests spanning Credibility Premium Estimation Ratemaking and Reserving Solvency II implementation Longevity Risk modeling Non-Life Risk assessment His scholarly contributions focus on developing advanced statistical methods for insurance applications, particularly in the areas of multi-population mortality modeling and quantile-based credibility approaches. Analysis of his recent publications (2016-2022) reveals a strong emphasis on integrating credibility theory with modern statistical techniques, especially quantile regression methods applied to mortality forecasting and loss reserving. His work frequently addresses challenges in modeling with limited data and cross-population dependencies, demonstrating particular expertise in adapting traditional actuarial methods to contemporary insurance industry needs under Solvency II frameworks. Professional service highlights include: Guest Editor for Risks (2021-2022) Associate Editor for Insurance Mathematics and Economics (2008-2019) Member of working groups preparing the Greek insurance market for Solvency II International referee for Research Council KULEUVEN Extensive conference organization roles, particularly for IME conferences Dr. Pitselis has supervised numerous graduate students through thesis committees and has been actively involved in developing curricula for actuarial science programs. His teaching portfolio spans undergraduate and graduate levels, covering core actuarial topics including Credibility Theory, Ratemaking-Reserving, Pension Plans, and Loss Models. He has also contributed to professional development through workshops on Solvency II and actuarial science topics both in Greece and internationally.

Kyle FalconburyView profile

Clinical Professor

• Actuarial Science
• Risk Management
• Insurance
• +1 more

Kyle Falconbury is a Clinical Professor and Director of the Actuarial Science Program at the University of St. Thomas. With 26 years of industry experience at Fortune 100 companies, he specializes in Property & Casualty insurance, ratemaking, and reserve estimation techniques. His office is located at OSS 201. Research focuses on actuarial methodologies including customer retention optimization and high-severity loss reserving. Notable honors include: Fellow of the Casualty Actuarial Society (2002) Carscallen Prize in Mathematics PRIMA National Conference presenter (2012)

Christopher Blier-WongView profile

Assistant Professor

• Actuarial Science
• Machine Learning
• Statistics
• +4 more

Christopher Blier-Wong is an Assistant Professor in the Department of Statistical Sciences within the Faculty of Arts and Science at the University of Toronto, where he joined in 2024 after completing a postdoctoral fellowship at the University of Waterloo. He holds a PhD in Actuarial Science from Université Laval (2023), along with multiple graduate degrees in Computer Science (AI specialization) and Actuarial Science from the same institution. His research lies at the intersection of actuarial science, machine learning, and statistics, with a particular focus on dependence modelling and e-variables. His work explores non-life insurance, risk theory, risk-sharing mechanisms, and the application of machine learning techniques to actuarial problems. Recent publications demonstrate his expertise in constructing high-dimensional copulas, developing efficient risk allocation methods, and pioneering the use of image data in insurance pricing. His research portfolio shows a strong trend toward developing computationally efficient methods for complex actuarial problems, particularly those involving dependence structures. He has made significant contributions to the understanding of Farlie-Gumbel-Morgenstern (FGM) copulas and their generalizations, creating methods that scale effectively to high dimensions while maintaining computational tractability. Scientific Awards and Recognition: NSERC Postdoctoral Fellowship (2023-2025) ARC Graduate Student Presentation Award from Society of Actuaries (2022) Canada Graduate Scholarships Michael Smith Foreign Study Supplements (2022) Multiple research grants from NSERC, Université Laval, and industry partners Honor Roll for Academic Excellence at Université Laval (2018, 2020) Blier-Wong currently leads research funded by a major sponsored research agreement titled 'Dependence, inference and unstructured data in actuarial science' (2025-2030). He has supervised graduate students through teaching courses such as 'STA 2536 Data Science for Risk Modelling' at the University of Toronto and previously taught 'ACT-1003 Complements in Mathematics' at Université Laval. His research group focuses on developing novel statistical methodologies with practical applications in insurance and risk management.

Heather ClemensView profile

Associate Professor

• Actuarial Science
• Life Insurance
• Health Insurance
• +6 more

Heather Clemens serves as Associate Professor of Practice and Assistant Director of the Actuarial Science Program at the University of Nebraska-Lincoln's College of Business within the Finance department, holding dual distinctions as Ameritas Actuarial Faculty Fellow and Seacrest Teaching Fellow. Her academic foundation includes a Bachelor of Science in Business Administration with Actuarial Science major from the University of Nebraska-Lincoln and a Master of Arts in Leadership from Bellevue University. With over 18 years of professional experience, Professor Clemens has provided actuarial expertise at Compass Health Analytics (claim liability analysis, rate development) and WellPoint (valuation leadership). Her teaching and research spans actuarial science, life/health insurance, property/casualty insurance, risk theory, loss reserving, ratemaking, and financial mathematics. Her distinguished honors include: Ameritas Actuarial Faculty Fellow (2016) Seacrest Teaching Fellow In her role as Assistant Director, she shapes the actuarial curriculum and prepares students for professional certification through graduate courses (ACTS 875, 840, 874, FINA 812) and undergraduate courses (ACTS 475, 440, 474, FINA 412), emphasizing practical applications in insurance pricing, valuation, and risk modeling.

Jing ZhangView profile

Professor

• Bayesian Statistics
• Spatial Analysis
• Statistical Modeling
• +3 more

Jing Zhang is a Professor in the Department of Statistics at Miami University, serving as Lead Departmental Advisor. She is affiliated with the Center for Aquatic and Watershed Sciences, Center for Analytics and Data Science, and Institute for the Environment and Sustainability. Her research focuses on Bayesian Statistics, Spatial Analysis, and Statistical Modeling for Environmental and Biological Studies. She holds a Ph.D. from the University of Missouri-Columbia (2008). Her work integrates statistical methodologies with environmental and biomedical applications, including aquatic toxicity assessment, maternal health studies, and material science. Recent research emphasizes Bayesian approaches for historical data integration in toxicity testing and environmental monitoring. She has published widely on topics ranging from statistical modeling in toxicology to educational policy analysis using PIAAC data. Her contributions span interdisciplinary collaborations, bridging statistics with environmental science, public health, and biomedical engineering. She is based in the Department of Statistics, DSB 350, and can be reached at zhangj8@MiamiOH.edu.

Gareth W. PetersView profile

Professor

• Actuarial Science
• Risk and Insurance Modeling
• Bayesian Statistics
• +6 more

Professor Gareth W. Peters is the Janet & Ian Duncan Chair in Actuarial Science at the Department of Statistics & Applied Probability, University of California Santa Barbara (UCSB). A Fellow of multiple prestigious institutions (FIOR, FRSS, FIMA, YAS-RSE, IEEE-Senior), his research spans statistical risk modeling, insurance analytics, machine learning, and environmental finance. Risk & Insurance Bayesian Modeling Machine Learning in Finance Environmental Data Analysis Cyber Risk Quantification His recent work explores agricultural commodities, municipal green bonds, hearing loss diagnostics, and financial regime classification. Articles reflect trends in network-structured data, multivariate screening, and causal inference. Scientific Awards Peter-Clark Prize (2024) Fellow of the Institute of Risk & Safety Management (FIOR) Fellow of the Royal Statistical Society (FRSS) Fellow of the Institute of Mathematics and its Applications (FIMA) Young Academy of Scotland (YAS-RSE) Elected Member of the International Statistical Institute (ISI) IEEE Senior Member

Seul ki KangView profile

Assistant Professor

• Actuarial Mathematics
• Extreme Value Theory
• Statistical Modeling
• +3 more

Seul ki Kang is an Assistant Professor in the Mathematics Department at the University of St. Thomas's College of Arts and Sciences. With dual PhDs in Risk Management and Insurance from Georgia State University and Applied Mathematics from Texas A&M, Kang's research spans actuarial mathematics, extreme value theory, and statistical modeling of financial losses. His publications focus on innovative risk analysis methodologies for insurance applications, developing multi-step approaches for ratemaking and risk assessment. Kang's computational modeling expertise includes spectral methods for analyzing flows in heterogeneous media. Courses taught include Foundations of Actuarial Mathematics (ACSC 451), covering life insurance mathematics, annuities, and premium calculations using probabilistic approaches.

• Risk Management
• Actuarial Science
• Financial Econometrics
• +4 more

Professor Jens Perch is a distinguished academic in Actuarial Science at Bayes Business School, City St George's, University of London. He holds a PhD from UC Berkeley and an Actuary degree from the University of Copenhagen. His expertise spans Risk Management, Actuarial Statistics, and Financial Econometrics, with notable contributions to insurance modeling and statistical methodologies. Affiliations: Honorary Professor (2006), Fellow of the Royal Statistical Society (2013), and member of the Danish Institute of Actuarial Science. Research focuses on telematics-driven insurance, survival analysis, and metabolic engineering applications. His work bridges actuarial science with advanced statistical techniques, addressing real-world challenges in risk assessment and financial systems. Key awards include the Honorary Professor title from the University of Copenhagen. Publications highlight innovative approaches in motor insurance pricing, microbial dynamics, and metabolic pathway engineering. Grants and collaborations span industries like financial services and biotechnology.