Gee Y. Lee

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.