Biography
Dr. Kuang-Yao Lee joins the Fox School on a tenure-track appointment within the Department of Statistical Science.
Prior to his arrival, Lee served as an associate research scientist at Yale University’s Center for Statistical Genomics and Proteomics, where he investigated and developed statistical methods for high-dimension data and conducted collaborative research.
His current research interests span across two disciplines — statistical genomics and machine learning with high dimensionality as a common theme. Much of his work has been published in top journals with applications to various fields, such as bioinformatics, image analysis, and functional data. He also works closely with biologists and computer scientists on problems related to genome-wide association studies, gene regulatory network, and pathway analysis.
Lee received his PhD in Statistics from The Pennsylvania State University. He earned a Master of Science degree and a Bachelor of Science degree, both in Mathematics, from National Taiwan University.
Research Interests
- Statistical machine learning
- Statistical genomics
- Graphical modeling and causality learning
- High-dimension inference
- Semi- and non-parametric methods and their applications
Courses Taught
Number | Name | Level |
|---|---|---|
STAT 3504 | Time Series and Forecasting Models | Undergraduate |
STAT 3506 | Nonparametric and Categorical Data Analysis | Undergraduate |
STAT 8121 | Statistical Computing and Optimization | Graduate |
Selected Publications
Recent
Lee, K., Li, L., Li, B., & Zhao, H. (2023). Nonparametric Functional Graphical Modeling Through Functional Additive Regression Operator. Journal of the American Statistical Association, 118(543), 1718-1732. Informa UK Limited. doi: 10.1080/01621459.2021.2006667.
Lee, K., Ji, D., Li, L., Constable, T., & Zhao, H. (2023). Conditional Functional Graphical Models. Journal of the American Statistical Association, 118(541), 257-271. Informa UK Limited. doi: 10.1080/01621459.2021.1924178.
Lee, K. & Li, L. (2022). Functional Structural Equation Model. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(2), 600-629. Oxford University Press (OUP). doi: 10.1111/rssb.12471.
Lee, K. & Li, L. (2022). Functional sufficient dimension reduction through average Fréchet derivatives. The Annals of Statistics, 50(2). Institute of Mathematical Statistics. doi: 10.1214/21-aos2131.
Lee, K., Liu, T., Li, B., & Zhao, H. (2020). Learning causal networks via additive faithfulness. Journal of Machine Learning Research, 21.