Kuang Yao Lee
Kuang-Yao Lee
Statistical Science
Assistant Professor
  • Office Location1810 Liacouras Walk, Room 375
  • Website Visit


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 Areas

  • Statistical machine learning
  • Statistical genomics
  • Graphical modeling and causality learning
  • High-dimension inference
  • Semi- and non-parametric methods and their applications


    • Ph.D., Statistics , The Pennsylvania State University, State College, PA


    • Jul 2017 – present, Assistant Professor of statistics, Temple University.
    • Jan 2016 – Jun 2017, Associate Research Scientist, Yale University

    Sample Publications

    • Lee, K.-Y., Li, B. and Chiaromonte, F. (2013), A general theory of nonlinear sufficient dimension reduction: formulation and estimation. Annals of Statistics, 41, 221-249
    • Lee, K.-Y., Li, B. and Zhao, H. (2016) On an additive partial correlation operator and nonparametric estimation of graphical models. Biometrika, 103 (3): 513-530.
    • Lee, K.-Y., Li, B. and Zhao, H. (2016) Variable selection via additive conditional independence. Journal of the Royal Statistical Society: Series B, 78 (5), 1037-1055.
    • Lee, K.-Y., Liu, T. and Zhao, H. (2016) RComment on the discussion paper `Causal inference using invariant prediction: identification and confidence intervals’ by Peters et al. Journal of the Royal Statistical Society: Series B, 78, 947-1012.