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Contact Information

P: 864-656-3434

Campus Location

O-110 Martin Hall


Monday - Friday:
8 a.m. - 4:30 p.m.


Profile Photo

Xinyi Li

Mathematical and Statistical Sciences

Assistant Professor

Martin Hall O329 [Office]

Educational Background

Ph.D., Statistics, Iowa State University
M.S., Statistics, University of Georgia
B.S., Mathematical Statistics, Beijing Normal University

Profile/About Me

I am currently an assistant professor in the School of Mathematical and Statistical Sciences. Before joining Clemson, I was a postdoctoral fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI), joint with the University of North Carolina at Chapel Hill. I completed my bachelor’s degree in mathematical statistics from Beijing Normal University (Beijing, China). I then earned a master’s degree in statistics at the University of Georgia, and a Ph.D. in statistics at Iowa State University.

Research Interests

My research interests include, but are not limited to, precision medicine, functional data analysis, non-/semi-parametric high-dimensional regression, spatio-temporal analysis, reinforcement learning, with application to statistical genetics, neuroimaging, and public health.

Courses Taught

MATH 4000 Theory of Probability -- Fall 2020, Spring 2021, Fall 2021
MATH 8050 Data Analysis -- Spring 2022
MATH 8050 Nonparametric Statistics -- Spring 2024
MATH 8710 Machine Learning I -- Fall 2022
MATH 8810 Mathematical Statistics -- Fall 2021

Selected Publications

Li, X., Freeman, N. and Wang, L. Q-Learning Based Methods for Dynamic Treatment Regimes. (2023) In Yichuan Zhao and Dinggen Chen (Eds) Precision Medicine: Methods and Applications, Springer.

Cramer, E., Ray, E., Lopez, V., [et al, including Li, X.] (2022) Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US. Proceedings of the National Academy of Sciences, 119(15), e2113561119.

Li, X., Wang, L. and Wang, H. (2021) Sparse learning and structure identification for ultra- high-dimensional image-on-scalar regression. Journal of the American Statistical Association (Theory and Methods), 116(536), 1994–2008.

Cho, H., Zitkovsky, J., Li, X., Lu, M., Shah, K., Sperger, J., Tsilimigras M. C. B. and Kosorok, M. R. (2020) Comment: Diagnostics and kernel-based extensions for linear mixed effects models with endogenous covariates. Statistical Science, 35(3), 396-399.

Li, X., Wang, L. and Nettleton, D. (2019) Simultaneous sparse model identification and learning for ultra-high-dimensional additive partially linear models. Journal of Multivariate Analysis, 173, 204-228.

Selected Talks

"Functional individualized treatment regimes with imaging features," North Carolina State University, March 09, 2023.
"Statistical learning in spatial regression: efficiency vs. accuracy," Joint Statistical Meeting, August 2022.
"Nonparametric regression for 3D point cloud learning," ICSA 2021 Applied Statistics Symposium, September 2021.
"Combining brain imaging and genomics for Alzheimer's studies," Pod of Asclepius, June 22, 2020.
"Structure identification and sparse learning for image-on-scalar regression with application to imaging genetics studies," University of North Carolina at Chapel Hill, January 17, 2019.


American Association for the Advancement of Science;
American Statistical Association;
Institute of Mathematical Statistics;
International Chinese Statistical Association;
Mu Sigma Rho National Statistical Honor Society.

Honors and Awards

National Science Foundation DMS-2210658, 2022-2025
IMS New Researcher Travel Award, 2019
ASA GA Chapter Best Student Poster Award, 2017
2nd Place (out of 120 teams worldwide) in 2016 Data Mining Cup

Contact Information

P: 864-656-3434

Campus Location

O-110 Martin Hall


Monday - Friday:
8 a.m. - 4:30 p.m.