Profile
Xinyi Li
Mathematical and Statistical Sciences
Assistant Professor
864-656-0861
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, with application to statistical genetics, neuroimaging, and public health.
Courses Taught
MATH 4000 Theory of Probability -- Spring 2021, Fall 2020.
Selected Publications
Wang, L., Wang, G., Li, X., Yu, S., Kim, M., Wang, Y., Gu, Z. and Gao, L. (2021+) Modeling and forecasting COVID-19. AMS: Notices of the American Mathematical Society. Accepted.
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). In press.
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.
Li, X., Wang, L. and Nettleton, D. (2019) Additive partially linear models for ultra-high-dimensional regression. Stat, 8(1), e223.
Selected Talks
"Sparse learning and structure identification for ultrahigh-dimensional image-on-scalar regression", Auburn University, January 28, 2021.
"Individualized treatment regimes incorporating imaging features", Joint Statistical Meetings, Virtual, August 5, 2020.
"Combining brain imaging and genomics for Alzheimer's Studies", Pod of Asclepius, June 22, 2020.
"Sparse learning for image-on-scalar regression with application to imaging genetics studies", Duke University, March 27, 2019.
"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.
Memberships
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
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
