Assistant Professor, School of Mathematical and Statistical Sciences
Email: lixinyi@clemson.edu
Biosketch
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) and 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
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.
Selected Publications
Li, X., Yu, S., Wang, Y., Wang, G., Wang, L. and Lai M-J. (2024) Nonparametric regression for 3D point cloud learning. Journal of Machine Learning Research, 25(102), 1–56.
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.