(* represents current or previous advisees)
Gallagher, C., Killick, R. and Li, X. (2026) Causal regions and simulation of autoregressive models. The American Statistician, 1–11. [pdf]
Gu, Z., Li, X., Wang, G. and Wang, L. (2026) Spatiotemporal heterogeneity learning: generalized spatiotemporal semi-varying coefficient models with structure identification. Journal of Time Series Analysis, 47(1), 8–24. [pdf]
Wang, Z.*, Rowe, D., Li, X. and Brown, D. A. (2025) Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data. Journal of Applied Statistics, 52(6), 1299–1314. [pdf]
Kung, E. O., Stokowski, S., Withycombe, J. S., Li, X. and Godfrey, M. (2025) Using wearable technology to explore sleep’s influence on college women’s basketball performance. Archives of Physical Health and Sports Medicine, 7(1), 18–27. [pdf]
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. [pdf][code][talk] [presentation]
Wang, Z.*, Rowe, D., Li, X. and Brown, D. A. (2024) A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data. Magnetic Resonance Imaging, 109, 271–285. [pdf]
Lopez, V., Cramer, E., Pagano, R., [et al, including Li, X.] (2024) Challenges of COVID-19 case forecasting in the US, 2020–2021. PLOS Computational Biology, 20(5), e1011200. [pdf]
Li, X., Freeman, N. L. and Wang, L. (2024) Q-Learning Based Methods for Dynamic Treatment Regimes. In: Zhao, Y. and Chen, DG. (Eds) Statistics in Precision Health: Theory, Methods and Applications, Springer. [pdf][code]
Wang, G., Gu, Z., Li, X., Yu, S., Kim, M., Wang, Y., Gao, L. and Wang, L. (2023) Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing. Journal of Applied Statistics, 50(11-12), 2408–2434. [pdf][code]
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. [pdf]
Lee, D., El-Zaatari, H., Kosorok, M. R., Li, X. and Zhang, K. (2022) Discussion of Gorsky and Ma “Multi-scale Fisher’s independence test for multivariate dependence.” Biometrika, 109(3), 593–596. [pdf]
Fisher, W.*, Zhang, Q., Li, X. and Deng, X. (2022) A NURBS fitting approach for quality assessment of 3D printing. Proceedings of IISE Annual Conference and Expo 2022, 357–362. [pdf]
Cramer, E., Huang, Y., Wang, Y., [et al, including Li, X.] (2022) The United States COVID-19 Forecast Hub dataset. Scientific Data, 9(1), 462. [pdf]
Wang, Y., Kim, M., Yu, S., Li, X., Wang, G., Wang, L. (2022) Nonparametric estimation and inference for spatiotemporal epidemic models. Journal of Nonparametric Statistics, 34(3), 683–705. [pdf][code]
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. [pdf][code]
Wang, L., Wang, G., Li, X., Yu, S., Kim, M., Wang, Y., Gu, Z. and Gao, L. (2021) Modeling and forecasting COVID-19. Notices of the American Mathematical Society, 68(4), 585–595. [pdf][code]
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. [pdf]
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. [pdf]
Li, X., Wang, L. and Nettleton, D. (2019) Additive partially linear models for ultra-high-dimensional regression. Stat, 8(1), e223. [pdf]
Li, X., Fang, W. and Lin, W. (2014) Comparison of interpolation methods for tropical cyclone track and intensity over Northwestern Pacific basin. Journal of Beijing Normal University (Natural Science), 50(2), 111. [pdf]
(* represents current or previous advisees)
Li, X. and Kosorok, M. R. Functional individualized treatment regimes with imaging features. [pdf]
Li, X., Hoch, M.* and Kosorok, M. R. Linear regression using Hilbert-space valued covariates with unknown reproducing kernel. [pdf]
Wang, L., Wang, G., Gao, L., Li, X., Yu, S., Kim, M., Wang, Y. and Gu, Z. Spatiotemporal dynamics, nowcasting and forecasting of COVID-19 in the United States. [pdf][code]
Ray, E., Wattanachit, N., Niemi, J., [et al, including Li, X.] Ensemble forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. [pdf]
National Science Foundation DMS-2610668
“Collaborative Research: Integrating Multimodal Data in AI for Precision Medicine”
Principal Investigator (Clemson University, lead organization), $273,113 (09/2026–08/2029)
National Science Foundation DMS-2210658
“Collaborative Research: Semiparametric and Reinforcement Learning for Precision Medicine”
Principal Investigator (Clemson University, lead organization), $393,850 (08/2022–07/2026)
South Carolina Alzheimer’s Disease Research Center, SPARK Grant
“Interpretable Statistical and Machine Learning for Precision Medicine with Abundant Features in Alzheimer’s Disease”
Sole Principal Investigator, $30,615 (01/2025–06/2025)
Clemson-MUSC AI Hub, AI Augmentation Grant
“AI Analysis of Cancer Cell Line Drug Sensitivity to Predict Targeted Drug Sensitivity in Patients”
Principal Investigator, $25,000 (07/2022–06/2024)
Clemson University, Faculty Excellence Interdisciplinary Enhancement Program
“DECAL: Data sECurity and mAchine Learning—When Theory Meets Practice”
Co-PI (PI: Felice Manganiello), $19,075 (05/2023–04/2024)
National Institutes of Health, Clemson CHG COBRE in Human Genetics Pilot Award P20 GM139769
“Statistical Imaging-Genetics Study for Precision Medicine in Alzheimer’s Disease”
Sole Principal Investigator, $150,000 (02/2022–01/2024)