(* represents current or previous advisees)
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]
Gu, Z., Li, X., Wang, G. and Wang, L.. (2025) Spatiotemporal heterogeneity learning: generalized spatiotemporal semi-varying coefficient models with structure identification. Journal of Time Series Analysis. In press.
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-2210658
“Collaborative Research: Semiparametric and Reinforcement Learning for Precision Medicine”
Contact Principal Investigator, $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)