Publications

A full list of my publications can be found on Google Scholar. Source code in these projects can be found on GitHub.

Asterisk represents equal contribution.

Peer-reviewed Publications

1. Guo, Z.*, Li, X.*, Han, L., and Cai, T. (2025). Robust Inference for Federated Meta-Learning. Journal of the American Statistical Association, 1-16.

2. Li, X., Li, S. and Luedtke, A. (2023). Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data. Journal of the Royal Statistical Society Series B (Statistical Methodology), 85(2), 356-377.

3. Sperotto, F., Gutierrez-Sacristan, A., Makwana, S., Li, X., Rofeberg, V., Cai, T., Bourgeois, F., Omenn, G., Hanauer, D., Saez, C., et al. (2023). Clinical Phenotypes and Outcomes in Children with Multisystem Inflammatory Syndrome across SARS-CoV-2 Variant Eras: A Multinational Study from the 4CE Consortium. eClinicalMedicine, 64.

4.  Li, X., May, S., Trumble, I.M., Archin, N.M., and Hudgens, M.G. (2022). Paired Serial Limiting Dilution Assays. Statistics in Medicine, 41(24), 4809-4821.

5. Nishath, T., Li, X., Chandramohan, A., et al. (2022). Risk Factors Associated with Abandonment of Care in Retinoblastoma: Analysis of 692 Patients from 10 Countries. British Journal of Ophthalmology.

6. Singh, S., Nishath, T., Fabian, I. D., Li, X., Othus, M., Tzukikawa, M., and Stacey, A. W. (2022). Seasonal Variation in the Diagnosis of Retinoblastoma. Ophthalmic Epidemiology, 1-6.

7. Tank, A.*, Li, X.*, Fox, E. B. and Shojaie, A. (2021). The Convex Mixture Distribution: Granger Causality for Categorical Time Series. SIAM Journal on Mathematics of Data Science, 3(1), 83-112.

Preprints

1. Li, X., Yuan, E.Y., Kuperberg, S.J., Bonzel, C., Jeffway, M.I., Cai, T., Liao, K.P., Aguiar-Ibáñez, R., Kao, Y., Santorelli, M.L., Christiani, D.C., Cai, T., and Duan, R. (2025+). Early detection of non-small cell lung cancer: an electronic health record data-driven approach. [medRxiv] Accepted at BMC Medicine.

2. Ying, C., Jin, J., Guo, Y., Li, X., Liang, M., and Zhao, J. (2025+). Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift. [arXiv] Accepted at ICML 2025.

3. Li, X., Tian, L., and Cai, T. (2025+). Sampling-based federated inference for M-estimators with non-smooth objective functions.  [arxiv]

4. Li, X.* and Li, S.* (2025+). Efficient inference for covariate-adjusted Bradley-Terry model with covariate shift. [arxiv]

5. Li, X., Safikhani A., and Shojaie, A. (2025+). Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm. [arxiv]

Commentary

1. Li, S.*, Li, X.*, and Luedtke, A. (2020). Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning. Journal of the American Statistical Association, 116(534), 680-689.

2. Li, X. and Shojaie, A. (2020). Discussion of ``A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression". Journal of the American Statistical Association, 115(532), 1717-1719.