欢迎感兴趣的本科生申请本人研究生，欢迎感兴趣的博士生邮件联系本人咨询博士后岗位信息，要求有较好的数理基础，对人工智能方向怀有兴趣且拥有一定编程基础。

Background

• 罗格斯新泽西州立大学统计系博士

• 宾夕法尼亚大学生物统计系和沃顿商学院统计系博士后

• 中国人民大学统计与大数据研究院助理教授（2021-2022）

• 中国人民大学统计与大数据研究院副教授（2022至2025）

• 清华大学统计与数据科学系副教授（2025-至今）

Publications:

Sai Li and Linjun Zhang. Multi-dimensional domain generalization with low-rank structures. Journal of the American Statistical Association(accepted). 2025.

Sai Li and Ting Ye. A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data. Journal of the Royal Statistical Society: Series B. 87(2), 529-548. 2025.

Jianqiao Wang, Sai Li, and Hongzhe Li. A unified approach to robust inference for genetic covariance. Journal of the American Statistical Association. 119(548): 2585-2597, 2024.

Sai Li, Yisha Yao, and Cun-Hui Zhang. Comment: A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models. Journal of the American Statistical Association. 118(543), 1586–1589, 2023.

Sai Li, Linjun Zhang, T. Tony Cai, and Hongzhe Li. Estimation and inference in high-dimensional GLMs with transfer learning. Journal of the American Statistical Association. 119(546), 1274–1285, 2023.

Sai Li, Tianxi Cai, and Rui Duan. Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach. Annals of Applied Statistics. 17(4): 2970-2992, 2023.

Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn. Improving out-of-distribution robustness via selective augmentation. International Conference of Machine Learning. PMLR 162:25407-25437, 2022.

Sai Li, T. Tony Cai, and Hongzhe Li. Transfer learning in large-scale graphical models with false discovery rate control. Journal of the American Statistical Association. 118(543), 2171–2183, 2022.

Sai Li, T. Tony Cai, and Hongzhe Li. Transfer learning for high-dimensional linear regression: Prediction, estimation, and minimax optimality. Journal of the Royal Statistical Society: Series B.84: 149–173, 2022.

Sai Li, T. Tony Cai, and Hongzhe Li. Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach. Journal of the American Statistical Association. 117(540): 1835-1846, 2022.

Sai Li. Debiasing the debiased Lasso with bootstrap. Electronic Journal of Statistics, 14(1): 2298-2337, 2020.

Sai Li, Ritwik Mitra, and Cun-Hui Zhang. Comment: An adaptive resampling test for detecting the presence of significant predictors. Journal of the American Statistical Association. 110(512): 1455-1456, 2016.