Email: saili “at” mail “dot” tsinghua “dot” edu “dot” cn
Address: Lv Dalong Building 811, Tsinghua University, Beijing.
Github: https://github.com/saili0103/
Ph.D., Department of Statistics and Biostatistics, Rutgers Univeristy, May 2018.
Advisors: Professor Cun-Hui Zhang and Professor Steven Buyske.
Bachelor of Economics, School of Statistics, Renmin University of China, June 2013.
Exchange student, Department of Statistics and Actuarial Science, University of Hong Kong, Spring 2011.
Associate professor at Department of Statistics and Data Science, Tsinghua University, December 2025 till now.
Associate professor at Institute of Statistics and Big Data, Renmin University of China, September 2022 to November 2025.
Assistant professor at Institute of Statistics and Big Data, Renmin University of China, September 2021 to August 2022.
Postdoctoral researcher at Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, July 2018 to June 2021.
Mentors: Professor Hongzhe Lee and Professor T. Tony Cai.
Sai Li. Debiasing the debiased Lasso with bootstrap. Electronic Journal of Statistics, 14(1): 2298-2337,2020.
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, 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. Transfer learning in large-scale graphical models with false discovery rate control. Journal of the American Statistical Association, 118(543): 2171–2183. 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. 2022.
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.
Sai Li, Linjun Zhang, T. Tony Cai, and Hongzhe Li. Estimation and inference in high-dimensional generalized linear models with knowledge transfer. Journal of the American Statistical Association(accepted). 2023.
Jianqiao Wang, Sai Li, and Hongzhe Li. A unified approach to robust inference for genetic covariance. Journal of the American Statistical Association(accepted). 2023.
Sai Li, T. Tony Cai, and Hongzhe Li. Statistical Inference for High Dimensional Regression with Proxy Data. Statistica Sinica (accepted).2024.
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.
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, 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.
Sai Li and Zijian Guo. Causal inference for nonlinear outcomes with possibly invalid instrumentalvariables. under revision. 2020.
Elynn Chen, Michael Jordan, Sai Li. Transferred Q-learning. submitted. 2022.
Sai Li, T. Tony Cai, and Hongzhe Li. Estimation and Inference with Proxy Data and its Genetic Applications. Submitted. 2022.
Concurrent session, Women in Statistics and Data Science 2020, online, ``Transfer learning in high-dimensional sparse regression’’, 10/2020.
Session Chair, Modern Statistical Learning Methods, JSM 2020.
Contributed talks, JSM 2019 and JSM 2020.
Invited talk, CMStatistics 2018, Pisa, Italy,``Debiasing the debiased Lasso with bootstrap’’, 12/2018.
Poster presentation, Mendelian randomization in the age of large-scale accessible genomics data, Bristol, UK, ``Mendelian Randomization when many instruments are invalid: hierarchical empirical Bayes estimation’’, 07/2017.
Student paper competition, WNAR conference, Santa Fe, ``Mendelian Randomization when many instruments are invalid: hierarchical empirical Bayes estimation’’, 06/17.
Instructor:
Statistical models and inference (PhD level), Fall 2021.
Statistical foundations of data science and AI (MS level), Fall 2021.
Introductory Statistics for Business (Undergrad), Summer 2016.
Travel Award, Conference on Mendelian randomization in the age of large-scale accessible genomics data, 07/17.
Student Distinguished Written Paper Award, WNAR conference, 06/17.
TA/GA Professional Development Fund, 06/15, 06/16.
Certificate of Excellence for attending 3rd Annual Interdisciplinary Quantitative Biology Boot Camp-drug discovery and development, 01/16.