Academic Position
Associate Professor, Department of Statistics and Data Science, Tsinghua University, 2024/07 – present
Associate Professor, Center for Statistical Science, Tsinghua University, 2018/12 – 2024/07
Assistant Professor, Center for Statistical Science, Tsinghua University, 2016/08 – 2018/12
Postdoctoral Scholar, University of California, Berkeley, 2014/07 – 2016/06
Education
Peking University, Beijing, China, 2009/09 – 2014/06
S. University of Science and Technology of China, Hefei, China, 2005/09 – 2009/06
Research and Visiting Experience
Research Interests
Selected Publications
Xin Lu, Tianle Liu, Hanzhong Liu* and Peng Ding (2023). Design-based theory for cluster rerandomization. Biometrika, 110(2), 467-483.
Hanzhong Liu, Fuyi Tu and Wei Ma* (2023). Lasso-adjusted treatment effect estimation under covariate-adaptive randomization. Biometrika, 110(2), 431-447.
Hanzhong Liu, Jiyang Ren and Yuehan Yang* (2023+). Randomization-based joint central limit theorem and efficient covariate adjustment in randomized block 2K factorial experiments. Journal of the American Statistical Association, in press.
Xinhe Wang, Tingyu Wang and Hanzhong Liu* (2023). Rerandomization in stratified randomized experiments. Journal of the American Statistical Association, 118(542), 1295-1304.
Ke Zhu and Hanzhong Liu* (2023+). Pair-switching rerandomization. Biometrics, in press.
Yujia Gu, Hanzhong Liu and Wei Ma* (2023+). Regression-based multiple treatment effect estimation under covariate-adaptive randomization. Biometrics, accepted.
Hanzhong Liu and Yuehan Yang* (2020). Regression-adjusted average treatment effect estimates in stratified randomized experiments, Biometrika, 107(4), 935-948.
Adam Bloniarz, Hanzhong Liu (co-first author), Cunhui Zhang, Jasjeet S. Sekhon and Bin Yu* (2016). Lasso adjustments of treatment effect estimates in randomized experiments. Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7383-7390.
Other Publications
Hanzhong Liu* (2023). Bootstrapping inference of average treatment effect in completely randomized experiments with high-dimensional covariates. Biostatistics & Epidemiology, 6(2), 203-220.
Wei Ma, Fuyi Tu and Hanzhong Liu* (2022). Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective. Statistics in Medicine, 41, 5645-5661.
Ke Zhu and Hanzhong Liu* (2022). Confidence intervals for parameters in high-dimensional sparse vector autoregression. Computational Statistics & Data Analysis, 168, 107383.
Ke Zhu, Yingkai Jiang, Xiang Wang, Zhicheng Shi, Chao Yang*, Hanzhong Liu* and Ke Deng* (2022). A new framework of customized production product certification based on the combination of domain knowledge and data inference (in Chinese). Chinese Journal of Applied Probability and Statistics, 38(4): 581-602.
Hanzhong Liu and Jinzhu Jia* (2022). On estimation error bounds of the Elastic Net when p >> n. Statistics, 56(3), 498-517.
Hanzhong Liu* (2021). Comment on `Inference after covariate-adaptive randomization: aspects of methodology and theory'. Statistical Theory and Related Fields, 5(3), 192-193.
Hanzhong Liu, Xin Xu and Jingyi Jessica Li* (2020). A bootstrap Lasso + Partial Ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models. Statistica Sinica, 30, 1333-1355.
Hanzhong Liu and Bin Yu* (2017). Comments on: High dimensional simultaneous inference with the bootstrap. Test, 26, 740-750.
Lan Wu, Yuehan Yang* and Hanzhong Liu (2014). Nonnegative-lasso and application in index tracking. Computational Statistics & Data Analysis, 70, 116-126.
Hanzhong Liu and Bin Yu* (2013). Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression. Electronic Journal of Statistic, 7, 3124-3169.
Submitted Paper
Haoyang Yu, Ke Zhu* and Hanzhong Liu (2023). Stratified causal bootstrap. Submitted to Biometrika.
Fuyi Tu, Wei Ma and Hanzhong Liu* (2023). A unified framework for covariate adjustment under stratified randomization. Submitted to Statistics in Medicine.
Xin Lu and Hanzhong Liu* (2022). Tyranny-of-the-minority regression adjustment in randomized experiments. Major revision in Journal of the American Statistical Association.
Ke Zhu, Yuehan Yang and Hanzhong Liu* (2022). Design-based theory for Lasso adjustment in randomized block experiments with a general blocking scheme. Major revision in Journal of Business & Economic Statistics.
Wenqi Shi, Anqi Zhao and Hanzhong Liu* (2022). Rerandomization and covariate adjustment in split-plot designs. Major revision in Journal of Business & Economic Statistics.
Teaching
Ph.D. Supervised
Ke Zhu
Jiyang Ren
Xin Lu
Hongzi Li
Haoyang Yu
Wanjia Fu
Service
2022/12-2026/12 全国工业永利赌场
教学研究会理事
2021/09-2026/09 北京应用永利赌场
会理事
2019/04-2023/04 全国工业永利赌场
教学研究会青年永利赌场
家协会理事
2017/03-2021/03 中国现场统计研究会计算统计分会副秘书长
Organizing Conference
Co-organizer, The 4th PKU-Tsinghua Colloquium on Statistics, Beijing, China, Jun 3, 2019
Co-organizer, The IASC-ARS 25th Anniversary Conference and the CASC 2nd Annual Conference, Beijing, China, Nov 9-11, 2018
Co-organizer, Tsinghua Symposium on Statistics and Data Science for Young Scholars, Beijing, China, Nov 17-19, 2017
Journal Reviewing
Annals of Statistics, Journal of the American Statistical Association, Annals of Applied Statistics, Journal of Econometrics, Journal of Machine Learning Research, International Conference on Machine Learning, etc
Funding
PI, National Natural Science Foundation of China, 2021-2024
PI, National Natural Science Foundation of China, 2018-2020
Participant, Guo Qiang Institute of Tsinghua University, 2021-2023
Participant, National Natural Science Foundation of China, 2018-2021
Participant, National Key Research and Development Plan, 2017-2020
