刘汉中 副教授

研究方向： 高维数据统计推断，因果分析，大数据，机器学习

地址： 清华大学伟清楼212-B室


电话： 010-62780575


邮箱： lhz2016@tsinghua.edu.cn


Academic Position
  • Associate Professor, Center for Statistical Science, Tsinghua University, 2018/12 – present
  • 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
  • 2012/09 – 2014/04, University of California, Berkeley, Visiting Scholar
Research Interests
  • Causal Inference
  • High-dimensional Statistics
  • Big Data
  • Machine Learninng
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
  • Graduate courses
  • Advanced Probability Theory II (2017-2023/Spring)
  • Undergraduate courses
  • Statistical Inference (2017-2023/Fall)
Ph.D. Supervised
  • Ke Zhu
  • Jiyang Ren
  • Xin Lu
  • Hongzi Li
  • Haoyang Yu
  • Wanjia Fu
Service
  • 2022/12-2026/12 全国工业统计学教学研究会理事
  • 2021/09-2026/09 北京应用统计学会理事
  • 2019/04-2023/04 全国工业统计学教学研究会青年统计学家协会理事
  • 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