个人简历

中国人民大学长聘教授、博士生导师，学校和学部学术委员会委员，统计与大数据研究院院长，人民教育出版社普通高中教科书《数学》（B）版第一册联合主编，国家重大人才工程入选者，国家杰出青年科学基金获得者，国家重点研发计划首席科学家，兼任中国现场统计研究会生存分析分会理事长和高维数据统计分会副理事长等。

先后受邀担任国际统计学领域顶级学术期刊AoS、国际权威学术期刊Stat Sinica、JMVA和SII等副主编，以及国内统计学领域顶级学术期刊《中国科学·数学》（中、英文版）、《系统科学与数学》（中、英文版）和《应用概率统计》等青年编委、编委和副主编等。

长期从事大数据统计学基础理论、方法和应用研究。1、在高维度大数据领域，提出不依赖于切片数的累积切片估计方法、不依赖于分布条件的半参数降维方法和不依赖于模型的变量筛选方法，解决了充分降维领域“公开问题”，被认为是该领域“突破性进展”，被列为变量筛选领域“基准方法”。2. 在非线性大数据领域，提出投影相关系数度量非线性相关关系，广泛应用于类脑科学和天文学等研究中；原创性提出区间分位数相依基本思想，扩宽了（分布）独立基本概念并建立了（分布）独立与分位数独立和均值独立的联系。3.在大数据应用领域，主持开发的虚假诉讼预警甄别系统已经在四川省高级人民法院和成都市中级人民法院等10家法院部署应用示范，参与编写的人民法院信息化标准《民事案件信息技术规范》已被最高人民法院发布实施。

部分论文：

21、Yaowu Zhang, and Liping Zhu (2023).  Projective Independence Tests in High Dimensions: theCurses and the Cures. Biometrika. Accepted.

20、Wei Zhong, Chen Qian, Wanjun Liu, Liping Zhu and Runze Li (2023). Feature screening for interval-valued response with application to study association between posted salary and required skill. Journal of the American Statistical Association. Accepted.

19、Runze Li, Kai Xu, Yeqing Zhou, and Liping Zhu (2023). Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative Covariances. Journal of the American Statistical Association. 118:543,2184-2194.

18、Tingyou Zhou, Liping Zhu, Runze Li and Chen Xu (2019): Model-free forward regression via cumulative divergence. Journal of the American Statistical Association. 115:531,1393-1405.

17、Shujie MA, Liping ZHU, Zhiwei ZHANG, Chih-Ling TSAI and Raymond CARROLL (2019): A robust and efficient approach to causal inference based on sparse sufficient dimension reduction, The Annals of Statistics. 47(3): 1505-1535.

16、 Liping ZHU，Yaowu ZHANG and Kai XU（2018）：Measuring and testing for interval quantile dependence. The Annals of Statistics 46(6A): 2683-2710

15、Liping ZHU, Kai XU, Runze LI and Wei ZHONG (2017): Projection correlation between two random vectors. Biometrika. 104(4): 829-843

14、 Xingdong FENG and Li-Ping ZHU（2016）：Estimation and testing of varying coefficients in quantile regression. Journal of the American Statistical Association. 111(513)：266-274

13、 Kelin XU, Wenshen GUO, Momiao XIONG, Li-Ping ZHU and Li JIN（2016）：An estimating equation approach to dimension reduction in longitudinal data. Biometrika. 103 (1)：189-203

12、 Yanyuan MA and Li-Ping ZHU (2014). On estimation efficiency of the central mean subspace.Journal of the Royal Statistical Society, Series B. 76(5) 885-901

11、 Yanyuan MA and Li-Ping ZHU (2013). Efficient estimation in sufficient dimension reduction. The Annals of Statistics. 41(1) 250-268

10、 Yanyuan MA and Li-Ping ZHU (2013). Efficiency loss and the linearity condition in dimension reduction.Biometrika. 100(2) 371-383

09、 Zhou YU, Li-Ping ZHU, Heng PENG and Li-Xing ZHU（2013）：Dimension reduction and predictor selection in semiparametric models. Biometrika. 100(3)：641-654

08、 Yanyuan MA and Li-Ping ZHU (2013). Doubly robust and efficient estimators for heteroscedastic partially linear single-index model allowing high-dimensional covariates.Journal of the Royal Statistical Society Series B. 75(2) 305-322.

07、 Runze LI, Wei ZHONG and Li-Ping ZHU (2012). Feature screening via distance correlation learning.Journal of the American Statistical Association. 107(499), 1129-1139.

06、 Yanyuan MA and Li-Ping ZHU (2012). A Semiparametric Approach to Dimension Reduction.Journal of the American Statistical Association, 107(497), 168-179.

05、 Li-Ping ZHU, Lexin LI, Runze LI and Lixing ZHU (2011). Model-free feature screening for ultrahigh dimensional data.Journal of the American Statistical Association. 106(496). 1464-1475.

04、 Le-Xin LI, Li-Ping ZHU and Lixing ZHU (2011). Inference on primary parameter of interest with aid of dimension reduction estimation.Journal of the Royal Statistical Society, Series B, 73(1) 59-80.

03、 Li-Ping ZHU, Tao WANG, Lixing ZHU and Louis FERRE (2010). Sufficient dimension reduction through discretization-expectation estimation. Biometrika, 97(2), 295-304.

02、 Li-Ping ZHU, Lixing ZHU and Zhenghui FENG (2010). Dimension reduction in regressions through cumulative slicing estimation.Journal of the American Statistical Association, 105(492) 1455-1466.

01、 Li-Ping ZHU and Lixing ZHU (2009). On distribution-weighted partial least squares with diverging number of highly correlated predictors.Journal of the Royal Statistical Society, Series B. 71(2), 525-548.