Curriculum vitae

Dr. Zhu received his BS degree in 2001 and PhD degree in 2006 from East China Normal University. He is now Distinguished Professor at Renmin University of China, and Associate Dean at Institute of Statistics and Big Data.

Dr. Zhu’s research interest is in Theories, Methodologies and Algorithms for Complex Data Analysis. To deal with high dimensional data, he (and his coauthors) suggested a class of sufficient dimension reduction methods which are completely free of tuning parameters, and proposed a class of semiparametric dimension reduction methods which completely remove the longstanding stringent and annoying distributional assumptions on the covariates. He (and his coauthors) also derived the semiparametric efficiency bound for sufficient dimension reduction and suggested an efficient dimension reduction method which attains the semiparametric efficiency bound. To deal with nonlinearly dependent data, he (and his coauthors) suggested projection correlation to characterize the degree of nonlinear dependence between two random vectors, and introduced the concept of interval quantile independence, which generalizes the notion of distributional independence in classic statistics textbooks and bridges the gap between quantile independence and distributional independence.

Dr. Zhu served as the AE of The Annals of Statistics, Statistica Sinica. He is now the AE of international statistical journals such as Journal of Multivariate Analysis, Statistics and Its Interface and Statistical Analysis and Data Mining, and domestic statistical journals such as Journal of Systems Science and Complexity, 系统科学与数学and 应用概率统计. He is also the Field Chief Editor of the interdisciplinary journal Statistics, Optimization and Computer Science.

Selected Publications from over 70 Papers:

19、Tingyou Zhou, Liping Zhu, Runze Li and Chen Xu (2019): Model-free forward regression via cumulative divergence. Journal of the American Statistical Association. To appear

18、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, To appear.

17、Jian HUANG, Yuling JIAO, Xiliang LU and Liping ZHU (2018): Robust decoding from 1-bit compressive sampling with ordinary and regularized least squares. SIAM Journal of Scientific Computing, 40(4), 2062-2086

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.