LI Zeng
Associate Professor

Research Interest:

  • Random Matrix Theory and Applications in High dimensional Statistics

  • Time Series Analysis

  • Change-point detection


Professional Experience:

Jan 2021-present, Department of Statistics and Data Science, Southern University of Science and Technology, Associate Professor

Aug 2019-Dec 2020, Department of Statistics and Data Science, Southern University of Science and Technology, Assistant Professor

Oct 2017-Aug 2019, Department of Statistics, Pennsylvania State University Eberly Postdoc Fellow, mentor: Prof. Runze Li

Apr 2017-Aug 2017, Department of Statistics, University of Washington, Seattle,Research Assistant, mentor: Dr. Fang Han

Sep 2012- Mar 2017, Department of Statistics and Actuarial Science, HKU, Teaching Assistant


Educational Background:

Apr 2017, The Unviersity of Hong Kong (HKU), Ph.D., Department of Statistics and Actuarial Science Advisor: Prof. Jianfeng Yao

Sep 2012, Renmin University of China, Beijing (RUC) M.Sc., School of Statistics

Sep 2009, Beijing Normal University, Beijing (BNU), B.Sc., School of Mathematical Science


Awards and Honors:

Sep 2012- Mar 2017, Department of Statistics And Actuarial Science, HKU Excellent Teaching Assistant Award (5 times)


Academic Services:

Referee Service. Journal of the Royal Statistical Society: Series B Biometrika, IEEE Transactions on Signal Processing Journal of Multivariate Analysis, IISE Transactions


Selected Publications:

[1] Zeng Li, Qinwen Wang, Runze Li (2021). Central limit theorem for linear spectral statistics of large dimensional Kendall's rank correlation matrices and its applications, The Annals of Statistics, to appear.

[2] Zeng Li, Qinwen Wang, Chuanlong Xie, (2021). Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-norm Least Squares Estimator, in International Conference on Machine Learning (ICML), May 2021

[3]Zeng Li, Fang Han, Jianfeng Yao (2020). Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model, The Annals of Statistics, 48(6), 3138-3160.

[4] Zeng Li, Jianfeng Yao, Clifford Lam, Qiwei Yao (2019). On testing for high-dimensional white noise, The Annals of Statistics, 47(6), 3382-3412.

[5] Weiming Li, Zeng Li, Jianfeng Yao (2018). Joint CLT for linear spectral statistics of dependent large dimensional sample covariance matrices, Scandinavian Journal of Statistics, 45(3), 699-728.

[6] Zeng Li, Qinwen Wang, Jianfeng Yao (2017). Identifying number of factors from singular values of lagged sample auto-covariance matrix, The Annals of Statistics, 45(1), 257-288.

[7] Zeng Li, Jianfeng Yao (2016). Testing the sphericity of a covariance matrix when the dimension is much larger than the sample size, Electronic Journal of Statistics, 10(2), 2973-3010.

[8] Zeng Li, Guangming Pan, Jianfeng Yao (2015). On singular value distribution of large-dimensional auto-covariance matrices, Journal of Multivariate Analysis, 137, 119-140.

[9] Chao Yu, Yue Fang, Zeng Li, Bo Zhang, Xujie Zhao (2014). Nonparametric estimation of high frequency spot volatility for Brownian semimartingale with jumps, Journal of Time Series Analysis, 35(6), 572-591.