Associate Professor Department of Statistics and Data Science
Dr Li is currently an associate professor in the Department of Statistics and Data Science, Southern University of Science and Technology. Previously she was a postdoctoral fellow in the Department of Statistics at the Pennsylvania State University. Dr. Li obtained her Ph.D. degree from the Department of Statistics and Actuarial Science at the University of Hong Kong. Dr. Li’s research covers random matrix theory and high dimensional statistics. She has published papers in top statistical journals.
Personal Profile
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, Journal of the American Statistical Association, The Annals of Statistics, Biometrika, IEEE Transactions on Signal Processing Journal of Multivariate Analysis, IISE Transactions
Opennings:
Postdoc/Research Associate Positions are available. Applicants with strong background in probability/High dimensional statistics/Random Matrix Theory are particularly welcomed
Research
Random Matrix Theory
High dimensional Statistics
Machine Learning
Teaching
Statistical Calculation and Software(Fall, UG)
High Dimensional Statistics(Spring, PG)
Publications Read More
- Zeng Li, Cheng Wang*, Qinwen Wang (2023). On eigenvalues of a high-dimensional Kendall’s rank correlation matrix with dependence. Science China Mathematics, 66, 2615-2640.
- Xuanzhe Xiao, Zeng Li*, Chuanlong Xie, Fengwei Zhou (2023). Heavy-tailed regularization of weight matrices in deep neural networks. 32nd International Conferences on Artificial Neural Networks (ICANN), Sep 2023.
- Jiaxin Qiu, Zeng Li*, Jianfeng Yao (2023). Asymptotic normality for eigenvalue statistics of a general sample covariance matrix when p/n-> infinity and applications. The Annals of Statistics, 51(3), 1427-1451.
- Zhanting Long, Zeng Li*, Ruitao Lin, Jiaxin Qiu (2023). On singular values of large dimensional lag-tau sample auto-correlation matrices. Journal of Multivariate Analysis, 197, 105205.
- Zhehan Kan, Shuoshuo Chen, Zeng Li, Zhihai He *(2022). Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation, Proceedings of the 17th European Conference on Computer Vision (ECCV), 2022.
- Zeng Li, Qinwen Wang*, Runze Li (2021). CLT for linear spectral statistics of large dimensional Kendall rank correlation matrices, The Annals of Statistics, 49(3), 1569-1593.
- 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.
- 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.
- Zeng Li, Jianfeng Yao*, Clifford Lam, Qiwei Yao (2019). On testing for high-dimensional white noise, The Annals of Statistics, 47(6), 3382-3412.
- 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.
- 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.
- 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.
- Zeng Li, Guangming Pan, Jianfeng Yao* (2015). On singular value distribution of large-dimensional auto-covariance matrices, Journal of Multivariate Analysis, 137, 119-140.
- 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.
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At present, the department has an undergraduate program as well as two graduate programs(M.Phil and PhD). The department is in the process of developing a Major Program in Data Science and Big Data Technology. In 2019, the first batch of 38 undergraduate students in Statistics received the bachelor degree from SUSTech. 5 M.Phil students in Statistics also graduated in the same year. There are currently 9 M.Phil students, 7 PhD students in Statistics and 2 Postdoctoral Fellows. As the department grows in size and strength, the number of graduate students is expected to rise substantially from the current level and the areas of graduate studies will also expand to include Data Science and Big Data Technology.
This is the age of big data which offers exciting opportunities to researchers in Statistics and Data Science. Our department is expected to grow rapidly in the coming years. We invite outstanding scholars to join us in our journey to become a first-class department in Statistics and Data Science. We also welcome brilliant undergraduate and graduate students to apply for our programs. At the same time, we actively recruit excellent postdoctoral candidates.