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— Faculty —

LI, Zhaoyuan

Title:

Assistant Professor

Education Background

Ph.D (The University of Hong Kong)

MS (Renmin University of China)

BS (Xi’an Jiaotong University)

Research Field
High-dimensional statistics; Random matrix theory; Applications of statistical methods; Machine learning; Financial Technology
Personal Website

http://www.lizhaoyuan.com

Email

lizhaoyuan@cuhk.edu.cn

Biography:


 Zhaoyuan Li received her BS from Xi’an Jiaotong University in 2010, MS from Renmin University of China, and Ph.D. from The University of Hong Kong in 2016. Her research interests include random matrix theory, high-dimensional statistics, machine learning and interdisciplinary research. Her research works has been published in Journal of the Royal Statistical Society Series B (Statistical Methodology) (top journal in statistics), Nucleic Acids Research (top journal in biochemistry) and etc. She has been a post-doctoral fellow in finance at University of Hong Kong, and a part-time lecturer at HKU’s Faculty of Business and Economics for the new course “Machine learning and artificial intelligence in finance”, which is developed by her independently for the master of finance program at HKU. She was awarded the teaching award in 2014 and 2016 at HKU.


Academic Publications:


[1] D. Passemier, Z. Y. Li and J. Yao, “On estimation of the noise variance in high-dimensional probabilistic principal component analysis,” Journal of the Royal Statistical Society Series B (Statistical Methodology), 79(1), 51-67, 2017.

[2] Z. Y. Li and J. Yao, “Testing for heteroscedasticity in high dimensional regressions,” accepted at Econometrics and Statistics, 2018.

[3] Z. Y. Li and M. Tian, “A new method for dynamic clustering based on spectral analysis,” Computational Economics, 50(3), 373-392, 2017.

[4] Z. Y. Li and J. Yao, “On two simple and effective procedures for high-dimensional classification of general populations,” Statistical Papers, 57(2), 381-405, 2016.

[5] H. Yalamanchili, Z. Y. Li, P. Wang, M. Wong, J. Yao and J. Wang, “SpliceNet: recovering splicing isoform specific differential gene networks from RNA-Seq data of normal and diseased samples,” Nucleic Acids Research, 42(15), e121, 2014.

[6] Z. Y. Li, S. Liu and M. Tian, “Momentum effect differs across stock performances: Chinese evidence,” Acta Mathematicae Applicatae Sinica, English Series, 30(2), 279-288, 2014.