About Me

I am now a tenure-track assistant professor at Huazhong University of Science & Technology (HUST), School of Electronic Information and Communications (EIC), where I’m awarded the Wuhan Youth Talent and East Lake Youth Talent Fellowship 2021. Before that, I was a Postdoctoral Researcher at the University of California, Berkeley, Department of Statistics and ICSI, in 2020, hosted by Prof. Michael Mahoney. I received my Ph.D. from CentraleSupélec, University Paris-Saclay, in 2019, where I worked under the supervision of Prof. Romain Couillet and Prof. Yacine Chitour. I received my B.Sc degree in Optical and Electronic Information from Huazhong University of Science & Technology, China, in 2014, and my M.Sc. degree in Signal and Image Processing (ATSI) from University Paris-Saclay, France, in 2016. My research interests are broadly in (statistical) machine learning, signal processing, random matrix theory, and high-dimensional statistics.

Curriculum Vitae

Here is my CV in English and in Chinese.

My Erdős number is 4, via Romain Couillet, Zhidong Bai and Gutti Jogesh Babu.

News

• Happy to announce the 2022 Joint Workshop on “Mathematics for Data Science” between HUST and University Paris-Saclay taking place in Sep 22-23 2022 on zoom! Time: 9:00-12:00 Paris time and 15:00-18:00 Beijing Time. See the detailed schedule here and the playabck here (for Sep 22) and here (for Sep 23)!
• One paper at NeurIPS'2022 on the eigenspectral structure of Neural Tangent Kernel (NTK) of fully-connected Deep neural networks for Gaussian mixture input data, with a compelling application to “lossless” sparsification and quantization of DNN models! This extends our previous paper at ICLR'2022. See more details here.
• Invited talk on “Random Matrix Methods for Machine Learning: Lossless Compression of Large Neural Networks” at The China conference on Scientific Machine Learning (CSML 2022), see slides here.
• Upcoming book “Random Matrix Methods for Machine Learning” with Cambridge University Press, see more details here!
• One paper at ICLR'2022 on an incredibly efficient and theoretically guaranteed random feature compression technique, see more details here.
• One invited paper in Special Issue on Machine Learning 2021, JSTAT, see more details here.
• I’m very grateful to be supported by the CCF-Hikvision Open Fund on my research on the theory of neural network model compression (together with Dr. Kai Wan), see more details here.
• One oral paper (top 1% submission) at NeurIPS'2021 on the Hessian eigenspectra of generalized linear models, check our preprint here!
• One paper on “Sparse sketches with small inversion bias” accepted at COLT'2021, check our preprint here!
• Invited talk on “A Data-dependent Theory of Overparameterization: Phase Transition, Double Descent, and Beyond” at Workshop on the Theory of Over-parameterized Machine Learning (TOPML) 2021. See slides, two-page abstract, and paper.
• One paper on “kernel regression in high dimensions: refined analysis beyond double descent” at AISTATS'2021! Check our preprint here.
• A spotlight paper at ICLR'2021 on computationally efficient (sparse and quantized) spectral clustering! Check here.

For more information please refer to my detailed CV.

Contact Me

E-mail: zhenyu_liao at hust.edu.cn, or click zhenyu_liao@hust.edu.cn