About Me

I am now a tenure-track associate professor at Huazhong University of Science & Technology (HUST), School of Electronic Information and Communications (EIC). 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

• [Oct 2024] One paper on efficient high-dimensional approximate nearest neighbor (ANN) search accepted at SIGMOD'2024, check out our paper here!
• [Oct 2024] One paper on efficient non-uniform DNN quantization without backpropagation accepted at MLSP'2024, check out our paper here!
• [Oct 2024] Short course at IMT Toulouse, France, within the CIMI “Beyond classical regimes in statistical inference and machine learning” thematic trimester. See slides here: Part 1 and Part 2.
• [Oct 2024] I will be talk on “Understanding and Scaling Large and Deep Neural Networks or Random Matrix Theory for Extremely Large-Scale ML” at Shanghai Jiao Tong University. See slides here (please use Adobe Reader for the animation).
• [Aug 2024] I will be serving as an Area Chair of ICLR 2025.
• [Jun 2024] One paper at EUSIPCO'2024 on a novel and efficient RMT-improved Direction of Arrival estimation method. We show that standard ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) approach is biased in the case of large arrays, but can be effectively debiased using RMT. The paper is listed as a Best Student Paper Candidate of EUSIPCO'2024! Check our preprint here.
• [May 2024] One paper at ICML'2024 on RMT for Deep Equilibrium Model (DEQ, a typical implicit NN model) that provides very explicit connections between implicit and explicit NNs. Given a DEQ, check our preprint here for the recipe to design an “equivalent” simple explicit NN!
• I will be visiting the Centre de recherches mathématiques (CRM), Université de Montréal, Canada, as a CRM-Simons Scholar, during the thematic program “Mathematical Foundations of Data Science” in June 2025!
• I will be visiting the Centre International de Mathématiques et Informatique de Toulouse (CIMI), Toulouse, France, as a visiting professor, during the thematic trimester “beyond classical regimes in statistical inference and machine learning” in July 2024!
• [Jan 2023] I’m very grateful to be supported by the National Natural Science Foundation of China (youth program) on my research on the Fundamental Limit of Pruning Deep Neural Network Models via Random Matrix Methods.
• [Aug 2022] 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.
• [Jun 2022] Our book “Random Matrix Methods for Machine Learning” with Cambridge University Press, see more details here!

For more information please refer to my detailed CV.

Contact Me

E-mail: zhenyu_liao at hust.edu.cn