About Me

I am now an 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 Michael Mahoney. I received my Ph.D. from CentraleSupélec, University Paris-Saclay, in 2019, where I worked with Romain Couillet and 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. Click here for my headshot and here for a short bio in Chinese.

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

• [Jun 2025] 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.”
• [Aug 2025] I will be attending the Log-gases in Caeli Australi - Recent Developments in and around Random MATRIX Theory research program, at MATRIX institute of Creswick, Australia, during 4th - 15th August, 2025.
• [Jan 2025] I will be talking on “Examples and Counterexamples of Gaussian Universality in Large-dimensional Machine Learning” at RMTA 2024 Conference, Changchun, Jilin, China. See slides here.
• [Jan 2025] I am serving as an Associate Editor for Springer Statistics and Computing.
• [Dec 2024] I will be serving as an Area Chair for IJCNN 2025.
• [Nov 2024] I will be serving as an Area Chair for ICML 2025.
• [Oct 2024] Our work on communication-efficient and robust federated domain adaptation using random features technique accepted at IEEE Trans. KDE, check out our preprint here and code here!
• [Oct 2024] Our work on efficient high-dimensional approximate nearest neighbor (ANN) search accepted at SIGMOD'2025, check out our preprint here and code [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.
• [Aug 2024] I will be serving as an Area Chair for 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 the extended version 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 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!

Contact Me

E-mail: zhenyu_liao at hust.edu.cn