Hoi-To Wai @ CUHK

Associate Professor Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Hong Kong Email: htwai at se dot cuhk dot edu dot hk Links: My CV, Department Profile, Google Scholar

About Me

I am an Associate Professor at the Department of Systems Engineering and Engineering Management in The Chinese University of Hong Kong (CUHK). I also received my B. Eng (with first class Honor) and M. Phil. from CUHK, working with Prof. Wing-kin (Ken) Ma in Spring 2010 and 2012, respectively. I received my PhD degree from Arizona State University in Fall 2017, working with Prof. Anna Scaglione. Currently, I am serving the editorial board of IEEE TSIPN and Elsevier's Signal Processing, as well as a member of the IEEE SPS's SPTM-TC and DSI. I am also an organizer for the DEGAS Webinar Series sponsored by the IEEE SPS.

Besides ASU and CUHK, I have held research positions at LIDS, MIT working with Prof. Asuman E. Ozdaglar, and at Télécom ParisTech, Ecole Polytechnique, working with Prof. Eric Moulines, and at UC Davis as a PhD student with Anna before transferring to ASU. I have received the 2017's Dean's Dissertation Award at ASU's Ira A. Fulton Schools of Engineering (Full Circle), and a Best Student Paper Award from IEEE ICASSP 2018 (paper) and IEEE SAM Workshop 2024 (as co-author, paper).

My current research focus is on:

• Graph signal processing and graph learning problem for network science

• Stochastic algorithms for machine learning and distributed signal processing

Check out the Research page for more details.

I am looking for mathematically inclined and motivated students/Postdoc interested in optimization theory for machine learning, reinforcement learning or graph signal processing problems. Please contact me if you are interested. Also consider applying for the HK PhD Fellowship Scheme which offers great support for studying in CUHK.

Recent News

• Feb., 2025: One paper accepted by IEEE Transactions on Automatic Control on Tighter Analysis for Decentralized Stochastic Gradient Method: Impact of Data Homogeneity (with Qiang)  —  using a new analysis technique based on high order smoothness, we show that DSGD has transient time that is on-par with faster SOTA variants such as gradient-tracking DSGD.

• Dec., 2024: Two papers accepted by IEEE ICASSP 2025.

• • Network Games Induced Prior for Graph Topology Learning (with Chenyue, Shangyuan and Anthony)  —  we propose to use the ‘social welfare’ as a metric to design an implicit prior for graph learning from very limited no. of graph signal samples.

• • A Two-timescale Primal-dual Algorithm for Decentralized Optimization with Compression (with Haoming and Oscar)  —  we propose a primal-dual algorithm that supports nonlinear compression (quantization) with fast convergence.

• Dec., 2024: I have visited UBC and given a talk on ’'Stochastic Approximation Algorithms with Decision-Dependent Data: The Case of Performative Prediction’’. Thanks Renjie for hosting me.

• Dec., 2024: I have been promoted to Associate Professor.

• Nov., 2024: I have joined the Editorial Board of IEEE TSP as an Associate Editor.

• Oct., 2024: Together with Eric and Gersende, we will deliver a tutorial at ICASSP 2025 in Hyderabad, India on A Deep Dive into Recent Advances in Stochastic Approximation. The tutorial will be based upon our overview paper on stochastic approximation, published at IEEE TSP in 2023.