Hoi-To Wai @ CUHK

Assistant 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 Assistant 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 on 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.

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).

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

• May, 2024: 2 papers on performative prediction and contrastive learning accepted at ICML 2024:

• • Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data (with Haitong and Qiang - it's a work Haitong did during his UG study in CUHK!)  —  we propose an effective derivative-free algorithm for tackling the performative risk optimization problem that works with Markovian (i.e., gradually shifted distributions) data.

• • EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence (with Oscar, Mingyi, Parameswaran and Soumajyoti)  —  we develop an SGD-like algorithm for contrastive learning over a large set of negative samples using MCMC for negative sampling.

• April, 2024: I have visited Hanyang University and gave a talk on Graph Learning with Low Pass Graph Signal Processing at Sunwoo Kim's group. I have also attended IEEE ICASSP 2024 in Seoul.

• April, 2024: 1 paper accepted at IEEE SAM 2024: On Detecting Low-pass Graph Signals under Partial Observations (with Sean)  —  it's an extension over our prior work for detecting if a set of graph signals is low pass or not without knowing the graph topology, now under partially observed nodes. I am also organizing a special session on ‘Recent Advances on Graph Signal Processing’.

• March, 2024: Two journal papers accepted:

  • “Blind graph matching using graph signals” at IEEE Transactions on Signal Processing (with Hang and Anna)

  • “Distributed Optimization With Personalization: A Flexible Algorithmic Framework” at IEEE Transactions on Automatic Control (with Yan, Jimmy, and others)