Hoi-To Wai @ CUHK

To 

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

We are organizing the One World Signal Processing Seminar, with virtual seminars on Distributed Signal Processing, Data Science and Communications. Please subscribe to our mailing list!

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.

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:

  • Network science and graph learning problem

  • Optimization theory applied to machine learning and distributed signal processing

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

  • January, 2021: With Prof. Usman Khan (Tufts), we are organizing a special track on Signal Processing for Self-Aware and Social Autonomous Systems at the inaugural IEEE-ICAS in Montreal this August! The deadline of submission is March 3, 2021.

  • December, 2020: New paper Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models (with Ryan, Anna, Nurullah, Kari and Ken) accepted at AAAI 2021 (preprints available soon).

  • September, 2020: Two papers accepted at NeurIPS 2020:

    • Provably Efficient Neural GTD for Off-Policy Learning (with Zhuoran, Zhaoran, Mingyi) pre-proceeding.

    • A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm (with Gersende, Eric) pre-proceeding.

NeurIPS