Ngai-Man (Man) Cheung
Assistant Professor
Singapore University of Technology and Design (SUTD)

E-mail: ngaiman_cheung(at)sutd.edu.sg

Tel: +65 6499 4542

Building 1, Level 5, 1.502-17 
8 Somapah Road, Singapore 487372
Research Interest: Image and Video Processing/Analysis, Signal Processing/Classification, Multimedia Communication, Computer Vision


I received my Ph.D. degree in Electrical Engineering from 
University of Southern California (USC), Los Angeles, CA, in 2008. My Ph.D. research focused on image and video coding, and the work was supported in part by NASA-JPL.

From 2009-2011, I was a postdoctoral researcher with the Image, Video and Multimedia Systems group at Stanford University, Stanford, CA.

I have also held research positions with Texas Instruments Research Center Japan, Nokia Research Center, IBM T. J. Watson Research Center, HP Labs Japan, Hong Kong University of Science and Technology (HKUST), and Mitsubishi Electric Research Labs (MERL). My industrial work has resulted in 11 U.S. patents granted with several pending.

  • 7/2018: An improved GAN
    • Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung"Dist-GAN: An Improved GAN using Distance Constraints," in Proc. European Conference on Computer Vision (ECCV 2018) [PDF] [Supplementary]
  • 6/2018: Paper on multi-level similarity
    • Yiluan Guo, Ngai-Man Cheung“Efficient and Deep Person Re-Identification using Multi-Level Similarity,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR-18). [PDF]
  • 2018: Paper on deep neural network compression
    • Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, Pascal Frossard, “Adaptive Quantization for Deep Neural Network,” in Proc. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). [PDF]
  • 2017: Paper on sparse Graph Fourier Transform (GFT) for network traffic anomalies detection
    • Manas Khatua, Seyed Hamid Safavi, Ngai-Man Cheung, "Sparse Laplacian Component Analysis for Internet Traffic Anomalies Detection." IEEE Transactions on Signal and Information Processing over Networks, 2017. [PDF]