Yu LI 李渝


Ph.D. Candidate
Department of Computer Science and Engineering
The Chinese University of Hong Kong


Office: Rm1026, SHB, CHHK, Shatin, HongKong
Email: yuli@cse.cuhk.edu.hk
Google Scholar, DBLP


I am a final-year Ph.D. Candidate at The Chinese University of Hong Kong (Shatin, N.T., Hong Kong), under the supervision of Prof.Qiang Xu. I received my Master degree with ‘Cum Laude’ in 2017 from Katholieke Universiteit Leuven (KU Leuven, Leuven, Belgium). Before that, I received the B.Eng degree in 2016 from both KU Leuven and University of Electronic Science and Technology of China (UESTC, Chengdu, China).

Currently, my research interest includes AI security and AI testing.

news

Apr 30, 2021 Our paper “Information Bottleneck Approach to Spatial Attention Learning” has been accepted to IJCAI’21 with an acceptance rate of 13.9%.
Feb 26, 2021 Our paper “AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference” has been accepted to DAC’21 with an acceptance rate of 23%.

selected publications

  1. CCS’20
    DeepDyve: Dynamic Verification for Deep Neural Networks
    Li, Yu*, Li, Min*, Luo, Bo, Tian, Ye, and Xu, Qiang
    In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS) 2020
  2. ACSAC’19
    D2NN: A Fine-Grained Dual Modular Redundancy Framework for Deep Neural Networks
    Li, Yu, Liu, Yannan, Li, Min, Tian, Ye, Luo, Bo, and Xu, Qiang
    In Proceedings of the 35th Annual Computer Security Applications Conference (ACSAC) 2019
  3. DAC’21
    AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
    Li, Min, Li, Yu, Tian, Ye, Jiang, Li, and Xu, Qiang
    The Design Automation Conference (DAC) 2021
  4. IJCAI’21
    Information Bottleneck Approach to Spatial Attention Learning
    Lai, Qiuxia, Li, Yu, Zeng, Ailing, Liu, Minhao, Sun, Hanqiu, and Xu, Qiang
    International Joint Conference on Artificial Intelligence (IJCAI) 2021