Jingkang Wang

Jingkang Wang      

PhD student
Machine Learning Group
Department of Computer Science
University of Toronto

Email:   wangjk (at) cs (dot) toronto (dot) edu

CV  /  LinkedIn  /  GitHub  /  Google Scholar


Biography

I am a first-year Ph.D. student in Machine Learning Group, CS Department, University of Toronto. My advisors are Prof. Raquel Urtasun and Prof. Richard Zemel. I am also part of Uber ATG and Vector Institute. I received my B.S. degree from Shanghai Jiao Tong University advised by Prof. Cewu Lu.

My research interests involve Machine Learning and Computer Vision. Prior to UofT, I spent a wonderful year working with Prof. Bo Li at UIUC as a research intern and did some works on trustworthy machine learning.


Education

University of Toronto, Canada
Ph.D. in Computer Science

  • Sept 2019 to Aug 2024 (Expected)
  • Advisors: Professors Raquel Urtasun and Richard Zemel
  • Shanghai Jiao Tong University, China
    B.S. in Engineering

  • Sept 2015 to June 2019

  • Research

    BabyAI++: Towards Grounded-Language Learning beyond Memorization
    Tianshi Cao*, Jingkang Wang*, Annie Zhang*, Sivabalan Manivasagam*
    International Conference on Learning Representations (ICLR), BeTR-RL Workshop, 2020

    Towards a Unified Min-Max Framework for Adversarial Exploration and Robustness
    Jingkang Wang*, Tianyun Zhang*, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad and Bo Li
    arXiv preprint arXiv:1906.03563

    Reinforcement Learning with Perturbed Rewards
    Jingkang Wang, Yang Liu and Bo Li
    AAAI Conference on Artificial Intelligence (AAAI), 2020 (Spotlight)
    [paper] [code]

    On the Impact of Perceptual Compression on Deep Learning
    Gerald Friedland, Ruoxi Jia, Jingkang Wang, Bo Li and Nathan Mundhenk.
    International Conference on Multimedia Information Processing and Retrieval (MIPR), 2020.

    Multiple Character Embeddings for Chinese Word Segmentation
    Jingkang Wang*, Jianing Zhou*, Jie Zhou and Gongshen Liu
    Annual Meeting of the Association for Computational Linguistics (ACL), Student Research Workshop, 2019 [paper] [code]

    LiDAR-Video Driving Dataset: Learning Driving Policies Effectively
    Yiping Chen*, Jingkang Wang*, Jonathan Li, Cewu Lu, Zhipeng Luo, Han Xue and Cheng Wang
    Conference on Computer Vision and Pattern Recognition (CVPR), 2018
    [paper] [code] [dataset]


    Research Internships

    Ant Financial, Alibaba Group
    Research Intern

  • Advisor: Professor Le Song
  • Research Focus: Trustworthy Machine Learning


  • University of Illinois at Urbana-Champaign
    Research Intern at CSD*   (* = remote work)

  • Advisor: Professor Bo Li
  • Research Focus: Robust Reinforcement Learning
  • University of California, Berkeley
    Research Intern at BAIR Lab*   (* = remote work)

  • Advisor: Professors Bo Li and Dawn Song
  • Research Focus: Trustworthy Machine Learning
  • Machine Vision and Intelligence Group (MVIG)
    Research Assistant

  • Advisor: Professor Cewu Lu
  • Research Focus: Computer Vision; Autonomous Driving

  • Honors & Awards

  • National Scholarships
  • Level-A SJTU Outstanding Scholarships
  • Excellent Bachelor Thesis (Top %1) of SJTU
  • Outstanding Undergraduate in Shanghai
  • First Prize in National College Student Information Security Contest
  • Meritorious Winner Prize in The Mathematical Contest in Modeling (MCM)
  • Second Prize in The Chinese Mathematics Competition (CMC, Shanghai)
  • Second Prize in National College Students Information Security Contest
  • First Prize in Chinese Mathematical Olympiad (CMO, 10th in Shanxi)

  • Talks & Presentations

  • Trust Region Policy Optimization (TRPO). CSC2621 Topics in Robotics. 2021/02. [slides]
  • Efficient Nonmyopic Active Search. CSC2547 Learning to Search. 2019/10. [slides]
  • Towards Secure and Interpretable Learning in Deep Neural Networks. Uber ATG. 2019/07. [slides]


  • Updated by Feb 28th, 2020.

    Thanks jonbarron for this amazing work.