Jingkang Wang

Jingkang Wang      

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

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

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Biography

I am a second-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 affiliated with Vector Institute. From 2019-2021, I was also a research scientist at Uber-ATG Toronto, working with Prof. Raquel Urtasun. 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

    AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
    Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas,
    Mengye Ren and Raquel Urtasun
    Conference on Computer Vision and Pattern Recognition (CVPR), 2021
    [paper] [video]

    Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
    Sean Segal*, Nishanth Kumar*, Sergio Casas, Wenyuan Zeng, Mengye Ren, Jingkang Wang, Raquel Urtasun
    arXiv preprint arXiv:2104.03956
    [paper]

    Adversarial Attacks on Multi-Agent Communication
    James Tu*, Tsunhsuan Wang*, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren and Raquel Urtasun
    arXiv preprint arXiv:2101.06560
    [paper] [video]

    Learning to Communicate and Correct Pose Errors
    Nicholas Vadivelu, Mengye Ren, James Tu, Jingkang Wang and Raquel Urtasun
    Conference on Robot Learning (CoRL), 2020
    [paper] [video]

    Cost-Efficient Online Hyperparameter Optimization
    Jingkang Wang*, Mengye Ren*, Ilija Bogunovic, Yuwen Xiong and Raquel Urtasun
    International Conference on Machine Learning (ICML), RealML Workshop, 2020
    arXiv preprint arXiv:2101.06590
    [paper] [video]

    Policy Learning Using Weak Supervision
    Jingkang Wang*, Hongyi Guo*, Zhaowei Zhu and Yang Liu
    Advances in Neural Information Processing Systems (NeurIPS), DeepRL and RWRL Workshops, 2020
    arXiv preprint arXiv:2010.01748
    [paper] [code]

    Is Robust Neurons’ Activation Sufficient to Robustify CNNs against Adversarial Attacks?
    Jingkang Wang*, Gaoyuan Zhang* and Sijia Liu
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), AdvML Workshop, 2020

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

    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
    [paper] [code]

    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.
    [paper] [code]

    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]


    Experience

    Uber-ATG Toronto
    Research Scientist

  • Advisor: Professor Raquel Urtasun
  • Research Focus: Automating the Training & Testing for Self-Driving


  • 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

  • On the Importance of Initialization and Momentum in Deep Learning. CSC2541 Neural Net Training Dynamics. 2021/03. [slides]
  • Physics-based Differentiable Rendering. Reading group. 2021/03. [slides]
  • Differentiable Monte Carlo Ray Tracing through Edge Sampling. CSC2547 3D & Geometric Deep Learnig. 2021/02. [slides] [video][pptx]
  • Trust Region Policy Optimization (TRPO). CSC2621 Topics in Robotics. 2020/02. [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]

  • Service

  • Conference reviewer: ICCV 2021, ACL 2021, CVPR 2021, KDD 2020, ICPAI 2020


  • Updated by March 18th, 2021.

    Thanks jonbarron for this amazing work.