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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LinkedIn  / 
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Biography
I am a fourth-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 Vector Institute and Waabi. 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 and Prof. Gongshen Liu.
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.
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Education
University of Toronto, Canada
Ph.D. in Computer Science
Sept 2019 to Present
Advisors: Professors Raquel Urtasun and Richard Zemel
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Shanghai Jiao Tong University, China
B.S. in Engineering
Sept 2015 to June 2019
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Research
UniSim: A Neural Closed-Loop Sensor Simulator
Ze Yang*, Yun Chen*, Jingkang Wang*, Sivabalan Manivasagam*, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Urtasun
Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Highlight)
[paper]
[video]
[website]
Learning Compact Representations for LiDAR Completion and Generation
Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, and Raquel Urtasun
Conference on Computer Vision and Pattern Recognition (CVPR), 2023
[paper]
[video]
[website]
NeuSim: Reconstructing Objects in-the-wild for Realistic Sensor Simulation
Ze Yang, Sivabalan Manivasagam, Yun Chen, Jingkang Wang, Rui Hu and Raquel Urtasun
IEEE International Conference on Robotics and Automation (ICRA), 2023
[paper]
[video]
[website]
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Simulation
Jingkang Wang, Sivabalan Manivasagam, Yun Chen, Ze Yang, Ioan Andrei Bârsan, Anqi Yang, Wei-Chiu Ma and Raquel Urtasun
Conference on Robot Learning (CoRL), 2022
[paper]
[video]
[website]
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]
[poster]
[video]
[website]
Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang*, Tianyun Zhang*, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad and Bo Li
Advances in Neural Information Processing Systems (NeurIPS), 2021
[paper]
[code]
[poster]
[website]
Policy Learning Using Weak Supervision
Jingkang Wang*, Hongyi Guo*, Zhaowei Zhu and Yang Liu
Advances in Neural Information Processing Systems (NeurIPS), 2021
[paper]
[code]
[poster]
[website]
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
Conference on Robot Learning (CoRL), 2021
[paper]
Adversarial Attacks on Multi-Agent Communication
James Tu*, Tsunhsuan Wang*, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren and Raquel Urtasun
International Conference on Computer Vision (ICCV), 2021
[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]
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]
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]
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Experience
Waabi Innovation Inc.
Research Scientist
Advisor: Professor Raquel Urtasun
Research Focus: Automating Sensor Simulation for Self-Driving
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Uber-ATG Toronto
Research Scientist
Advisor: Professor Raquel Urtasun
Research Focus: Automating the Training & Testing for Self-Driving
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Ant Financial, Alibaba Group
Research Intern
Advisor: Professor Le Song
Research Focus: Trustworthy Machine Learning
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University of Illinois at Urbana-Champaign
Research Intern at CSD*   (* = remote work)
Advisor: Professor Bo Li
Research Focus: Robust Reinforcement Learning
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University of California, Berkeley
Research Intern at BAIR Lab*   (* = remote work)
Advisor: Professors Bo Li and Dawn Song
Research Focus: Trustworthy Machine Learning
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Machine Vision and Intelligence Group (MVIG)
Research Assistant
Advisor: Professor Cewu Lu
Research Focus: Computer Vision; Autonomous Driving
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Honors & Awards
Outstanding Reviewer, CVPR21
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)
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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]
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Service
Conference reviewer: NeurIPS 2022, CoRL 2022, ICCV 2021-2023, CVPR 2021-2023, ECCV 2022, ICRA 2023, IROS 2023, AutoML 2023, EMNLP 2021, ACL 2021-2022, NAACL 2022, KDD 2020, ICPAI 2020
Journal reviewer: IEEE TNNLS, CVIU, IEEE TSP, RA-L
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Updated by Oct 14th, 2021.
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