Jingkang Wang      

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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.

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

Shanghai Jiao Tong University, China
B.S. in Engineering

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]

Waabi Innovation Inc.
Research Scientist

Uber-ATG Toronto
Research Scientist

Ant Financial, Alibaba Group
Research Intern

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

University of California, Berkeley
Research Intern at BAIR Lab*   (* = remote work)

Machine Vision and Intelligence Group (MVIG)
Research Assistant

Thanks jonbarron for this amazing work.