Yun Chen

Yun Chen

Researcher/PhD student



Yun Chen is a PhD student at Department of Computer Science, University of Toronto, advised by Prof. Raquel Urtasun. He also works as a researcher at

Before that, he worked as a research scientist at Uber ATG R&D led by Raquel Urtasun and worked closely with Shenlong Wang, Ming Liang and Bin Yang.

He is a follower of Unix philosophy, an advocator of Linux, a geek of Android, the author of a PyTorch best-seller, and open-source contributor.

He has set new state-of-the-art for several tasks (including Autonomy/NLP/Vision), also served as reviewer for CVPR, ICCV, ECCV, ICLR, NeurIPS, CoRL, ICRA, ACCV, WACV, TIP, TPAMI and RA-L.

  • Autonomous Driving
  • Computer Vision
  • Sensor Simulation
  • Linux
  • PhD in Computer Science, 2021-Now

    University of Toronto

  • MSc in Communication Eng., 2016-2019

    Beijing University of Posts and Telecommunications

  • BSc in Communication Engineering, 2012-2016

    Beijing University of Posts and Telecommunications

Research Publications


Jun 2021 – Present Toronto, Canada
Research focus on 3D reconstruction and sensor simulation, espeically Camera Simulation.
Research Scientist
Dec 2019 – Present Toronto, Canada

Working closely with Prof. Raquel Urtasun and Prof. Shenlong Wang on 3D simulation.

  • 3D Reconstruction with large-scale weakly-labelled data in the wild
  • Photorealistic Image Simulation with Geometry-Aware Composition for Self-Driving
AI Resident
Sep 2018 – Dec 2019 Toronto, Canada

Working closely with Ming Liang and Bin Yang in ATG R&D for 3D Perception tasks.

  • Depth Completion: Densify LiDAR with image guidance, SOTA in KITTI
  • 3D Perception: 3D detection, tracking with multi-sensor. New SOTA in KITTI
  • Map structure learning with graph neural network. New SOTA in Argoverse motion forecasting
Research Intern
Mar 2018 – Jul 2016 Beijing, China

Working on Medical Imaging in Machine Intelligence Group led by Dr. Xian-Sheng Hua

  • Developed 3D R-CNN for CT/MRI, faster and more accurate than radiologist.
  • Explored weakly-supervised learning with limited labels and active learning for efficient labelling.