Chris Zhang
I am currently a 5th year PhD student in the Machine Learning Group at the University of Toronto, advised by Raquel Urtasun. Prior to this, I obtained my BASc in Systems Design Engineering at the University of Waterloo.
I am also currently a Research Scientist at the self-driving startup Waabi. Here I research learning-based driving policies, with applications in behavior simulation and autonomy.
My goal is to develop safe and intelligent agents for the real world. Key research questions I’m interested in are:
- What is the right data engine to enable scalable learning?
- What learning algorithms enable effective scaling with compute?
- How can we guarantee, test or validate the safety and performance agents we deploy?
publications
- SceneControl: Diffusion for Controllable Traffic Scene GenerationIn International Conference on Robotics and Automation (ICRA) , 2024
Use guided sampling to generate realistic, constraint-satisfying traffic initialization scenes.
- Towards Scalable Coverage-Based Testing of Autonomous VehiclesIn 7th Annual Conference on Robot Learning (CoRL) , 2023
Formulate structured scenario-based testing as a level-set estimation problem and use Gaussian Processes to obtain probabilistic coverage estimates.
- Graph HyperNetworks for Neural Architecture SearchIn International Conference on Learning Representations (ICLR) , 2019
Speed up architecture search by learning to predict the weights of a candidate architecture by message passing along its computation graph.
- Efficient Convolutions for Real-time Semantic Segmentation of 3d Point CloudsIn 2018 International Conference on 3D Vision (3DV) , 2018
Replace 3D convolutions with 2D convolutions in birds eye view for more efficient semantic segmentation.