Please note: This seminar will take place in DC 1304.
Jingkang Wang, PhD student, Machine Learning Group
Department of Computer Science, University of Toronto
AI empowers physical robots with intelligence to perceive, predict, plan, and act in dynamic real-world environments. Despite significant advances, modern autonomous systems remain fragile: they perform reliably only in controlled setups but fail to generalize to unstructured or safety-critical scenarios. This poses challenges for real-world deployment, particularly in domains like self-driving, where handling long-tail edge cases is critical. Existing solutions either depend on expensive, risky real-world data collection or on handcrafted simulators that lack diversity, realism, and scalability.
In this talk, I will present my efforts on scaling world simulators for safe physical intelligence. First, I develop methods that combine generative models, neural rendering, and physics priors to transform sparse sensor streams into scalable 4D world simulators. Then, I show how these simulators provide a foundation for safety by enabling closed-loop evaluation, counterfactual generation, and robust policy learning. These world simulators bridge the gap between simulation and reality for generalizable and trustworthy physical agents. Finally, I will discuss how these efforts point toward a broader vision: endowing physical agents with richer spatial intelligence so they can operate safely and reliably in complex real-world environments.
Bio: Jingkang Wang is a final-year Ph.D. student at Machine Learning Group, University of Toronto, working with Raquel Urtasun. He is also a researcher at Waabi, leading the World Models & Digital Twins team.
His research lies in the intersection of computer vision, robotics, and machine learning, with a focus on scalable and trustworthy 4D world simulators and their applications to self-driving vehicles. Previously, Jingkang was a research scientist at Uber ATG. He received his B.S. from Shanghai Jiao Tong University.