Towards robust edge intelligence in autonomous systems

Guest Info

Hang Qiu is an assistant professor of ECE and CSE at the University of California, Riverside. Previously, he was a postdoctoral scholar in the Platform Lab at Stanford University, a software engineer at Waymo LLC. He received his Ph.D. from the Department of Electrical and Computer Engineering at the University of Southern California and his Bachelor’s degree from Shanghai Jiao Tong University. His research focus is on networked cyber-physical systems and edge ML systems. His work draws upon theories and methods from machine learning, wireless networking, computer vision, and robotics to build robust and cooperative intelligence in edge autonomous systems. He is a recipient of a recipient of MLSys Outstanding Paper Award, ACM Mobisys Best Paper Runner-up Award, a Qualcomm Innovation Fellowship Finalist, an Outstanding Winner of COMAP ICM.


Using advanced 3D sensors and sophisticated deep learning models, autonomous systems such as self-driving cars, delivery drones are already transforming our daily life. However, a significant remaining challenge for further advancement is the reliability, robustness, and the ability to anticipate and handle long-tail events and corner-cases. Humans, on the other hand, are extremely good at handling corner-cases, so if autonomous systems are to be widely accepted, they must achieve human-level reliability. In this talk, I will present a series of systems research, through the lens of autonomous cars, that leverages cooperative autonomy and edge ML ecosystems to enhance the robustness across the autonomous systems stack.