Note: This Seminar Will Be Delivered Via Zoom

“Graph-structured data science for autonomous driving”
Thursday, April 2 @ 1:00 pm
Zoom Link
Meeting ID: 698 868 493

Abstract

As one of the most exciting engineering projects in the modern world, autonomous driving is an aspiration for many researchers and engineers across generations. It is a goal that might fundamentally redefine the future of human society and everyone’s daily life. Although there has been significant progress across many groups in industry and academia, there is still a lot of challenges to be tackled, which require efforts from both industry and academia. From a data-science perspective, many seemingly different challenges share a similar pattern: data are naturally associated with irregular structures, which cannot be directly handled by traditional methodologies. The necessity of analyzing such data call for graph-based methodologies.

In this talk, we will introduce a series of graph-based methodologies, including sampling theory of graph data and graph neural networks. We will illustrate the power of the proposed methodologies with the applications to 3D point cloud processing, human behavior understanding and future trajectory prediction in autonomous driving.

Biography

Siheng Chen is a research scientist at Mitsubishi Electric Research Laboratories. Before that, he was an autonomy engineer at Uber Advanced Technologies Group, working on the perception and prediction systems of self-driving cars. Before joining Uber, Dr. Chen was a postdoctoral research associate at Carnegie Mellon University. Dr. Chen received his doctorate in Electrical and Computer Engineering from Carnegie Mellon University in 2016, where he also received two master degrees in Electrical and Computer Engineering (College of Engineering) and Machine Learning (School of Computer Science), respectively. He received his bachelor’s degree in Electronics Engineering in 2011 from Beijing Institute of Technology, China. He is the recipient of the 2018 IEEE Signal Processing Society Young Author Best Paper Award. His co-authored paper received the Best Student Paper Award at 2018 IEEE Global Conference on Signal and Information Processing. His research interests include signal processing, machine learning, 3D point cloud processing, and autonomous driving.