Time: 10:00-11:00 , Sept .28
Location: SIST 1A 200
Host: Ziping Zhao
From a school of fish to a biological neural network with billions of neurons, the nature shows the power of connections. What would it take to allow machines to understand complex, implicit social connections in human society, and even build their own connections?
In this talk, we introduce graph learning as a general approach to model and uncover implicit connections. Based on the emerging unrolling techniques, we consider a graph learning framework that leverages both mathematical designs and end-to-end learning ability. We further talk about its applications to autonomous systems. For multi-agent perception, we learn a communication graph that can coordinate multiple agents to strategically collaborate with each other and better perceive a shared scene. For multi-agent prediction, we learn an interaction graph that captures the underlying social connections of multiple agents, promoting more precise and interpretable behavior prediction.
Siheng Chen is a tenure-track associate professor of Shanghai Jiao Tong University and co-PI at Shanghai AI laboratory. He received his doctorate from Carnegie Mellon University. His research interests include graph machine learning and collective intelligence. Dr. Chen's work on sampling theory of graph data received the 2018 IEEE Signal Processing Society Young Author Best Paper Award. His co-authored paper on structural health monitoring received ASME SHM/NDE 2020 Best Journal Paper Runner-Up Award and another paper on 3D point cloud processing received the Best Student Paper Award at 2018 IEEE Global Conference on Signal and Information Processing. His technique on joint perception and prediction was applied on all the UBER's autonomous cars. Dr. Chen also contributed to the project of scene-aware interaction, winning MERL President's Award.