معرفی
Lin SHAO is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on developing algorithms and systems for robotic perception and manipulation, aiming to create general-purpose robotic systems capable of performing diverse tasks in varied environments. He holds a Ph.D. and M.S. from Stanford University, and a B.S. in Geochemistry from Nanjing University.
His educational background includes:
- Ph.D. (Stanford University, 2021)
- M.S. (Stanford University, 2017)
- B.S. in Geochemistry (Nanjing University, 2014)
Key research areas include robotic perception and manipulation, reinforcement learning, differentiable physics simulation, and large-scale robotic learning systems. His work emphasizes enabling robots to autonomously perceive and interact with their environment through advanced algorithms and simulation techniques. Notable contributions include the SAM-RL framework for model-based reinforcement learning and the SAGCI-System for sample-efficient robot learning.
He has received the Best System Paper Award Finalist at RSS 2023 and serves as a co-chair of the IEEE Robotics and Automation Society's Technical Committee on Robot Learning. Courses taught include CS2040S (Data Structures and Algorithms), CS4278/CS5478 (Intelligent Robots: Algorithms and Systems), and Knowledge@Computing workshops.
His research group actively explores topics such as robotic grasping, motion planning, and human-robot interaction, with applications in both industrial and service robotics domains.
