معرفی
Kuan Fang is an Assistant Professor of Computer Science at Cornell University, specializing in robotics, machine learning, and computer vision. His research focuses on enabling robots to perform complex tasks in unstructured environments through deep learning-based perception and control systems. Previously, he was a postdoc at UC Berkeley under Sergey Levine and earned his Ph.D. and M.S. from Stanford University under Fei-Fei Li and Silvio Savarese, with a B.S. from Tsinghua University. He has also worked at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia.
Education:
- Ph.D. & M.S., Computer Science, Stanford University
- Bachelor's Degree, Tsinghua University
Research Interests:
- Robot manipulation and control
- Reinforcement learning and policy optimization
- Robot perception and vision-language integration
- Generalization in robotics across tasks, environments, and robots
- Open-world robotic systems leveraging large-scale data
Teaching:
- CS 6758: Deep Learning for Robotics (Fall 2024)
- CS 4756: Robot Learning (Spring 2025)
Lab & Collaborations:
His lab at Cornell develops scalable algorithms and systems for robotic perception and control, emphasizing data-driven methods. Notable work includes ReLIC for interlimb coordination, GLIDE for bimanual manipulation, and TRA for compositional task execution. He collaborates with institutions like Boston Dynamics AI Institute and UC Berkeley.
