Kuan Fang

Kuan Fang is an Assistant Professor in the Department 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 and scalable algorithms.

• Education: Ph.D. and M.S. in Computer Science from Stanford University, advised by Fei-Fei Li and Silvio Savarese
• Bachelor's degree from Tsinghua University
• Previous roles: Postdoc at UC Berkeley (advised by Sergey Levine), research experience at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia

His work emphasizes:

• Acquisition of versatile skills for visuomotor control via massive data learning
• Continuous robot improvement through autonomous data generation
• Boosting generalization by integrating prior knowledge across domains

Recent publications span topics in interlimb coordination, diffusion policy learning, visual prompting for reinforcement learning, and language-guided decomposition. While specific scientific awards aren't listed, his work appears in top robotics conferences including RSS, ICRA, IROS, and CoRL.

He actively mentors students and maintains collaborations with UC Berkeley, Boston Dynamics AI Institute, and Stanford researchers.