Tse-Kai (Kevin) Chan

MSCS Student @ Stanford

About Me

Hi, I’m Kevin. I’m a first-year M.S. in Computer Sceince student at Stanford University, specializing in Artificial Intelligence, and a ML researcher at Stanford Artificial Intelligence Laboratory (SAIL).

Previously, I received my B.S. in Computer Science from UC San Diego, where I was fortunate to be advised by Professor Hao Su on robotics and reinforcement learning research and Dr. Neil Smith at Qualcomm Institute on AI x XR applications.

interests

Embodied AI
Generative AI
Virtual Simulation
ML Infrastructure

education

M.S. Computer Science Stanford University
B.S. Computer Science University of California, San Diego

news

Sep 19, 2025	Started graduate school at Stanford University
Jun 15, 2025	Graduated from UC San Diego with Summa Cum Laude
Jun 22, 2024	New portfolio webpage!
Sep 19, 2022	Started my undergraduate journey at UC San Diego majoring in Computer Science

selected publications

ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

Stone Tao, Fanbo Xiang, Arth Shukla , and 17 more authors

Proceedings of Robotics: Science and Systems (RSS), 2025

Bib PDF Code Website

@article{tao2024maniskill3,
  title = {ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI},
  author = {Tao, Stone and Xiang, Fanbo and Shukla, Arth and Qin, Yuzhe and Hinrichsen, Xander and Yuan, Xiaodi and Bao, Chen and Lin, Xinsong and Liu, Yulin and Chan, Tse-kai and Gao, Yuan and Li, Xuanlin and Mu, Tongzhou and Xiao, Nan and Gurha, Arnav and Huang, Zhiao and Calandra, Roberto and Chen, Rui and Luo, Shan and Su, Hao},
  journal = {Proceedings of Robotics: Science and Systems (RSS)},
  year = {2025},
}

Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency

Stone Tao, Arth Shukla, Tse-kai Chan , and 1 more author

In The Twelfth International Conference on Learning Representations (ICLR) , 2024

Bib PDF Code Website

@inproceedings{tao2023reverse,
  title = {Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency},
  author = {Tao, Stone and Shukla, Arth and Chan, Tse-kai and Su, Hao},
  booktitle = {The Twelfth International Conference on Learning Representations (ICLR)},
  year = {2024},
}