Yao Feng Quantitative Researcher at Jane Street Asia Ltd. Address: Hong Kong, China Contact: Email: yaofeng1998@gmail.com LinkedIn | Google Scholar | Github

Biography

I am a quantitative researcher at Jane Street Asia Ltd. in Hong Kong. I received my Ph.D. from the Tsinghua SAIL Group, Department of Computer Science and Technology, Tsinghua University, supervised by Prof. Jun Zhu.

Education:

• Ph.D. in Computer Science and Technology, Tsinghua UniversityJun 2026
• M.S. in Computer Science, Carnegie Mellon UniversityMay 2022
• B.S. in Mathematics and Physics, Tsinghua UniversityJun 2020
• B.S. in Economics, Tsinghua UniversityJun 2020

Research Interests:

• Machine Learning
• Video Generation Models
• Robotics
• Reinforcement Learning
• Diffusion Models

Selected Awards

• Outstanding Ph.D. Graduate in Computer Science and Technology, Tsinghua UniversityJun 2026
• Top Ten Singers of the Eight-Department Singing Competition at Tsinghua UniversityMay 2024
• Champion in Neural MMO Challenge (NeurIPS 2023)Apr 2024
• Stars of Tomorrow in Microsoft Research AsiaMay 2022
• Champion in the Optiver Clash of the Solvers ChallengeMar 2022
• Five-star Redbud Volunteer of Tsinghua UniversityJun 2021
• First Prize in the National Final of the Chinese Mathematics Competitions (Ranked 28th in China)Apr 2021
• Qisun Ye Award (Highest Undergraduate Honor in Department of Physics, Tsinghua University)Jun 2020
• Beijing Outstanding GraduatesJun 2020
• China National Scholarship (Top 0.2%)Nov 2018
• Meritorious Winner in Mathematical Contest in ModelingApr 2017

Publications

• Vidarc: Embodied Video Diffusion Model for Closed-loop Control
  Yao Feng, Chendong Xiang, Xinyi Mao, Hengkai Tan, Zuyue Zhang, Shuhe Huang, Kaiwen Zheng, Haitian Liu, Hang Su, Jun Zhu
  arXiv, 2025
• Motus: A Unified Latent Action World Model
  Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, Chendong Xiang, Yinze Rong, Hongyan Zhao, Hanyu Liu, Zhizhong Su, Lei Ma, Hang Su, Jun Zhu
  arXiv, 2025
• Vidar: Embodied Video Diffusion Model for Generalist Manipulation
  Yao Feng*, Hengkai Tan*, Xinyi Mao, Guodong Liu, Shuhe Huang, Chendong Xiang, Hang Su, Jun Zhu
  arXiv, 2025
• AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
  Hengkai Tan*, Yao Feng*, Xinyi Mao*, Shuhe Huang, Guodong Liu, Zhongkai Hao, Hang Su, Jun Zhu
  arXiv, 2025
• Self-Consistent Model-based Adaptation for Visual Reinforcement Learning
  Xinning Zhou, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
  IJCAI, 2025
• Adaptive Shielding via Parametric Safety Proofs
  Yao Feng, Jun Zhu, André Platzer, Jonathan Laurent
  OOPSLA, 2025
• Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning
  Joseph Suárez, Kyoung Whan Choe, David Bloomin, Jianming Gao, Yunkun Li, Yao Feng, Saidinesh Pola, Kun Zhang, Yonghui Zhu, Nikhil Pinnaparaju, Hao Xiang Li, Nishaanth Kanna, Daniel Scott, Ryan Sullivan, Rose S. Shuman, Lucas de Alcântara, Herbie Bradley, Kirsty You, Bo Wu, Yuhao Jiang, Qimai Li, Jiaxin Chen, Louis Castricato, Xiaolong Zhu, Phillip Isola
  arXiv, 2025
• Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
  Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
  arXiv, 2022
• Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
  Yao Feng, Yuhong Jiang, Hang Su, Dong Yan, Jun Zhu
  arXiv, 2022
• Min-max Optimization without Gradients: Convergence and Applications to Black-box Evasion and Poisoning Attacks
  Sijia Liu*, Songtao Lu*, Xiangyi Chen*, Yao Feng*, Kaidi Xu*, Abdullah Al-Dujaili*, Mingyi Hong, Una-May O'Reilly
  ICML, 2020
• Capacitance Extraction and Power Grid Analysis Using Statistical and AI Methods
  Wenjian Yu, Ming Yang, Yao Feng, Ganqu Cui, Ben Gu
  ASP-DAC, 2020

Teaching

• 2023 Spring, TA in Statistical Learning Theory and Applications, Tsinghua University (Instructor: Jun Zhu)
• 2021 Spring, TA in Statistical Machine Learning, Tsinghua University (Instructor: Hang Su)