Jesse Zhang

jessezha at cs.washington.edu

Hi! I am a postdoc at UW, advised by Dieter Fox and Abhishek Gupta. I am focused on enabling robots to learn new tasks, autonomously, in the real world.

I completed my PhD at USC, advised by Jesse Thomason and Erdem Biyik at USC, and Joseph J. Lim at KAIST. I completed my undergrad at UC Berkeley, where I worked with Sergey Levine and Dinesh Jayaraman.

I am currently a collaborator at Ai2 and working with Toyota Research Institute. Previously, I've interned at NVIDIA in the Seattle Robotics Lab, AWS Lablets in Rasool Fakoor's team, NAVER AI Labs and Horizon Robotics.

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News
  • 04/26 Selected as a 2026 RSS Pioneer!
  • 04/26 Gave a talk at Yu Xiang's group at UT Dallas!
  • 03/26 Gave a talk at NVIDIA SRL hosted by Ankur Handa!
  • 09/25 Gave a talk at Xiaolong Wang's group at UCSD
  • 05/25 I am excited to announce that I will be starting a postdoc with Dieter Fox and Abhishek Gupta at UW!
  • 03/25 Presented HAMSTER at OpenAI's Robotics Reading Group w/ Yi Li and Yuquan Deng!
  • 02/25 Gave a talk as part of the Google Deepmind Tech Talk Series!
  • 01/25 Gave a talk at at the RLLAB at Yonsei University!
  • 12/24 Gave a talk at at the NTU Robot Learning Lab!
  • 02/24 Gave a talk at the PAL lab at UPenn
    • Research (Highlighted / All)
    (* indicates equal contribution, † indicates equal advising)
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    Blog Posts
  • From Generalists to Specialists: A Case for Real-World RL in Robot Manipulation
  • LLMs can help robots learn new tasks in unfamiliar places
    • Service
  • Workshop Organizing: Eval&Deploy at CoRL 2025.

  • UROS: Student-run, cross-department robotics reading group and seminar series at USC.

  • Serving as a mentor for the UW ML Reading Group discussing ML research papers.
  • Served as a PhD mentor for the Google x USC AI Community Project, focused on assisting undergraduates from underrepresented backgrounds in teaching AI to students from local Los Angeles middle and high schools.

  • Serving/Served as a reviewer for RSS 2026, CoRL 2026, ICLR 2025, CoRL 2025, NeurIPS 2025, RSS 2025, RA-L, CHI 2024, ICML 2024, ICLR 2024, NeurIPS 2023, ICML 2023, ICLR 2023, CoRL 2022, ICML 2022, ICLR 2022 (highlighted reviewer award, top 8%), NeurIPS 2021 (outstanding reviewer award, top 8%), CoRL 2021, ICLR 2021, ICLR SSL-RL Workshop 2021, IEEE ITSC 2019
    • Teaching
    TA at University of Southern California
  • 01/2025: CSCI 360: Intro to AI (Undergrad Level)
  • 08/2024: CSCI 566: Deep Learning (Master's Level)
  • 01/2023: CSCI 566: Deep Learning (Master's Level)
  • 01/2022: CSCI 360: Intro to AI (Undergrad Level)
  • 08/2020: CSCI 566: Deep Learning (Master's Level)

    • UC Berkeley
  • 08/2019: TA CS188: Intro to AI (rating: 4.75/5.00, 0.42 above dept avg)
  • 01/2019: Course Reader CS170: Efficient Algs and Intractable Problems
    • Others

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