PeopleofSuperintelligence

Researcher

Jingwei Zhang

Jingwei Zhang is a researcher at DeepMind whose work on reinforcement learning ranges from robot navigation to interactive world models and generalist robots. After a master's degree in mechanical engineering at Carnegie Mellon University, they completed a doctorate at the University of Freiburg in 2021 in Wolfram Burgard's group, with a dissertation on learning navigation policies with deep reinforcement learning. During the doctorate, they published a successor-feature navigation method in 2017 and presented Scheduled Intrinsic Drive at an ICML workshop in 2019. At DeepMind, they were first author of the 2023 jumpy-models paper on planning in robotic domains and one of the authors of Genie, which won an ICML Best Paper award in 2024. They were also among the authors of the Gemini Robotics reports of 2025.

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Timeline

  1. 2007–2013

    A master's at Carnegie Mellon

    A bachelor's degree in measurement and control instruments and technology, with a minor in computer science, runs from 2007 to 2011. Zhang then studies mechanical engineering at Carnegie Mellon University from 2011 to 2013 for a Master of Science.

  2. 2014–2021

    Doctorate in Burgard's group

    In 2014, Zhang joins Wolfram Burgard's Autonomous Intelligent Systems group at the University of Freiburg as a PhD student. The Freiburg doctorate (Dr. rer. nat.) follows in 2021 with the dissertation "Learning Navigation Policies with Deep Reinforcement Learning", advised by Burgard.

  3. 2017

    Successor features for navigation

    In 2017, Zhang, Jost Tobias Springenberg, Joschka Boedecker and Burgard propose a successor-feature deep reinforcement learning algorithm that carries knowledge from earlier navigation tasks to new ones, tested on a Robotino robot for IROS 2017. In June 2017, Zhang also posts Neural SLAM as first author, a method that builds SLAM-like procedures into an external memory so that reinforcement learning agents can explore new environments.

  4. 2019

    Scheduled Intrinsic Drive

    At the 2019 ICML workshop on Exploration in Reinforcement Learning, Zhang, Niklas Wetzel, Nicolai Dorka, Boedecker and Burgard present Scheduled Intrinsic Drive as a spotlight talk. The agent learns separate intrinsic and extrinsic policies and schedules between them, and is evaluated in VizDoom, DeepMind Lab and the DeepMind Control Suite.

  5. 2023

    Jumpy models at DeepMind

    At DeepMind in 2023, Zhang is first author of Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains, with seven co-authors. The paper learns a jumpy model alongside a skill embedding space from unlabeled experience, then uses them for planning in the RGB-stacking environment. A Generalist Dynamics Model for Control, posted in May 2023 with Zhang as second author, studies transformer sequence models as dynamics models.

  6. 2024

    A Best Paper for Genie

    Zhang is one of the authors of Genie: Generative Interactive Environments, a DeepMind paper from February 2024 on a generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. Genie generates action-controllable virtual worlds from text, synthetic images, photographs and sketches. The paper receives a Best Paper award at ICML 2024.

  7. 2025

    Gemini Robotics reports

    Zhang is among the authors of Gemini Robotics: Bringing AI into the Physical World, posted in March 2025, which presents a Vision-Language-Action model that directly controls robots. Zhang also co-authors Gemini Robotics 1.5, a multi-embodiment Vision-Language-Action model reported in October 2025.

  8. As of October 2026

    Research at DeepMind

    As of October 2026, Zhang works at DeepMind. The recent papers there include Genie in 2024 and the Gemini Robotics reports in 2025.

Career

  1. NowGoogle DeepMindResearcher

Education

  1. 2021University of FreiburgPhD
  2. 2013Carnegie Mellon UniversityMaster of Science

From the biography.

  1. 16% of AI career moves in the 2020s went from a university to a companyIncludes Jingwei Zhang. We estimate that 16% of AI career moves in the 2020s went from a university to a company; 29% of moves starting at a university went to a company, and 76% ended the university role.
  2. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  3. Google has the highest combined h-index among AI organizationsIncludes Google. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Jingwei Zhang yet. Send us a free-licensed photo.

  1. Jingwei Zhang - Autonomous Intelligent Systems www2.informatik.uni-freiburg.de. Checked 21 October 2026.
  2. Jingwei Zhang - The Mathematics Genealogy Project mathgenealogy.org. Checked 21 October 2026.
  3. Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments arxiv.org. Checked 21 October 2026.
  4. Neural SLAM: Learning to Explore with External Memory arxiv.org. Checked 21 October 2026.
  5. Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration arxiv.org. Checked 21 October 2026.
  6. Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains (full text) ar5iv.labs.arxiv.org. Checked 21 October 2026.
  7. Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains arxiv.org. Checked 21 October 2026.
  8. A Generalist Dynamics Model for Control arxiv.org. Checked 21 October 2026.
  9. Genie: Generative Interactive Environments — Google DeepMind deepmind.google. Checked 21 October 2026.
  10. ICML 2024 Awards icml.cc. Checked 21 October 2026.
  11. Gemini Robotics: Bringing AI into the Physical World arxiv.org. Checked 21 October 2026.
  12. Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer arxiv.org. Checked 21 October 2026.
  13. Jingwei Zhang - Google Scholar scholar.google.de. Checked 21 October 2026.