PeopleofSuperintelligence

Researcher

Tom Erez

Tom Erez is a research scientist at Google DeepMind in London who studies embodied intelligence in real and simulated robots, on a team that develops the MuJoCo physics engine. They earned a Ph.D. in computer science at Washington University in St. Louis in 2011 and described MuJoCo in a 2012 paper with Emanuel Todorov and Yuval Tassa. After joining DeepMind in 2014, they coauthored the paper Continuous control with deep reinforcement learning in 2015, the DeepMind Control Suite in 2018 and dm_control in 2020. DeepMind acquired MuJoCo in 2021 and open-sourced it in 2022, and ICLR named the 2015 paper a Test of Time winner in 2026.

Works
63
Citations
19,793
h-index
25
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2011

    PhD at Washington University

    They earn a Ph.D. in computer science at Washington University in St. Louis in 2011. Their B.Sc. in mathematics is from Hebrew University in Jerusalem.

  2. 2012

    The MuJoCo physics engine

    In 2012, they coauthor the paper describing MuJoCo with Emanuel Todorov and Yuval Tassa. The engine, short for Multi-Joint dynamics with Contact, is first developed in the Movement Control Laboratory at the University of Washington.

  3. 2014

    Joining DeepMind with Tassa

    In 2014, they join DeepMind together with Yuval Tassa after a postdoc with Emo Todorov at the University of Washington, where the two develop MuJoCo.

  4. 2015

    Continuous control with deep reinforcement learning

    In 2015, they are among the eight authors of Continuous control with deep reinforcement learning, with Timothy Lillicrap, Jonathan Hunt, Alexander Pritzel, Nicolas Heess, Yuval Tassa, David Silver and Daan Wierstra. The paper presents an actor-critic, model-free algorithm based on the deterministic policy gradient that solves more than 20 simulated physics tasks.

  5. 2018–2020

    DeepMind Control Suite and dm_control

    In 2018, they coauthor the DeepMind Control Suite, a set of continuous control tasks written in Python and powered by MuJoCo, intended as performance benchmarks for reinforcement learning agents. In 2020, they coauthor dm_control, a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.

  6. 2021–2022

    MuJoCo becomes open source

    In October 2021, DeepMind announces that it has acquired the MuJoCo physics simulator and is making it freely available, in a post they coauthor with Yuval Tassa, Saran Tunyasuvunakool, Nimrod Gileadi and Andrea Huber. In May 2022, MuJoCo is open-sourced under the Apache 2.0 license. In December 2022, they coauthor Predictive Sampling, which introduces MuJoCo MPC (MJPC), an open-source application for real-time predictive control.

  7. 2026

    ICLR Test of Time Award

    In 2026, the ICLR program chairs name Continuous control with deep reinforcement learning, which lists them among its authors, one of two Test of Time winners among papers published at ICLR 2016. The announcement credits the paper with introducing the Deep Deterministic Policy Gradient (DDPG) algorithm.

  8. As of October 2026

    Research scientist at Google DeepMind

    They are a research scientist at Google DeepMind in London, where they study embodied intelligence in real and simulated robots, and their team develops open-source projects including MuJoCo and MuJoCo-MPC. In December 2025, they give a plenary talk at the RiTA conference titled Do Vision-Language-Action (VLA) models solve Robotics?

Career

  1. Since 2014Google DeepMindResearch Scientist
  2. 2015–2026Google
  3. 2011–2014University of WashingtonPostdoc

Education

  1. 2011Washington University in St. LouisPhD
  2. Hebrew University of JerusalemB.Sc.

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Tom Erez. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  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

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  1. Plenary Speakers urobot.kaist.ac.kr. Checked 20 October 2026.
  2. 18 February: Robotics and Embodied Intelligence Workshop - CHIA chia.cam.ac.uk. Checked 20 October 2026.
  3. MuJoCo - Wikipedia en.wikipedia.org. Checked 20 October 2026.
  4. Continuous control with deep reinforcement learning arxiv.org. Checked 20 October 2026.
  5. DeepMind Control Suite arxiv.org. Checked 20 October 2026.
  6. dm_control: Software and Tasks for Continuous Control arxiv.org. Checked 20 October 2026.
  7. Opening up a physics simulator for robotics — Google DeepMind deepmind.google. Checked 20 October 2026.
  8. Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo arxiv.org. Checked 20 October 2026.
  9. Announcing the Test of Time Awards from ICLR 2016 – ICLR Blog blog.iclr.cc. Checked 20 October 2026.