PeopleofSuperintelligence

Researcher

Mark Rowland

Mark Rowland is a research scientist at Google DeepMind whose work spans reinforcement learning and formal mathematical reasoning. He developed distributional reinforcement-learning methods, coauthored a textbook on the field, and proved convergence results for quantile temporal-difference learning. In 2024, he contributed to AlphaProof, which helped a combined system reach silver-medal level at the International Mathematical Olympiad.

Works
67
Badges

Publication numbers from OpenAlex, as of 22 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.

Timeline

  1. 2018

    Cambridge degrees and quantile regression

    At Cambridge, he studies for a BA and MMath, then a PhD at Clare College, co-supervised by Rich Turner at the Machine Learning Group and John Aston at Statslab. In 2018, he coauthors a quantile-regression algorithm that models the distribution of returns in reinforcement learning.

  2. 2019

    An expectile-based RL algorithm

    In 2019, he and his coauthors introduce EDRL, an algorithm based on estimates of the expectiles of a return distribution.

  3. 2023

    A textbook on distributional RL

    In 2023, he coauthors the MIT Press textbook Distributional Reinforcement Learning, an introduction to the field.

  4. 2024

    Proving quantile TD convergence

    In 2024, he and his coauthors analyze quantile temporal-difference learning and prove its convergence with probability 1 to fixed points of related dynamic-programming procedures.

  5. 2024

    A minimax result for distributional RL

    In 2024, he and his coauthors propose a model-based distributional RL algorithm with a minimax-optimal bound, up to logarithmic factors, for approximating return distributions with a generative model.

  6. July 2024

    AlphaProof at the 2024 IMO

    In July 2024, he contributes to AlphaProof, a reinforcement-learning system for formal mathematical reasoning. With AlphaGeometry 2, AlphaProof helps a combined AI system reach silver-medal level at the International Mathematical Olympiad.

  7. November 2025

    AlphaProof's formal reasoning method

    In November 2025, he coauthors the Nature paper presenting AlphaProof as an agent that learns formal proofs through reinforcement learning on millions of auto-formalized problems.

  8. As of October 2026

    Research scientist at DeepMind

    As of October 2026, he is a research scientist at Google DeepMind, where he works on reinforcement learning from theoretical guarantees to large-scale deep-RL applications.

Career

  1. NowGoogle DeepMindResearch scientist

Education

  1. University of CambridgeBA and MMath

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Mark Rowland. 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.

Sources and credits

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  1. Mark Rowland – MLG Cambridge mlg.eng.cam.ac.uk. Checked 20 October 2026.
  2. Distributional Reinforcement Learning With Quantile Regression ojs.aaai.org. Checked 20 October 2026.
  3. Statistics and Samples in Distributional Reinforcement Learning proceedings.mlr.press. Checked 20 October 2026.
  4. Distributional Reinforcement Learning distributional-rl.org. Checked 20 October 2026.
  5. An Analysis of Quantile Temporal-Difference Learning jmlr.org. Checked 20 October 2026.
  6. Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model arxiv.org. Checked 20 October 2026.
  7. AI achieves silver-medal standard solving International Mathematical Olympiad problems deepmind.google. Checked 20 October 2026.
  8. Olympiad-level formal mathematical reasoning with reinforcement learning nature.com. Checked 20 October 2026.
  9. Mark Rowland sites.google.com. Checked 20 October 2026.