PeopleofSuperintelligence

Researcher

Ian Gemp

Ian Gemp is a staff research scientist on the Game Theory team at Google DeepMind in New York, where he builds equilibrium algorithms and recasts learning problems as games. Trained in mechanical engineering and applied mathematics at Northwestern, he earned a computer science PhD at UMass Amherst in 2018 with Sridhar Mahadevan and joined DeepMind in London in 2019. His paper "EigenGame: PCA as a Nash Equilibrium," which treated principal component analysis as a competitive game, won an Outstanding Paper Award at ICLR 2021. Work with Luke Marris and Georgios Piliouras on approximating Nash equilibria earned an ICLR 2024 honorable mention, and a ranking paper drawing on social choice theory won the AAMAS 2025 Best Paper Award. In 2024, he applied game-theoretic solvers to language models in negotiation scenarios, with the goal of making them more strategic.

Timeline

  1. 2006–2013

    Engineering at Northwestern

    He finishes high school at Kinkaid in Houston in 2006 and studies mechanical engineering and applied mathematics at Northwestern University, earning a BS in 2010 and an MS in 2011. From 2011 to 2013, he works as a consultant at Capgemini in Chicago.

  2. 2013–2018

    A PhD at UMass Amherst

    From 2013 to 2018, he is a research assistant at UMass Amherst's College of Information and Computer Sciences, working with Sridhar Mahadevan in the Autonomous Learning Laboratory on optimization, equilibration, reinforcement learning and multi-agent systems. He earns an MS in 2016 and a PhD in 2018; his dissertation, "From Optimization to Equilibration: Understanding an Emerging Paradigm in Artificial Intelligence and Machine Learning," studies equilibria in differentiable games. He also interns at Adobe Research in 2016 and at AWS AI in New York in 2018.

  3. 2019–2021

    Joining DeepMind in London

    He joins DeepMind in London as a research scientist in 2019. By 2021, he works on its Multiagent team, asking how agents should behave in competitive, mixed-motive and cooperative groups.

  4. 2021–2022

    EigenGame and EigenGame Unloaded

    With Brian McWilliams, Claire Vernade and Thore Graepel, he recasts principal component analysis as a competitive game in which each approximate singular vector is controlled by a player maximizing its own utility; "EigenGame: PCA as a Nash Equilibrium" receives an Outstanding Paper Award at ICLR 2021. The team's follow-up, EigenGame Unloaded at ICLR 2022, adds an unbiased stochastic update for datasets with larger sample sizes. By April 2022, he is a senior research scientist on DeepMind's Multiagent team.

  5. 2024

    Nash equilibria via stochastic optimization

    With Luke Marris and Georgios Piliouras, he proposes the first loss function for approximate Nash equilibria of normal-form games that is amenable to unbiased Monte Carlo estimation. The paper, "Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization," receives an Outstanding Paper Honorable Mention at ICLR 2024.

  6. February 2024

    Strategic language models

    In February 2024, he and colleagues publish "Steering Language Models with Game-Theoretic Solvers," which applies game theory to negotiation scenarios with the goal of making language models more strategic. He explains that game trees built from a model's response probabilities let researchers compute Nash equilibria and rank rebuttals to a harsh paper review.

  7. May 2025

    AAMAS 2025 Best Paper Award

    "Soft Condorcet Optimization for Ranking of General Agents," written with Marc Lanctot, Kate Larson, Doina Precup and six other coauthors, wins the Best Paper Award at AAMAS 2025 in Detroit in May 2025. The paper draws on ideas from social choice theory, ranking and optimization to evaluate AI agents. In 2025, he also gives tutorials on evaluating AI agents at AAMAS and SAGT with Manfred Diaz, Marc Lanctot and Kate Larson.

  8. As of October 2026

    Game Theory team in New York

    He is a staff research scientist on the Game Theory team at Google DeepMind in New York, where he develops equilibrium algorithms and game-theoretic formulations of learning problems. He co-organizes the Strategic Engineering Workshop on LLMs and Game Theory at AAMAS 2026 and serves as a coordinating editor of the Journal of Autonomous Agents and Multi-Agent Systems.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

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. Ian Gemp imgemp.github.io. Checked 19 October 2026.
  2. EigenGame: PCA as a Nash Equilibrium site.uottawa.ca. Checked 19 October 2026.
  3. Students people.cs.umass.edu. Checked 19 October 2026.
  4. Game theory as an engine for large-scale data analysis — Google DeepMind deepmind.google. Checked 19 October 2026.
  5. Ian Gemp – Publications imgemp.github.io. Checked 19 October 2026.
  6. Economics and Computation UK ecuk.doc.ic.ac.uk. Checked 19 October 2026.
  7. ICLR 2024 Awards iclr.cc. Checked 19 October 2026.
  8. Game Theory Can Make AI More Correct and Efficient | Quanta Magazine quantamagazine.org. Checked 19 October 2026.
  9. [2402.01704] Steering Language Models with Game-Theoretic Solvers arxiv.org. Checked 19 October 2026.
  10. Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners - AIhub aihub.org. Checked 19 October 2026.
  11. Ranking AI: Professor Kate Larson wins Paper Award at AAMAS | Cheriton School of Computer Science | University of Waterloo cs.uwaterloo.ca. Checked 19 October 2026.