Pol Moreno
Also known as Pol Moreno Comellas
Pol Moreno is an AI researcher whose work spans computer vision, multi-agent learning, sports analytics and mathematical reasoning. His 2013 MSc project studies dependencies in neural recordings, and he presents inverse-graphics research at ECCV in 2016. At Edinburgh, he is a PhD student under Chris Williams and interns at DeepMind from August to December 2017, developing generative methods for scene descriptions. He contributes to Google DeepMind's 2021 football-analytics overview and coauthors a paper on recursive belief states; in 2024, he coauthors TacticAI, a Liverpool FC collaboration for corner-kick analysis. In 2025, he is named among the IMO team's leads and key contributors as Gemini Deep Think reaches gold-medal level, and as of October 2026 he is part of the Deep Think team behind a Gemini-backed Paper Assistant tested at STOC 2026.
Timeline
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2013
MSc project under Iain Murray
His MSc project under Iain Murray studies machine learning of dependencies in high-density neural recordings in 2013.
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2016
Overcoming occlusion with inverse graphics
In 2016, he coauthors Overcoming Occlusion with Inverse Graphics with Christopher K. I. Williams, Charlie Nash and Pushmeet Kohli, and presents it orally at ECCV’s Geometry Meets Deep Learning workshop.
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2017
PhD and DeepMind internship
In 2017, he is a PhD student at the University of Edinburgh under Chris Williams, with research interests in machine learning and computer vision. From August to December 2017, he works as a DeepMind research intern; his research combines generative models and deep learning to produce interpretable, accurate scene descriptions.
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2021
AI for football analytics
In 2021, he contributes to Google DeepMind’s overview of sports analytics. It presents sports as a testbed for AI in complex multiagent environments and connects football analysis to computer vision, statistical learning and game theory.
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2021
Recursive belief states
In 2021, he coauthors Neural Recursive Belief States in Multi-Agent Reinforcement Learning. The paper uses recursive deep generative models to approximate hierarchical beliefs about other agents, and its belief-model agents outperform model-free baselines with equivalent representational capacity.
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2024
TacticAI for corner kicks
He coauthors TacticAI in 2024, a football tactics assistant developed and evaluated with Liverpool FC experts for corner-kick analysis. Its predictive and generative components let coaches explore player setups and recommend position adjustments.
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2025
Gemini Deep Think at IMO
He is named among the leads and key contributors to the IMO 2025 team. Gemini Deep Think solves five of six problems perfectly, earns 35 points and reaches gold-medal-level performance.
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As of October 2026
Deep Think and Paper Assistant
As of October 2026, he is part of Google’s Deep Think team. The team contributes to Paper Assistant Tool, a Gemini 2.5 Deep Think system tested for STOC 2026 to give authors automated feedback on conference papers.
Career
- Since 2021Google
- 2021–2024Google DeepMindResearch Intern
Education
Most-cited works
- Game Plan: What AI can do for Football, and What Football can do for AIWith Alex Bridgland, Demis Hassabis, Michal Valko and Thore Graepel
- TacticAI: an AI assistant for football tacticsWith Demis Hassabis and Michael Bowling
- NeRF-VAE: A Geometry Aware 3D Scene Generative ModelWith Danilo Jimenez Rezende
- Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single ImageWith Pushmeet Kohli
- Overcoming Occlusion with Inverse GraphicsWith Pushmeet Kohli
Sources and credits
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- Projects supervised by Iain Murray
- PhD Studentship in Vision as Inverse Graphics
- Pol Moreno Comellas
- Advancing sports analytics through AI research
- Neural Recursive Belief States in Multi-Agent Reinforcement Learning
- TacticAI: an AI assistant for football tactics
- Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad
- Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026



