World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
77
Citations
184969
World Ranking
1225
National Ranking
647

Research.com Recognitions

  • 2018 - Fellow of the Royal Society, United Kingdom
  • 2017 - Fellow of the Royal Academy of Engineering (UK)

Overview

Demis Hassabis is affiliated with Google in the United States and has a research focus concentrated primarily within Computer Science and Biochemistry, Genetics and Molecular Biology. Their scholarly output encompasses significant contributions to the fields of Artificial Intelligence and Molecular Biology, among various other interdisciplinary domains.

Their research spans several subfields of study, including:

  • Artificial Intelligence
  • Molecular Biology
  • Materials Chemistry
  • Economics and Econometrics
  • Cognitive Neuroscience

Within these domains, Hassabis has engaged extensively with topics such as:

  • Protein Structure and Dynamics
  • Sports Analytics and Performance
  • Artificial Intelligence in Games
  • Machine Learning in Bioinformatics
  • Enzyme Structure and Function
  • Reinforcement Learning in Robotics
  • Topic Modeling

Frequent co-authors collaborating with Hassabis include Pushmeet Kohli, John Jumper, Andrew Senior, Nenad Tomašev, and Tim Green, reflecting teamwork in varied research efforts.

Hassabis's work has appeared across several notable publication venues, with multiple papers featured in:

  • arXiv (Cornell University)
  • Nature
  • Zenodo (CERN European Organization for Nuclear Research)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Science

Among recent significant papers are the following:

  • Highly accurate protein structure prediction with AlphaFold, 2021, Nature
  • Accurate structure prediction of biomolecular interactions with AlphaFold 3, 2024, Nature
  • AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models, 2021, Nucleic Acids Research
  • Protein complex prediction with AlphaFold-Multimer, 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • Improved protein structure prediction using potentials from deep learning, 2020, Nature

Hassabis has received recognition including fellowships from distinguished bodies:

  • Fellow of the Royal Society, United Kingdom, 2018
  • Fellow of the Royal Academy of Engineering (UK), 2017

Best Publications

  • Highly accurate protein structure prediction with AlphaFold

    John M. Jumper;Richard O. Evans;Alexander Pritzel;Tim Green

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • Mastering the game of Go with deep neural networks and tree search

    David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez

  • Mastering the game of Go without human knowledge

    David Silver;Julian Schrittwieser;Karen Simonyan;Ioannis Antonoglou

  • AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.

    Mihaly Varadi;Stephen Anyango;Mandar Deshpande;Sreenath Nair

  • Overcoming catastrophic forgetting in neural networks

    James Kirkpatrick;Razvan Pascanu;Neil C. Rabinowitz;Joel Veness

  • A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Protein complex prediction with AlphaFold-Multimer

    Richard Evans;Michael O'Neill;Alexander Pritzel;Natasha Antropova

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Improved protein structure prediction using potentials from deep learning

    Andrew W. Senior;Richard Evans;John Jumper;James Kirkpatrick

  • International evaluation of an AI system for breast cancer screening.

    Scott Mayer McKinney;Marcin Sieniek;Varun Godbole;Jonathan Godwin

  • Highly accurate protein structure prediction for the human proteome

    Kathryn Tunyasuvunakool;Jonas Adler;Zachary Wu;Tim Green

  • Clinically applicable deep learning for diagnosis and referral in retinal disease

    Jeffrey De Fauw;Joseph R. Ledsam;Bernardino Romera-Paredes;Stanislav Nikolov

  • Mastering Atari, Go, chess and shogi by planning with a learned model

    Julian Schrittwieser;Ioannis Antonoglou;Thomas Hubert;Karen Simonyan

  • Hybrid computing using a neural network with dynamic external memory

    Alex Graves;Greg Wayne;Malcolm Reynolds;Tim Harley

  • Neuroscience-Inspired Artificial Intelligence.

    Demis Hassabis;Dharshan Kumaran;Christopher Summerfield;Matthew Botvinick

  • The Future of Memory: Remembering, Imagining, and the Brain

    Daniel L. Schacter;Donna Rose Addis;Demis Hassabis;Victoria C. Martin

  • Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

    Unknown

  • Using Imagination to Understand the Neural Basis of Episodic Memory

    Demis Hassabis;Dharshan Kumaran;Eleanor A. Maguire

  • Human-level performance in 3D multiplayer games with population-based reinforcement learning

    Max Jaderberg;Wojciech M. Czarnecki;Iain Dunning;Luke Marris

Frequent Co-Authors

Dharshan Kumaran
Dharshan Kumaran Google (United States)
Eleanor A. Maguire
Eleanor A. Maguire University College London
Matthew Botvinick
Matthew Botvinick Yale University
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
David Silver
David Silver DeepMind (United Kingdom)
Charles Blundell
Charles Blundell DeepMind (United Kingdom)
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)

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