World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

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Computer Science

D-Index
84
Citations
185293
World Ranking
815
National Ranking
39

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2019 - ACM Prize in Computing For breakthrough advances in computer game-playing

Overview

David Silver is a researcher affiliated with DeepMind in the United Kingdom, with a primary focus on computer science and artificial intelligence. Their publication record includes significant contributions across several subfields, notably artificial intelligence, molecular biology, and computational theory and mathematics.

The main topics covered in David Silver's work include reinforcement learning in robotics, evolutionary algorithms and applications, artificial intelligence in games, adversarial robustness in machine learning, protein structure and dynamics, and enzyme structure and function.

Frequent publication venues for their research are:

  • arXiv (Cornell University)
  • Nature
  • The Journal of Urology
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Zenodo (CERN European Organization for Nuclear Research)

David Silver has collaborated extensively with several researchers, including:

  • Demis Hassabis
  • Satinder Singh
  • Hado van Hasselt
  • Matteo Hessel
  • André Barreto

Their recent publications include:

  • Highly accurate protein structure prediction with AlphaFold (2021, Nature)
  • Improved protein structure prediction using potentials from deep learning (2020, Nature)
  • Deep learning, reinforcement learning, and world models (2022, Neural Networks)
  • Discovering faster matrix multiplication algorithms with reinforcement learning (2022, Nature)
  • Applying and improving AlphaFold at CASP14 (2021, Proteins Structure Function and Bioinformatics)

In 2019, David Silver received the ACM Prize in Computing for breakthrough advances in computer game-playing.

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

  • Continuous control with deep reinforcement learning

    Timothy P. Lillicrap;Jonathan J. Hunt;Alexander Pritzel;Nicolas Heess

  • Mastering the game of Go without human knowledge

    David Silver;Julian Schrittwieser;Karen Simonyan;Ioannis Antonoglou

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Deep reinforcement learning with double Q-Learning

    Hado van Hasselt;Arthur Guez;David Silver

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

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • 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

  • Deep reinforcement learning with double Q-learning

    H Van Hasselt;A Guez;D Silver

  • Prioritized Experience Replay

    Tom Schaul;John Quan;Ioannis Antonoglou;David Silver

  • Deterministic Policy Gradient Algorithms

    David Silver;Guy Lever;Nicolas Heess;Thomas Degris

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

    Julian Schrittwieser;Ioannis Antonoglou;Thomas Hubert;Karen Simonyan

  • Rainbow: Combining Improvements in Deep Reinforcement Learning

    Matteo Hessel;Joseph Modayil;Hado van Hasselt;Tom Schaul

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

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Monte-Carlo Planning in Large POMDPs

    David Silver;Joel Veness

  • Asynchronous Methods for Deep Reinforcement Learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Reinforcement Learning with Unsupervised Auxiliary Tasks

    Max Jaderberg;Volodymyr Mnih;Wojciech Marian Czarnecki;Tom Schaul

  • Emergence of Locomotion Behaviours in Rich Environments

    Nicolas Heess;Dhruva Tb;Srinivasan Sriram;Jay Lemmon

Frequent Co-Authors

Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Hado van Hasselt
Hado van Hasselt University College London
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Demis Hassabis
Demis Hassabis Google (United States)
Thore Graepel
Thore Graepel University College London
Volodymyr Mnih
Volodymyr Mnih DeepMind (United Kingdom)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA

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