Google DeepMind
University of Toronto
Queen's University
David SilverProfessor · UCLResearcher↗
Researcher · Academic
Timothy Lillicrap
UCLAdjunct Professor
Lillicrap co-developed DDPG, which lets AI learn smooth actions such as steering or moving a robot arm.[1] He contributed to AlphaGo[2] and AlphaZero[3], and co-developed feedback alignment, showing that neural networks can learn through random feedback connections—a clue to how learning might work in the brain.[4]
Career & education
Current roleUCL2016-presentAdjunct Professor
UCL2016-presentPreviously
University of Oxford2012-2014Postdoctoral Research Fellow
Staff Research Scientist2016-2025
Research Scientist -> Senior Research Scientist2014-2016
University of Oxford2012-2014Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations193,147As of Nov 1, '26
citations+4,517Since last snapshot, Oct 1, '26
Selected contributions
Mastering the game of Go with deep neural networks and tree search
With David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser and others ·AlphaGo combined neural networks and search to beat a professional Go player.
Mastering the game of Go without human knowledge
With David Silver, Julian Schrittwieser, Karén Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert and others ·AlphaGo Zero learned Go through self-play without examples of human games.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
With David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot and others ·AlphaZero learned chess, shogi and Go through self-play rather than human examples.
References
- Continuous Control with Deep Reinforcement Learningarxiv.org
Lead coauthorship; DDPG operates over continuous action spaces in simulated physics/control tasks.
- Mastering the Game of Go with Deep Neural Networks and Tree Searchdoi.org
Original AlphaGo coauthorship cross-checked in Lillicrap’s author-hosted CV. Publisher web access redirected to blocked identity service.
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmarxiv.org
Original AlphaZero paper includes Lillicrap; contribution not leadership.
- Random Synaptic Feedback Weights Support Error Backpropagation for Deep Learningpmc.ncbi.nlm.nih.gov
Lead coauthorship; random feedback weights provide useful learning signals; potential neuroscience implications, not a demonstrated brain mechanism.
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Reviewed July 23, 2026 · Checked for accuracy and new information.
