About
Timothy Lillicrap received an Hon. B.Sc. in Cognitive Science & Artificial Intelligence from the University of Toronto and a Ph.D. in Systems Neuroscience from Queen’s University in Canada. He moved to the University of Oxford in 2012, where he worked as a Postdoctoral Research Fellow. In 2014 he joined Google DeepMind as a Research Scientist. His research focuses on machine learning for optimal control and decision-making, as well as using these mathematical frameworks to understand how the brain learns.
Awards
- NSERC Postdoctoral Research Fellowship, 2015
- Governor General’s Academic Medal, 2001
Relevant Publications
- Hafner, D., Pasukonis, J., Ba, J., Lillicrap, T. (2024). Mastering diverse domains through world models. arXiv:2301.04104.
- Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews Neuroscience, 21(6), 335-346.
- Lillicrap, T. P., Cownden, D., Tweed, D. B., & Akerman, C. J. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature communications, 7(1), 13276.