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Nando de Freitas


  • Associate Fellow
  • Learning in Machines & Brains




Nando de Freitas is a computer scientist who wants to understand intelligence and how brains work.

His key areas of research are neural networks and deep learning, reinforcement learning, apprenticeship learning and teaching, goal and program discovery, transfer and multi-task learning, reasoning and cognition.

He is a strong believer in building artificial intelligence (AI) tools to improve health care, advance science and provide decision support systems for lawyers, economists, politicians, environmentalists and others, with a goal of improving life on Earth. In his view, the price to be paid if we do not develop AI tools to extend our minds – and address our complex problems – is simply too high.


  • Charles A. McDowell Award for Excellence in Research, 2013
  • Distinguished Paper Award at IJCAI, 2013
  • MITACS Young Researcher Award, 2010

Relevant Publications

  • Wang, Z. et al. "Dueling network architectures for deep reinforcement learning." In Proceedings of the 33rd International Conference on Machine Learning (ICML), 1995–2003. 2016.
  • Wang, Z. et al. "Bayesian optimization in high dimensions via random embeddings." In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI),  1778–1784. 2016.
  • Reed, S., and N. de Freitas. "Neural Programmer-Interpreters." ICLR, 2015. arXiv:1511.06279.

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