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Richard Zemel


  • Canada CIFAR AI Chair
  • Pan-Canadian AI Strategy




Richard Zemel’s research contributions include foundational work on systems that learn useful representations of data without any supervision; methods for learning to rank and recommend items; and machine learning systems for automatic captioning and answering questions about images. He developed the Toronto Paper Matching System, a system for matching paper submissions to reviewers, which is being used in many conferences, including NIPS, ICML, CVPR, ICCV, and UAI. His research is supported by grants from NSERC, CIFAR, Microsoft, Google, Samsung, DARPA and iARPA.

His awards include an NVIDIA Pioneers of AI Award, a Young Investigator Award from the Office of Naval Research, a Presidential Scholar Award, and two NSERC Discovery Accelerators. Rich is on the Executive Board of the Neural Information Processing Society, which runs the premier international machine learning conference. He is a Google/NSERC Industrial Research Chair in Machine Learning and the Chief Scientists for Machine Learning for the Creative Destruction Lab at the Rotman School of Management.



  • Industrial Research Chair in Machine Learning, NSERC, 2018
  • Pioneers of AI, NVIDIA, 2016
  • Discovery Accelerator Award, NSERC, 2009, 2014
  • Dean's Excellence Award, University of Toronto, 2005-2008,2011, 2013, 2014

Relevant Publications

  • Klys, J., Snell, J., & Zemel, R. (2018). Learning latent subspaces in variational autoencoders. In Advances in Neural Information Processing Systems (pp. 6444-6454).
  • Madras, D., Creager, E., Pitassi, T., & Zemel, R. (2018). Learning adversarially fair and transferable representations. arXiv preprint arXiv:1802.06309.
  • Snell, J., Swersky, K., & Zemel, R. (2017). Prototypical networks for few-shot learning. In Advances in neural information processing systems (pp. 4077-4087).
  • Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. (2015). Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493.
  • Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013, February). Learning fair representations. In International Conference on Machine Learning (pp. 325-333).

Support Us

CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.

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