Amir-massoud Farahmand
  • Assistant Professor, Department of Computer Science, Faculty of Arts & Science, University of Toronto

    Faculty Member, Vector Institute

    Canada CIFAR Artificial Intelligence Chair

    Website | Google Scholar

Research Interests

  • Reinforcement Learning
  • Statistical Learning Theory


Amir-massoud Farahmand is a faculty member, research scientist, and CIFAR AI Chair at the Vector Institute in Toronto, Canada. He is also an assistant professor at the Department of Computer Science, University of Toronto.

He received his PhD from the University of Alberta in 2011, followed by postdoctoral fellowships at McGill University (2011–2014) and Carnegie Mellon University (CMU) (2014). Prior to joining the Vector Institute, he was a principal research scientist at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, USA for three years, working on developing theoretically-sound algorithms for challenging industrial problems.

Amir-massoud’s research goal is designing an agent that controls its stream of experience, by learning how the outside and inside worlds work while focusing on aspects that are most relevant to its decision making. He takes a theoretical approach to this goal.


  • Best reviewer or outstanding area chair awards for International Conference on Machine Learning (ICML) (2015, 2019), Neural Information Processing Systems (NeurIPS) (2019), International Conference on Learning Representations (ICLR) (2018, 2021).
  • NSERC Postdoctoral Fellowship, 2012–2014
  • PhD Outstanding Thesis Award, Department of Computing Science, University of Alberta, 2012.

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