PeopleofSuperintelligence

Researcher

Amir-massoud Farahmand

Amir-massoud Farahmand is a reinforcement-learning researcher whose work studies how efficient agents interact with their environment and improve over time. He earned electrical-engineering degrees in Iran and a computing-science PhD at Alberta, then held postdoctoral fellowships at McGill and Carnegie Mellon. At Mitsubishi Electric Research Laboratories, he applied reinforcement learning to air-conditioning systems and hybrid-vehicle energy choices before serving as a research scientist and CIFAR AI Chair at Vector from 2018 to 2024. In 2025, he continued revising a draft reinforcement-learning textbook and coauthored an ICML paper on categorical distributional reinforcement learning with a convergence guarantee. Since November 2024, he is an associate professor at Polytechnique Montréal and a core academic member of Mila, and he holds a status-only associate professorship at the University of Toronto; CIFAR confirmed renewal of his Canada-CIFAR AI Chair in October 2026, and he teaches an introductory reinforcement-learning course in Fall 2026.

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Timeline

  1. 1998–2005

    Degrees in Iran

    From 1998 to 2002, he earns a BS in electrical engineering at K.N. Toosi University of Technology with Mohammad Sadegh Abrishamian. From 2002 to 2005, he earns an MS in electrical engineering at the University of Tehran with Majid Nili Ahmadabadi, Babak N. Araabi and Caro Lucas.

  2. 2005–2011

    A PhD at Alberta

    At the University of Alberta, he completes a computing science PhD in 2011 with Csaba Szepesvári and Martin Jägersand.

  3. 2011–2014

    Postdoctoral work at McGill and CMU

    At McGill University's School of Computer Science, he holds a postdoctoral fellowship with Doina Precup from 2011 to 2014, in a lab with Joelle Pineau. In 2014, he holds a postdoctoral fellowship at Carnegie Mellon's Robotics Institute with J. Andrew Bagnell.

  4. 2014–2018

    Industrial reinforcement learning at MERL

    He serves as principal research scientist at Mitsubishi Electric Research Laboratories (MERL) from 2014 to 2018. His reinforcement-learning projects optimize air-conditioning systems and decide when a hybrid electric vehicle switches between battery and fuel.

  5. 2018–2024

    A research chair at Vector

    From 2018 to 2024, he serves as a research scientist, faculty member and CIFAR AI Chair at the Vector Institute in Toronto. He studies principles for reinforcement-learning agents that can solve challenging industrial problems.

  6. 2021–2025

    A draft reinforcement-learning textbook

    His draft textbook, Foundations of Reinforcement Learning, grows out of Lecture Notes on Reinforcement Learning, written for an Introduction to Reinforcement Learning course in Spring 2021. In 2025, he continues revising the book.

  7. 2025

    Categorical reinforcement learning at ICML

    In 2025, he coauthors the ICML paper “Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics,” which uses a KL-divergence loss. Its preconditioned algorithm has a convergence guarantee.

  8. Since November 2024

    Professorships in Montréal and Toronto

    Since November 2024, he is an associate professor in the Department of Computer and Software Engineering at Polytechnique Montréal and a core academic member of Mila. As of October 2026, he also holds a status-only associate professorship in the University of Toronto's Department of Computer Science; his research studies how efficient AI agents interact with their environment and improve over time. In October 2026, CIFAR confirms the renewal of his Canada-CIFAR AI Chair, and in Fall 2026 he teaches an introductory course on reinforcement learning.

Career

  1. Since 2024Polytechnique MontréalAssociate Professor
  2. Since 2024MilaCore Academic Member
  3. NowUniversity of TorontoAssociate Professor
  4. 2018–2024Vector InstituteResearch Scientist, Faculty Member, CIFAR AI Chair
  5. 2014Carnegie Mellon UniversityPostdoctoral Fellow
  6. 2014–2018Mitsubishi GroupPrincipal Research Scientist
  7. 2011–2014McGill UniversityPostdoctoral Fellow

Education

  1. 2011University of AlbertaPhD
  2. 2005University of TehranMS
  3. 2002K. N. Toosi University of TechnologyBS

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Toronto. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Toronto. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorateIncludes University of Toronto. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorate. Across 2,116 people with both an undergraduate degree and a doctorate recorded, we trace which undergraduate schools feed which doctoral programs, how often students stay at the same school, and how paths differ by birth-country region.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of Toronto. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Bio – Amir-massoud Farahmand academic.sologen.net. Checked 21 October 2026.
  2. Cross-Country Career in Reinforcement Learning Leads Amir-massoud Farahmand to Vector Institute in Toronto vectorinstitute.ai. Checked 21 October 2026.
  3. Amir-massoud Farahmand | Mila mila.quebec. Checked 21 October 2026.
  4. Publications – Amir-massoud Farahmand academic.sologen.net. Checked 21 October 2026.
  5. Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics proceedings.mlr.press. Checked 21 October 2026.
  6. Five Mila Members Appointed Canada-CIFAR AI Chairs | Mila mila.quebec. Checked 21 October 2026.
  7. Introduction to Reinforcement Learning | A course on reinforcement learning. amfarahmand.github.io. Checked 21 October 2026.
  8. Research – Amir-massoud Farahmand academic.sologen.net. Checked 21 October 2026.