Skip to content
CIFAR header logo
fr
menu_mobile_logo_alt
  • News
  • Events
    • Public Events
    • Invitation-only Meetings
  • Programs
    • Research Programs
    • Pan-Canadian AI Strategy
    • Next Generation Initiatives
    • Global Call for Ideas
  • People
    • Fellows & Advisors
    • CIFAR Azrieli Global Scholars
    • Canada CIFAR AI Chairs
    • AI Strategy Leadership
    • Solution Network Members
    • Leadership
  • Support Us
  • About
    • Our Story
    • CIFAR 40
    • Awards
    • Partnerships
    • Publications & Reports
    • Careers
    • Staff Directory
    • Equity, Diversity & Inclusion
  • fr
  • Home
  • Bio

Follow Us

amir-massoud-farahmand2

Amir-massoud Farahmand

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

Personal Page

Google Scholar

About

Amir-massoud Farahmand is a Canada CIFAR AI Chair at the Vector Institute, an assistant professor in the department of computer science at the University of Toronto.

Farahmand’s research focuses on machine learning and reinforcement learning. He is interested in understanding the principles required to design reinforcement learning and adaptive situated agents. These agents interact with their environment, collect data, and use data not only to predict, but also to control the environment, with the goal of maximizing their long-term performance. He is particularly interested in solving difficult industrial problems using principles developed to design adaptive situated agents.

Awards

  • Best Reviewer Award, International Conference on Learning Representations (ICLR), 2018
  • Best Reviewer Award, International Conference on Machine Learning (ICML), 2015
  • NSERC Postdoctoral Fellowship, 2012-2014
  • PhD Outstanding Thesis Award, Department of Computing Science, University of Alberta, 2011-2012
  • NSERC Pre-approved Industrial Research and Development Fellowship (IRDF) Candidate, 2012

Relevant Publications

  • Farahmand, A. M. (2018). Iterative Value-Aware Model Learning. In NeurIPS (pp. 9090-9101).

  • Farahmand, A. M., Barreto, A., & Nikovski, D. (2017). Value-aware loss function for model-based reinforcement learning. In Artificial Intelligence and Statistics (pp. 1486-1494). PMLR.

  • Farahmand, A. M., Ghavamzadeh, M., Szepesvári, C., & Mannor, S. (2016). Regularized policy iteration with nonparametric function spaces. The Journal of Machine Learning Research, 17(1), 4809-4874.

  • Farahmand, A. M., Munos, R., & Szepesvári, C. (2010). Error propagation for approximate policy and value iteration. In Advances in Neural Information Processing Systems.

  • Farahmand, A. M., Ghavamzadeh, M., Szepesvári, C., & Mannor, S. (2008). Regularized policy iteration. In nips (pp. 441-448).

Institution

University of Toronto

Vector Institute

Department

Computer Science

Education

  • PhD (Computer Science), University of Alberta
  • MSc (Electrical Engineering), University of Tehran

Country

Canada

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.

Donate Now
CIFAR header logo

MaRS Centre, West Tower
661 University Ave., Suite 505
Toronto, ON M5G 1M1 Canada

Contact Us
Media
Careers
Accessibility Policies
Supporters
Financial Reports
Subscribe

  • © Copyright 2023 CIFAR. All Rights Reserved.
  • Charitable Registration Number: 11921 9251 RR0001
  • Terms of Use
  • Privacy
  • Sitemap

Subscribe

Stay up to date on news & ideas from CIFAR.

This website stores cookies on your computer. These cookies are used to collect information about how you interact with our website and allow us to remember you. We use this information in order to improve and customize your browsing experience and for analytics and metrics about our visitors both on this website and other media. To find out more about the cookies we use, see our Privacy Policy.
Accept Learn more