Amir-massoud Farahmand

Faculty Member

Assistant Professor, Department of Computer Science, Faculty of Arts & Science, University of Toronto

Canada CIFAR Artificial Intelligence Chair

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.

Research Interests

  • Reinforcement Learning
  • Statistical Learning Theory


PID Accelerated Value Iteration Algorithm

Amir-Massoud Farahmand and Mohammad Ghavamzadeh


Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations

Erfan Pirmorad and Faraz Khoshbakhtian and Farnam Mansouri and Amir-massoud Farahmand


Understanding and Mitigating the Limitations of Prioritized Replay

Yangchen Pan and Jincheng Mei and Amir-massoud Farahmand and Martha White and Hengshuai Yao and Mohsen Rohani and Jun Luo


Lecture Notes on Reinforcement Learning

Amir-massoud Farahmand