- Reinforcement Learning
- Statistical Learning Theory
Amir-massoud Farahmand is a faculty member at the Vector Institute in Toronto, Canada. His research interests are in reinforcement learning and machine learning with a focus on developing theoretically-sound algorithms for challenging industrial problems. 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 worked as a principal research scientist at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, USA for three years.
- NSERC Postdoctoral Fellowship, 2012–2014
- PhD Outstanding Thesis Award, Department of Computing Science, University of Alberta, 2012.
- International Conference on Machine Learning (ICML) Best Reviewer Award, 2015.
Research Activity and News
- A. M. Farahmand, Sepideh Pourazarm, and Daniel Nikovski, “Random Projection Filter Bank for Time Series Data,” In the Proceedings of Neural Information Processing Systems (NIPS), December 2017.
- A. M. Farahmand, André M.S. Barreto, and Daniel Nikovski, “Value-Aware Loss Function for Model-based Reinforcement Learning,“ The 20th International Conference on Artificial Intelligence and Statistics (AISTATS), April 2017.
- A. M. Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor, “Regularized Policy Iteration with Nonparametric Function Spaces,” Journal of Machine Learning Research (JMLR), Vol. 17, No. 139, 2016.
- A. M. Farahmand, Saleh Nabi, Piyush Grover, and Daniel Nikovski, “Learning to Control Partial Differential Equations: Regularized Fitted Q-Iteration Approach,” IEEE Conference on Decision and Control (CDC), December 2016.
- A. M. Farahmand, Doina Precup, André M.S. Barreto, Mohammad Ghavamzadeh, “Classification-based Approximate Policy Iteration,” IEEE Transactions on Automatic Control, Vol. 60, No. 11, 2015.