Amir-massoud Farahmand is a research scientist/faculty member at the Vector Institute for Artificial Intelligence 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.
Amir-massoud received an NSERC (Natural Sciences and Engineering Research Council of Canada) postdoctoral fellowship (2012–2014) and the University of Alberta’s Department of Computing Science Ph.D. Outstanding Thesis Award for the period of 2011–2012. His work has been published in top machine learning (JMLR, MLJ, NIPS, ICML, AISTATS, AAAI), control engineering (IEEE TAC, CDC, ACC), and robotics (IROS and ICRA) venues.
See also: CV
- Faculty Member/Research Scientist, Vector Institute for Artificial Intelligence, 2018–present.
- Principal Research Scientist, Mitsubishi Electric Research Laboratories (MERL), 2014–2018.
- Postdoctoral Fellow, Carnegie Mellon University (RI) (Working with J. Andrew Bagnell), 2014
- Postdoctoral Fellow, McGill University (SCS) (Working with Doina Precup), 2011-2014
- PhD in CS from the University of Alberta (Working with Csaba Szepesvári and Martin Jägersand), 2005–2011.
- MS in EE from the University of Tehran (Working with Majid Nili Ahmadabadi, Babak N. Araabi, and Caro Lucas), 2002–2005.
- BS in EE from K.N. Toosi University of Technology (Working with Mohammad Sadegh Abrishamian), 1998–2002.