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About me

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Welcome to my homepage!

I am an AI scientist whose career goal is to understand computational principles underlying intelligence. More specifically, I study agents that interact with a sequential environment to improve their behavior through trial and error. This agent-environment interaction is referred to as the reinforcement learning problem.

I have a PhD and an extensive background in the theoretical and empirical foundations of reinforcement learning. I have published more than a dozen papers at top-tier AI conferences such as Neurips, ICML, ICLR, AAAI, and ACL. On the academic side, I am really interested in understanding the optimization problem that arises in the context of learning the value function. On the aplication side, I am quite interested in the developing assistive AI agents that interact with humans and learn from feedback to provide better service. My aspiration is to make AI agents that co-exist with and help us live our best lives.

The best way to reach me for career-related stuff is by shooting me an email at my first name @alumni.brown.edu. I am genuinely interested in forming meaningful connections with fellow AI scientists, engineers, students, etc, so feel free to reach out even if we never met in person.

News

• Our paper on Fairness in Reinforcement Learning is accepted at RLC, the first instance of the Reinforcement Learning Conference. Super Excited to meet our wonderful community in Amherst!
• Our paper on learning the target network in function space got into ICML 2024. I am super excited about this one, so please stay tuned!
• Our paper on foundation models for continual learning was accepted at ICLR 2024.
• Will present two papers at Neurips 2023. (See you again in New Orleans!)
• Gave a talk at Seattle Mind and Machines meetup on UW’s beautiful campus.
• Gave a talk at Amazon’s RL reading group.
• Moved from Oahu, Hawaii to Seattle, WA.
• One paper accepted at AISTATS 2022.
• Coauthered the RL chapter of the D2L book.
• Two papers accepted at Neurips 2022.
• Gave a guest lecture at Harvard’s ML class.
• Moved from SF bay area to WFHH (Work From Home in Hawaii)
• One paper accepted at Neurips 2021.
• Two papers accepted at AAAI 2021.
• Moved from Providence, RI to SF bay area.

Affiliation

• 2020-Now: Applied Scientist at Amazon
• 2015-2020: PhD Student at Brown University
  (Advisor: Michael Littman)
• 2013-2015: Master’s Student at the University of Alberta
  (Advisor: Rich Sutton)
• 2008-2013: Undergraduate student at the University of Tehran