Michael Gimelfarb - Personal Website

Summary

I am a researcher in reinforcement learning, a branch of artificial intelligence that studies how an algorithm (agent) should interact with a dynamic environment (task) to achieve a specific goal. My current research tackles the following questions:

• Transfer Learning: How (and what) information learned by one agent on one task could help other agents trying to solve other related tasks? (i.e.)
• Automated Planning: How can the optimal actions be found efficiently when the observations and interactions are high-dimensional and complex? (i.e.)
• Offline RL: How can agents be evaluated/optimized better without costly interactions, by using data of prior interactions from another source? (i.e.)

Biography

I am currently working as a postdoc AI researcher in the RVL Lab led by Florian Shkurti at the University of Toronto, working on offline RL. In 2023, I was a researcher in the D3M Lab led by Scott Sanner, where I worked on automated planning and the development of the Python planning toolkit pyRDDLGym (which has now been included in scikit-decide!). In 2022, I interned at Google DeepMind, UK. I completed my PhD in Industrial Engineering at the University of Toronto advised by Scott Sanner and Chi-Guhn Lee in 2022, and my master’s degree in the same department under supervision of Michael Jong Kim in 2017. I received my Bachelor’s degree in Business Administration (BBA) from the Schulich School of Business.

I enjoy photography, outdoor sports, and play classical piano.