Mike Gimelfarb

Hi!

I'm currently a Post-Doc in the D3M Lab led by Prof. Scott Sanner at the University of Toronto. My work focuses on leveraging structure for planning and reinforcement learning (RL) (using tools such as probabilistic programming, i.e. RDDL). I am also actively looking for continuing post-doc and industry research positions in the area of reinforcement learning/dynamic decision making.

Prior to this, I completed my PhD under the supervision of Scott Sanner and Prof. Chi-Guhn Lee in the same department in December 2022. My thesis focused on rendering modern RL more efficient through the transfer of prior knowledge (e.g. skills, demonstrations) from multiple sources. I developed novel applications of Bayes' inference to robustly distinguish good knowledge sources from bad, and tackled the problem of transfer learning for risk-sensitive agents. My research has been published in top AI/ML conferences such as NeurIPS, UAI, AAAI, and ICLR. I also completed an internship at DeepMind in 2022, and I was previously a post-graduate affiliate of the Vector Institute from 2020 to 2022.

Prior to this, I completed my master's degree (MASc) at U of T under supervision of Prof. Michael J. Kim. My thesis focused on the theoretical developments of Thompson sampling applied in queueing and admission control problems with demand uncertainty. I received my Bachelor's degree in Business Administration (BBA) from the Schulich School of Business in 2014, graduating with distinction.

I enjoy reading books on cognitive science and play classical piano in my spare time.