Michael Gimelfarb
Postdoctoral Research Fellow at Department of Computer Science, University of Toronto
Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolMichael Gimelfarb is a Postdoctoral Research Fellow in Toronto specializing in reinforcement learning, transfer learning, and offline RL with eight years of experience bridging theory and applied systems. He develops scalable gradient-based planning and generative-model-driven sequential planning methods, and has a track record of publishing at NeurIPS, ICLR, UAI and AAAI. His work includes practical engineering: building Python toolchains that auto-generate OpenAI Gym environments from PDDL, managing CI for a planning competition, and prototyping research code in TensorFlow and JAX at DeepMind. Comfortable moving between optimization (Gurobi), robotics, and large-scale empirical evaluation, he focuses on making planning robust and explainable in high-dimensional settings. An economist-turned-ML researcher by training, he blends operations research rigor with hands-on software development to turn complex RL ideas into reproducible experiments and usable tooling.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Reinforcement Learning and Operations Research, Doctor of Philosophy - PhD Reinforcement Learning and Operations Research at University of TorontoBachelor’s Degree Finance, Bachelor’s Degree Finance at Schulich School of Business - York University
Github Skills (34)
simulation10
sgd10
visualization10
simulator10
gymnasium10
visualizer10
openai-gym10
mdp10
benchmarking10
reinforcement-learning10
openai10
backpropagation10
evaluation-framework10
planning10
controller9
Programming languages (3)
Github contributions (5)
mike-gimelfarb/bayesian-reward-shaping
Apr 2018 - Mar 2019
Contributions:65 commits, 55 pushes, 1 branch in 11 months
mike-gimelfarb/deep-successor-features-for-transfer
Jan 2021 - May 2021
A reusable framework for successor features for transfer in deep reinforcement learning using keras.
Contributions:38 commits, 4 PRs, 34 pushes in 3 months
pytorchdeep-learningreinforcement-learningdeep-reinforcement-learningsuccessor
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