Summary
Amin Abyaneh is a PhD candidate and associate researcher at Mila/McGill with eight years of experience advancing safe, predictable robot control through a blend of imitation and reinforcement learning grounded in control-theoretic stability guarantees. His work spans academic and industry settings—from Max Planck and EPFL to hands-on sim-to-real manipulation projects using Nvidia Omniverse and Kinova arms—demonstrating both theoretical rigor and practical deployment. He has tackled decentralized learning and causal discovery for pandemic modeling and led TA teams in core electrical and CS courses, reflecting strong mentorship and systems intuition. Known for combining control theory with modern RL to produce globally stable, flexible policies, he brings a rare mix of mechatronics, embedded software, and research engineering. Based in Montreal, he pairs deep technical breadth with an ability to translate research into robust robotic behaviors.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Electrical and Electronics Engineering, Bachelor's degree, Electrical and Electronics Engineering at Sharif University of Technology
High School, Mathematics, High School, Mathematics at National Organization for Development of Exceptional Talents (Sampad)
Doctor of Philosophy - PhD, Mechatronics, Robotics, and Automation Engineering, Doctor of Philosophy - PhD, Mechatronics, Robotics, and Automation Engineering at McGill University