Aditya Mahajan
Aditya Mahajan studies reinforcement learning and control for partially observed systems; he earned a BTech in electrical engineering at IIT Kanpur and MS and PhD degrees in electrical engineering and computer science at the University of Michigan. He shared the 2015 IEEE CSS George S. Axelby Outstanding Paper Award with Ashutosh Nayyar and Demosthenis Teneketzis, and received a 2016 NSERC Discovery Accelerator Award and a 2024 Jean d'Alembert Fellowship. In 2022, he coauthored an approximate-information-state framework for planning and reinforcement learning, and in 2025 he coauthored work on regularized Q-learning for POMDPs. As of October 2026, he is a full professor in McGill's Department of Electrical and Computer Engineering and a core academic member of Mila.
- Now at
- McGill University and Mila
Timeline
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2015
A George Axelby award
He shares the 2015 IEEE CSS George S. Axelby Outstanding Paper Award with Ashutosh Nayyar and Demosthenis Teneketzis for their paper on decentralized control with partial history sharing. His research spans decentralized stochastic control, team theory, reinforcement learning, multi-armed bandits and information theory.
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2016
An NSERC award
He receives a 2016 NSERC Discovery Accelerator Award.
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June 2016–November 2023
Associate professor at McGill
From June 2016 to November 2023, he serves as an associate professor in McGill's Department of Electrical and Computer Engineering.
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2022
Approximate information states
With Jayakumar Subramanian, Amit Sinha and Raihan Seraj, he publishes “Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems” in the Journal of Machine Learning Research in 2022. Their framework uses approximate information states to form an approximate dynamic program and bound the policy's loss of optimality.
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November 2023
Full professor at McGill
In November 2023, he becomes a full professor in McGill's Department of Electrical and Computer Engineering.
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2024
A Jean d'Alembert Fellowship
He receives a 2024 Jean d'Alembert Fellowship from Université Paris-Saclay.
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2025
Q-learning for POMDPs
In 2025, he coauthors “Convergence of regularized agent-state-based Q-learning in POMDPs” with Amit Sinha and Matthieu Geist for the IEEE Conference on Decision and Control.
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As of October 2026
Professor and Mila member
As of October 2026, he is a full professor in McGill's Department of Electrical and Computer Engineering and a core academic member of Mila. He earns a BTech in electrical engineering from IIT Kanpur and MS and PhD degrees in electrical engineering and computer science from the University of Michigan. His current research includes reinforcement learning, structure-aware reinforcement learning and multi-agent reinforcement learning.
Career
- Since 2016McGill UniversityAssociate Professor, Full Professor
- NowMilaCore Academic Member
Education
- Indian Institute of Technology KanpurBTech in Electrical Engineering
- University of MichiganMS in Electrical Engineering and Computer Science
Sources and credits
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- Ashutosh Nayyar, Aditya Mahajan, Demosthenis Teneketzis - 2015 recipient of IEEE CSS George S. Axelby Outstanding Paper Award
- bio – Aditya Mahajan
- Aditya Mahajan : GERAD
- Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems
- Aditya Mahajan | Mila
- Aditya Mahajan | Electrical and Computer Engineering - McGill University

