Science Café – Artificial Intelligence in the Physical World
Wednesday, March 11, 2026 from 6:30 pm to 8:00 pm
- In-person event
- 1054 Bank St , Ottawa , ON , K1S 3X2
- Contact
- odscomms@carleton.ca
While Artificial Intelligence and Machine Learning have revolutionized our digital world, they are yet to be widely adopted in the physical world. For instance, we do not yet see a ubiquitous presence of robots and autonomous vehicles in the real world. Most tasks in the physical world have the nature of sequential decision-making, where clear labels of correct behaviour are hard to collect. Reinforcement Learning is an active area of research that proposes algorithms that enable autonomous agents to directly learn from experience; however, several challenges exist in deploying these algorithms in the physical world. This talk will describe some of these challenges along with the latest research advances achieved by my research, along with the work of others.
About the Speaker
Dr. Sriram Ganapathi Subramanian is an Assistant Professor at the School of Computer Science in Carleton University, where he holds a Canada Research Chair in Artificial Intelligence. He received his Bachelor’s degree in Engineering from Anna University, Chennai (India) in 2016, a Master’s degree in Electrical and Computer Engineering from the University of Waterloo (Canada) in 2018, and a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (Canada) in 2022. His doctoral dissertation won the Best Doctoral Dissertation Award from the Canadian AI Association in 2023. Sriram’s research primarily focuses on the development of novel algorithms and data structures for the fields of Multi-agent Systems, Reinforcement Learning, Game Theory, and Deep Learning. Additionally, his research investigates existing algorithms in these fields, either theoretically or empirically, establishing new insights and properties.
Personal Website: https://sriramsubramanian.com/
Carleton Profile: https://carleton.ca/scs/people/sriram-subramanian/
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