Logan Mondal Bhamidipaty

Phd Student

Logan is a researcher working on building intelligent robotic systems, combining reinforcement learning, game theory, and control to create agents that can act strategically and robustly in complex environments. His work spans robot infrastructure, learning algorithms, and designing systems that can adapt to uncertainty.

Tell us about your journey before you joined the Centre for AI in Assistive Autonomy?

Before coming to Edinburgh, I studied mathematics and computer science at Stanford University, where I focused on reinforcement learning and multi-agent systems. During my time there, I had the privilege of working across several labs on decision-making under uncertainty, meta-learning, and RLHF. While completing my master’s, I also worked as an economic consultant at Auctionomics where I helped analyze complex auction markets.

What motivates you to work in this area?

I’m motivated by the challenge of making robots think and act strategically in the real world. RL really excites me because it blends math, economics, and ML, while robotics offers a tangible testbed. I’m particularly drawn to problems where environments are dynamic and hard to model—like multi-agent settings, human interaction, or markets—where robust learning really matters.

What do you love about Edinburgh?

The history, scenery, and access to nature are fantastic. I also love the balance here—the city feels vibrant and full of character, but it’s never far from quiet green spaces to think and recharge.

Personal Website: https://logan-mondal-bhamidipaty.com/