Biography

Hey! I’m in my final year as a Machine Learning D.phil student at the OATML group at University of Oxford. I’m co-supervised by Yarin Gal and Tim Rudner.

I’m currently working on designing methods, architectures and benchmarks to enable embodied agents to learn to do long horizon tasks by creating and accessing episodic memories. I am specifically focusing on transformer-based agents trained through large-scale RL. Some of the topics I have done research on over the previous three years are:

• Leveraging advances in visual diffusion modeling for robotics
• Mechanistic interpretability in transformer-based world models
• Training tokenised visual world models, and
• Causally-correct policy learning from imbalanced data.

I also collaborated closely with researchers from Toyota Research (Adrien Gaidon and Rowan McAllister) on topics related to causal robot learning.

Prior to starting my Ph.D, I had been working as a deep learning researcher at Wayve, a startup based in London that is employing end-to-end deep learning to achieve autonomous driving. Before that I graduated from a Machine Learning Research Master’s at Mila (Sept 2020) where I primarily did research on the topics of meta and continual learning and inverse reinforcement learning. I was also an ED&I Fellow with the MPLS department at the University of Oxford in 2022-2023 cohort.

Download my resumé.