I am a computer scientist working as a Postdoctoral Researcher at the Oxford Robotics Institute, University of Oxford, where I am a part of the GOALS group led by Nick Hawes. I am also a Retained Lecturer in Engineering Science at Jesus College and an Honorary Research Fellow at UCL Computer Science. From November 2026, I will join the University of Sheffield as a Lecturer (Assistant Professor).

I am interested in reinforcement learning (RL) and artificial intelligence (AI) more broadly. The key insight behind my work is the ability of RL to discover, by trial-and-error, ways of solving decision-making problems that can outperform or complement traditional methods. My work develops rigorous RL methodologies, especially for graph-structured systems (Graph RL), and applies them to disciplines as diverse as robotics, operations research, and statistics (AI for Science).

News

[Jul 2026] Thrilled to be be joining the School of Electrical and Electronic Engineering at the University of Sheffield as a Lecturer in Machine Learning for Engineering from November 2026. I'll be recruiting a PhD student (fully funded, UK home fees) to start in February 2027 as I set up my research group on graph reinforcement learning and AI for science & engineering. Feel free to get in touch for an informal chat.

[May 2026] I am serving the community as Reviewing Chair for the Learning on Graphs (LoG) 2026 Conference. We are recruiting reviewers to assist with the process. Relative to generalist machine learning conferences, LoG has a focused topic, special emphasis on review quality via monetary rewards, and a lower load. You can find the reviewer sign-up form here.

[May 2026] Our latest work introduces FlowIQN, a theoretically grounded flow-matching critic for distributional RL. By aligning flow matching with optimal transport and Wasserstein geometry, we bridge recent ideas in generative models with the foundations of distributional RL. This improves the accuracy of the return distribution and downstream offline RL performance.

For older news, see the archive.