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.

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 scientific disciplines as diverse as robotics, operations research, and statistics.

News

[Sep 2025] I am on the job market for Lecturer or Assistant Professor positions to start in 2026/27. If you think I might be a good fit for your department, feel free to get in touch. I am happy to share my application materials upon request.

[Sep 2025] Excited to share two new pre-prints. Our work that proposes the GNARL method reimagines Neural Algorithmic Reasoning as Markov Decision Processes, unlocking the powerful tools of RL, and addressing some of the key limitations of NAR. Furthermore, in a collaboration with researchers at Imperial College, we leverage Graph RL to accelerate a fundamental discovery task in atomic physics.

[Apr 2025] Our paper Tree search in DAG space with model-based reinforcement learning for causal discovery has been published in Proceedings of the Royal Society A. We address the problem of discovering causal graphs with a model-based reinforcement learning method, which is powered by an incremental algorithm for determining cycle-inducing edges, and is shown to compare favorably to model-free RL methods and greedy search. Code for the proposed CD-UCT algorithm and benchmarks of causal discovery methods is publicly available.

For older news, see the archive.