I'm a research intern at H Company, using RL to train computer-use agents. I previously worked as a research scientist on the AI Agents team at Huawei's Noah's Ark Lab, and completed my PhD in the Bath RL Lab, supervised by Özgür Şimşek.
Interests
I'm interested in all things reinforcement learning. Currently, I'm focussing on building LLM-based agents that continuously learn by setting, pursuing, and verifying their own goals (2025).
Previously, I studied how to explain reinforcement learning agents, identifying their behaviour, outcomes, and predictions as aspects of interaction worth understanding (2023). This involved developing a theoretical framework to derive these explanations (2025) and creating scalable methods to approximate them in practice (2025).
More broadly, a common thread in how I think about intelligence is bounded rationality: how intelligence emerges as a necessary adaptation for agents operating with limited resources in a world far more complex than themselves. I'm also drawn to conversations that question the foundations of reinforcement learning, exploring how our assumptions about agents and environments shape the algorithms and conceptual models we design.
Papers
Open-Source Projects
Darwin Mobile Agent
An end-to-end pipeline for large-scale online reinforcement learning of mobile GUI agents.
FastSVERL
A scalable library for approximating Shapley value explanations in reinforcement learning.
Talks
Explaining Reinforcement Learning with Shapley Values: Theory and Algorithms
Edinburgh RL Group, September 2025
A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values
ART-AI Colloquium Series, February 2025
Bath Doctoral Festival of Ideas, July 2024
An Introduction to Explainable and Hierarchical Reinforcement Learning
Bath AI Society, April 2024
Explaining Reinforcement Learning with Shapley Values
Bath Computer Science Conference, July 2023
Alan Turing Institute Student Presentations, June 2023
News
- Jul. 2026: Started as a Research Intern at H Company, London.
- Jun. 2026: Passed my PhD viva with no corrections!
- Dec. 2025: Released Darwin Mobile Agent, the first fully open-source, end-to-end pipeline for large-scale online reinforcement learning and inference of mobile GUI agents. Paper + Code.
- Dec. 2025: Attended NeurIPS in San Diego.
- Sep. 2025: Gave a talk to the Edinburgh RL group. Slides.
- Sep. 2025: Paper on Approximating Shapley Explanations in Reinforcement Learning accepted as a poster at NeurIPS 2025! Poster.
- Jun. 2025: New preprint: A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values.
- Jun. 2025: Joined the AI Agent group at Huawei Noah's Ark Lab as a Research Scientist!
- Jun. 2025: Presented a poster at the 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM). Poster.
- Feb. 2025: Gave a talk as part of the ART-AI colloquium series. Slides.
- Oct. 2024: Presenting a poster at the 17th European Workshop on Reinforcement Learning (EWRL). Poster.
- Sep. 2024: Attended the Mediterranean Machine Learning Summer School. Poster.
- Jul. 2024: Attended the CIFAR Deep Learning + Reinforcement Learning (DLRL) Summer School. Poster.
- Jun. 2024: Accepted a Doctoral Recognition Award at the Bath Doctoral Research Celebration Evening!
- Jun. 2024: Gave a talk as part of the Digital Futures session at the Bath Doctoral Festival of Ideas. Poster.
- Apr. 2024: Gave a talk to the Bath AI Society on the work of the Bath Reinforcement Learning Lab.
- Oct. 2023: Attended the UKRI Inter AI CDT Conference in Bristol. Poster.
- Sep. 2023: Our work on "Reformulating Reactivity Design for Data-Efficient Machine Learning" has been accepted to ACS Catalysis!
- Aug. 2023: Presented our poster on "Explaining Reinforcement Learning with Shapley Values" at ICML 2023.
- Jul. 2023: Presented a talk on "Explaining Reinforcement Learning with Shapley Values" at the Bath Conference of Computer Science.
- Jun. 2023: Presented a talk on "Explaining Reinforcement Learning with Shapley Values" at the Alan Turing Institute Student Presentations.
- Apr. 2023: Our paper on "Explaining Reinforcement Learning with Shapley Values" has been accepted as a poster at ICML 2023!
- Oct. 2021: Joined the Bath Reinforcement Learning Lab at the University of Bath as a 1st year Ph.D. student.
Teaching
I have taught computer science and mathematics at the University of Bath, including lecturing on reinforcement learning, supporting undergraduate and postgraduate teaching across reinforcement learning, statistics, programming, and applied mathematics, and supervising student projects.
Teaching Assistant
- Reinforcement Learning, 2022-2023
- Software Technologies for Data Science, 2021
- Statistics for Data Science, 2021
- Programming, Foundations, and Connections, 2021
- Programming and Discrete Mathematics, 2021
- Mathematical Methods and Applications, 2020