Published twelve papers as first or joint first author in top-tier ML conferences (e.g., NeurIPS [spotlight], ICML [long oral], ICLR [spotlight], RSS, AISTATS). Specific works include improving LLM agent planning with in-context learning and introducing EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control.
Research Experience
Recent RS intern at Google DeepMind and a fourth-year Ph.D. student in Machine Learning at the University of Cambridge, part of the Machine Learning and Artificial Intelligence group. Involved in multiple LLM-related research projects.
Education
PhD in Machine Learning, 2021 - 2025, University of Cambridge, advised by Mihaela van der Schaar FRS; MEng in Engineering Science, 2013 - 2017, University of Oxford.
Background
Interests include Large Language Models (LLMs), LLM Agents, Transformer Architectures, Reinforcement Learning (model-free and model-based), Control, Symbolic Regression (discovery), and applications to scientific discovery and healthcare. Focuses on driving foundational research to advance the state-of-the-art for LLM output generation, particularly in LLM agents using external memory and tool use.
Miscellany
Passionate about inventing and flexibly adapting to drive new prototypes forward, especially in areas like multi-modal LLM agents and using RL to automatically improve LLM agents.