• 2024-present: DPhil, Statistics and Machine Learning CDT, University of Oxford (Balliol College)
• 2020-2024: MMath and BA, Mathematics, University of Cambridge (Christ's College)

My goal is to use statistics and machine learning approaches to gain insights in infectious disease epidemiology.

I am a second-year DPhil student in the Department of Statistics and the Oxford-Man Institute of Quantitative Finance at the University of Oxford. I previously completed an MSc in Statistics (2022–2023) and a BSc in Mathematics and Statistics for Finance (2019–2022) at Imperial College London, where I developed a strong interest in statistical modelling and quantitative finance. Before beginning my doctoral studies, I worked as a Market Risk Analyst at JPMorgan Chase (2023–2024), where I gained valuable insights into the practical challenges of quantitative finance. My current research focuses on asset pricing anomalies and the effects of real-world trading frictions.

• Asset Pricing
• Portfolio Optimisation
• Machine Learning

I am a first year PhD student in the probability group supervised by Alison Etheridge.

Probabilistic modelling in population genetics

I am a DPhil student on the Health Data Science CDT working on statistical and generative machine learning for causal inference, with emphasis on applications to neuro-imaging data. Prior to starting the DPhil, I completed a BSc in Physics with Medical Physics at University College London (UCL) followed by an MPhil in Machine Learning and Machine Intelligence at the University of Cambridge. 

I am a first-year DPhil student researching efficient data acquisition through an information-theoretic lens, with a focus on Bayesian Experimental Design (BED) and Active Learning (AL). My work aims to improve the scalability of BED and AL, addressing computational bottlenecks such as sampling in high-dimensional parameter spaces (e.g. images), or the combinatorial complexity of searching over possible batches in AL.

Looking ahead, I’m also interested in exploring others areas including generative modelling, and Bayesian optimisation.

Deep learning for biomedical data analysis