About me

I am a 3rd-year PhD student in Machine Learning at the Gatsby Unit, University College London supervised by Professor Peter Orbanz. I work on causal machine learning, machine learning under symmetry and aspects of probabilistic machine learning such as Gaussian processes, MCMC, and scalable inference. Currently, I am focussed on understanding how measure transports that satisfy certain algebraic properties can be used to avoid the pitfalls of existing methods for counterfactual inference. I have most recently had two papers accepted at ICML 2025: (1) a novel algorithmic implementation for adaptive sampling methods designed to improve their computational efficiency on modern hardware, and (2) a novel, distributon-free method for causal discovery using parameterized vector fields. For a full list of my published and working papers, see ‘Publications’.

Prior to starting my PhD, I earned a master’s degree in Computational Statistics and Machine Learning from University College London (2021), a master’s degree in Economics at University College London (2016), and an undergraduate degree in Economics and International Relations from the University of Birmingham. Between 2016-2020 and 2021-2022 I worked for ~5 years as an econometrician in PwC’s Economic Consulting team in London. For key project highlights, see ‘Industry Portfolio’.