Having studied Computer Science and Mathematics at the University of York, I went on to complete a master’s in Machine Learning at the University of Bristol. During this time, I became interested in the use of probabilistic techniques and novel statistical inference to make sense of complex problems. This led to my final project, which used density ratio estimation to infer changes in Markov network models of neuroscientific data. After my master’s, I co-founded an automation focused proprietary trading company with social impact goals, Bayesian Shift, where we use sequential Monte Carlo methods to estimate volatility.
I am now driven to investigate the fundamentals of statistical learning in a research environment. Broadly, I am interested in developing methods that are able to capture the complexity demanded by modern scientific problems whilst maintaining a rigorous treatment of uncertainty.