My research interests lie within the field of Bayesian inference and machine learning, primarily focussing these interests in the field of spatial statistics. My PhD involves investigating the health effects arising from exposure to poor air quality. Due to the inherent data complexity involved with such analysis, part of my work involves developing scalable methods to handle large-scale data in a tractable fashion.

In 2018 I completed a master's degree in Data Science at Lancaster University, with a thesis focussing on making Bayesian approximations within deep learning architectures to detect adversaries. Prior to this, I completed an undergraduate degree in Mathematics and Statistics at the University of Reading, with a placement year working as a data analyst at software company SAP.

Alongside my master's degree, I worked as a Data Scientist for Natural Language Processing company Relative Insight. Here I developed a semi-automated abstractive text summariser to analyse large corpora of text data. Throughout my final-year undergraduate studies, I worked as a data analyst for the Institute of Environmental Analytics, developing models to perform inference in large graph networks.