Ashleigh is a quantitative researcher interested in global health and reducing the impact of preventable diseases. Now, pursuing a PhD in Applied Mathematics, Ashleigh is based at Imperial College London working between the department of mathematics, and the department of infectious disease. In his current PhD research, he is developing methods that exploit network theory, data-rich mathematical modelling, and machine learning to predict and combat hospital disease transmission. Specifically, he has a strong focus on transmission models for antimicrobial-resistant pathogens in healthcare settings.

More broadly, Ashleigh has dedicated his research positions to interdisciplinary work on quantitative tools to understand and prevent disease. Outside of his PhD, Ashleigh has worked on a range of projects, including developing control measures with the UK National Health Service & World Health Organisations for in-hospital transmission of COVID-19; wearable device surveillance at the Robert Koch Institute; gender bias with DeepMind; rapid infection diagnostics with the Defence Science and Technology Laboratory; and distributed computing to annotate the human genome. 

Research interests

At the Turing, Ashleigh aims to explore new methodologies for modelling the transmission of antimicrobial-resistant pathogens in healthcare settings. Ashleigh has a large dynamic dataset with rich meta-information, which he hopes to perform quantitive analyses with the help of Turing experts. He is interested in network science and graph theory and wishes to use them in conjunction with machine learning and other computational modelling procedures to capture and identify novel transmission dynamics. In addition, given the diverse and sensitive demographic data Ashleigh deals with, he also hopes to improve the ethical integrity of his research, which will more broadly guide decision-making throughout his research career.