My background is in Mathematics and Statistics, having completed a BSc in Mathematical Sciences at the University of Durham in 2021 and a MSc in Statistical Science at the University of Oxford in 2022. I am passionate about the use of quantitative methods for tackling challenging social, environmental and public health issues, and have worked on a variety of research projects exploring these topics.
I am currently working on automated personalised 4D heart modelling for disease prediction. Generative statistical models of cardiac anatomy and function have a wide range of applications such as disease diagnosis, personalised medicine, and generation of population and sub-population cohorts for in silico simulated studies. The aim is to develop a geometric deep-learning model for the cardiac shape over an entire contraction cycle.
Previous areas of interest have been prescribing practices of pain medications in the UK and evidence synthesis in conservation science, which I worked on as summer projects at Durham and Cambridge universities respectively. I became further interested in statistics and data science in health through my undergraduate project in modelling the dynamics of infectious disease and my master's dissertation on risk prediction in case-cohort studies using data from the China Kadoorie biobank (supervised by Christiana Kartsonaki at the Nuffield Department of Population Health).
Moving forward, apart from building on my previous research, I am interested in developing and applying some of the machine learning methods I studied during my master’s degree for applications such as building tools to facilitate more objective analysis of qualitative information in clinical settings.
In my spare time, I am an avid fencer.