My work at the EPSRC Centre for PCI Planning falls into 4 broad categories: Inference, Emulation, Uncertainty Quantification and Optimisation. My current primary focus is on surrogate modelling of a plaque-affected artery to infer its material parameters. This work aims to speed up the inferential framework by employing machine learning and statistical methods to emulate the arterial system. This will allow for a cost and time-saving framework for clinical use to optimise the planning and delivery of PCI for a given patient. Similar methods will be employed to evaluate the degree of restenosis through patient-specific geometric models for clinical risk assessment and decision-making.

My interest is inference and uncertainty quantification in applied biostatistics. My PhD broadly focuses on low-cost surrogate models for complex biological systems. Specifically, we use fluid dynamics physics-informed models to build patient-specific surrogates to estimate risk and uncertainty quantification in the planning of percutaneous coronary intervention.

• Multivariate Methods, Inference, Principals of Probability in Statistics