• Physiological boundary conditions for Navier-Stokes flow
• Physics-informed neural networks as surrogates for PDE solutions
• Coronary blood flow, and its control mechanisms in health and disease
• Convergence and accuracy of numerical schemes for PDEs
• Adaptivity for automatic error control
• Finite element analysis
• Mono/bidomain simulation of cardiac electrical activation

I received my MMath (mathematics) degree from Warwick in 2008, followed by a DPhil in Computational Cardiac Electrophysiology from Oxford in 2013. During my doctoral research, I developed a strong interest in the use of numerical computation for answering difficult questions in biology, and in particular, how we can design numerical tools so that they are as fast, powerful, and useful as possible to researchers.

Upon completing my doctorate, I joined the Figueroa Lab at KCL as a Research Associate, working on a project to design large-vessel Navier-Stokes blood flow boundary conditions which accurately model the properties of distal vascular beds. I am interested in determining the effect of these models on the global haemodynamics, and in the sorts of questions we can answer when we can accurately determine the blood flow to particular organs, with reference to the disease state of the whole cardiovascular system.

More recently, I was awarded the King's Prize Research Fellowship, to support research into coronary artery tree reconstruction from angiographic data. This involves employing neural networks to extract maximal useful information from clinical images, and investigating how physics-informed neural networks can encode coronary flow patterns in a complementary fashion to traditional FEM methods.