- Email: email@example.com
- Thesis title: Left Ventricular Remodelling: Integrating flow imaging and physics-informed machine learning into patient-specific cardiac models
Fergus graduated from the University of Leeds with a first class Bachelor’s degree in Mathematics in July 2019, receiving the Kuznetsov prize for his final year project in numerical methods. The following September he joined the fluid dynamics centre for doctoral training (CDT) at Leeds, undertaking a masters project studying the extrusion of non-Newtonian pastes. He began his PhD in 2020, working in biomedical flows.
Fergus’s primary interest lies in the application of physics-driven machine learning to cardiovascular flow problems. In modelling haemodynamic flows, the sparsity of available data precludes the usage of purely data-driven approaches, whereas the incorporation of known physical laws and boundary conditions allows physics-driven methods to operate effectively in this sparse regime.
- BSc Mathematics, University of Leeds, 2019