Biomedicine is the lab's core focus. We combine machine learning with patient-specific, in-silico models to turn medical images and clinical data into decision-support tools — learning how geometry, material properties, and blood flow interact to drive disease, and predicting it reliably for individual patients.
We build patient-specific computational models of the cardiovascular system to understand how geometry, wall mechanics, and blood flow interact to drive disease progression. Our work spans two key areas: aortic aneurysm rupture risk and carotid haemodynamics.
For abdominal aortic aneurysms (AAA), we develop virtual patient populations to re-assess rupture risk using detailed neck geometry, going beyond the simple diameter-based criteria currently used in clinics. For carotid haemodynamics, we use in silico modelling to analyse how carotid geometry and flow conditions influence plaque formation and atherosclerosis risk.
The eye presents unique biomechanical challenges — small dimensions, large deformations, and complex fluid-tissue interactions. We apply computational methods to retinal imaging analysis and ocular biomechanics to understand disease mechanisms and improve diagnostic tools.
Tendons and soft connective tissues are highly anisotropic, viscoelastic structures subject to large deformations under physiological loading. We develop high-fidelity finite element models using the Absolute Nodal Coordinate Formulation (ANCF) to capture fibre-level constitutive behaviour, contact, and damage in soft tissue.