I started my career as a physicist in training, beginning with my Bachelor's degree at Vanderbilt University in Nashville, USA. I majored in physics, applied mathematics and English literary criticism, and conducted research in collider simulations and computational high-energy physics. I later began a PhD programme in particle physics at Texas A&M University, where I worked for three years on research involving phonon transport in cryogenic semiconductor devices for detector and quantum information science applications.

I am currently completing the Erasmus Mundus Joint Master’s in Interdisciplinary Mathematics, with studies across the Università degli Studi dell'Aquila, Technische Universität Wien, and the Universitat Autònoma de Barcelona. My training spans mathematical modelling, numerical methods, optimisation, data analysis, and machine learning, and I particularly enjoy applying these methods to complex scientific and biomedical problems.

At the Digital Health Unit, I work on CardioGPT, a research project for the automated analysis of cardiac magnetic resonance imaging. My work focuses on developing and validating segmentation-based methods that convert cardiac images into interpretable measurements of regional heart motion. This includes extracting clinically meaningful features, building visualisation and quality-control tools, and evaluating machine-learning approaches for identifying regional wall-motion abnormalities.

More broadly, I am interested in scientific machine learning; I take special interest in the development of computational systems that remain understandable and useful to domain experts. I am motivated by problems that require mathematical reasoning, robust software, and close collaboration between technical researchers and specialists in the field.

On my day off you'll find me playing the classical guitar, writing science fiction, cooking, and learning about the history and architecture of the places in which I live.