I am currently a PhD researcher in the Leeds Computational Physiology Lab, as part of the Cellular Cardiology group where I currently study and produce computational models of cardiac myocytes to study fibrillatory diseases, and how tools such as machine learning, genetic algorithms and image analysis can be utilised to further research within this field.
I originally studied BSc Physics as an undergraduate here at the University of Leeds, where I specialised in astrophysics and biophysics, before pursuing an MSc in Data Science and Analysis (University of Leeds). My BSc project looking at computational modelling of anti-chemotherapeutic drugs for an entropic analysis, and MSc project utilising machine learning to produce a dynamic population system to study the predator-prey relationships of extinct organisms through their fossil records gave me a unique cross-disciplinary skillset and understanding in applying mathematics, physics and computational science to study dynamic and complex processes, such as those within biological systems. A personal interest in cardiovascular diseases led me to my current research area where I study at the cross-section of maths, physical sciences, computing and biomedical science.
My primary research interest lies within dissecting the multi-scale mechanisms of atrial fibrillation through the use of computational modelling. The Leeds Computational Physiology Lab's current research projects can be found, along with the code for the models themselves, at www.physicsoftheheart.com
I am also interested in systemic analysis, machine learning, genetic algorithms and image analysis - and how the applications of these tools can further current research within the cardiovascular field.
- BSc Physics (University of Leeds)
- MSc Data Science and Analytics (University of Leeds)