We specialise in developing models of personalised physiology to simulate different treatment approaches. An example application is developing engineering methodologies to personalise treatment approaches for cardiac arrhythmias. We use a combination of signal processing, machine learning and computational modelling techniques to develop novel methodologies for investigating cardiac arrhythmia mechanisms from clinical imaging data and electrical recordings. We aim to translate the tools we develop for analysing electrical and imaging data to clinically predict optimal patient specific treatment strategies. We are based at the Digital Environment Research Institute and the School of Engineering & Materials Science, Queen Mary University of London. We are also a part of the Centre for Advanced Cardiovascular Imaging.