British Heart Foundation Intermediate Basic Science Research Fellow

Associate Professor of Computational Medicine

Principal investigator within the BHF CRE

Visiting Professor, 2016-2019.

Professor of Computational Mathematics, QUT, Brisbane, Australia

To investigate the effect of variability in the generation of potential pro-arrhythmic conditions in human ventricles. Especially, the modelling of the ionic mechanisms underlying excitation-contraction coupling, genetic disorders in ion channels, and drug response in human.

Postdoctoral Researcher

Developing 3D computational models of the heart to investigate the effect of hypertrophic-related heterogeneities in the electrical properties of the myocardium on its susceptibility to arrhythmia. 

Postdoctoral Researcher

Develop fast computational toolkits to reproduce the heart structure and function, including ionic dynamics and activation (electrophysiology), contraction and pump blood (mechanics) and scar growth and remodelling (biochemistry), and search key mechanisms in myocardial infarction (MI), aiming to aid clinical management and drug development for MI. 

Aditi Roy

Postdoctoral Researcher

Development of multiscale computer models of human atrial electrophysiology, built through the integration of multimodality datasets. Investigate variability in atrial fibrillation mechanisms through computer simulation studies using populations of models, informed by clinical datasets. 

Aditi's profile

Rubén Doste

Postdoctoral Researcher

Computational modelling and simulation in Hypertrophic Cardiomyopathy

Development of personalised human ventricular models and conduction of high performance computing studies to investigate risk stratification and tailoring of pharmacological therapy in human hypertrophic cardiomyopathy.

Interdisciplinary investigation of cardiac function

Using a combination of detailed human-based computational modelling with wet-lab experiments and data analysis to understand the mechanisms underlying cardiac [dys]function, with focus on arrhythmogenesis.

DPhil in Computer Science, 2017-2020

DPhil in Computer Science, 2015-2018

Marie Curie DPhil Fellow, 2018-2021

Personalised models of human cardiac electromechanical physiology to predict the safety and efficacy of pharmacological action in the individual subject.

DPhil in Computer Science, 2015-2019

Automated segmentation of Cardiac MRI scans based on deep learning. Anatomical and functional analysis of left ventricle.

Marie Curie DPhill Fellow, 2018-2021

Synergies of cardiac MRI and computer models to address hypertrophic cardiomyopathy: CMR analysis, based on deep learning approaches and combination of ML techniques and personalised models.

Development of human multiscale biventricular bidomain models of HCM patients to include patient-specific ischemic and fibrotic burdens, to stratify arrhythmic risk.

Computational investigation into the interplay between pro-arrhythmic and mechanical abnormalities

Investigating population variability in cardiac excitation-contraction coupling to improve drug safety profiling and arrhythmic risk stratification'. My DPhil is technically in 'Ion Channels and Disease'.

My main research interest is how we can use computer simulation to provide a more ethical, accurate, faster and cheaper representation of biomedical processes. In my final year of my Master's degree I was part of a group research project aiming to predicting alarm validity by using machine learning on ECG signals, which sparked my interest in cardiovascular science. 

Use of induced pluripotent stem cell derived cardiomyocytes to test the consequences of genetic variants in atrial and ventricular arrhythmias

Using iPSC-CM cellular systems and computational modelling to investigate the impact truncating variants in the gene titin have on atrial fibrillation and ventricular arrhythmias

Atrial state characterization and treatment improvement in the context of atrial fibrillation 

Brief description: To deliver new technologies and novel strategies for individualized characterization of atrial fibrillation substrate to and increase treatments’ efficiency.

Dissecting disease heterogeneity in heart failure patients using multi-modal machine learning, modelling, and simulation methods.