The Computational Cardiovascular Science Team

The Computational Cardiovascular Science group is an interdisciplinary team of scientists aiming at translating their background in Engineering, Mathematics, Physics, and Biology to Cardiovascular Research. Each of our teammates has a different range of skill sets and we enjoy combining them to contribute to improving the understanding of the human hearts in health and disease, and to develop new mathematical and computational methodologies for Cardioascular Research.

Visit also our Former Members section.

NamePictureAreas of InterestContact
Blanca Rodriguez

Wellcome Trust Senior Research Fellow in Basic Biomedical Sciences

Professor of Computational Medicine

Principal investigator within the BHF CRE
Blanca Rodriguez
Computational Cardiac Electrophysiology

Mechanisms determining variability in the response of the human heart to anti-arrhythmic therapy in acute myocardial ischaemia, hypertrophic cardiomyopathy and atrial fibrillation.
Blanca's profile
Kevin Burrage

Formerly Professor of Computational Systems Biology, Department of Computer Science, from 2007-2015. Now, Visiting Professor to the Department of Computer Science 2016-2019.

Professor of Computational Mathematics, QUT, Brisbane, Australia
Development of Novel modelling and simulation approaches in Computational Biology

To capture the underlying variability in dynamical processes and to characterise the heterogeneity in biological tissues through stochastic and nonlocal multiscale techniques.
Kevin's profile
Vicente Grau

Professor of Biomedical Image Analysis

Director of Centre for Doctoral Training in Healthcare Innovation
Development of novel biomedical image analysis algorithms

Combination with computational models, and its applications in cardiovascular and pulmonary medicine, and in the analysis of biological images.
Vicente's profile
Yoram Rudy

Visiting Professor of Computational Medicine, Washington University in St. Louis
Yoram Rudi
Cardiac Bioelectricity, Electrophysiology and Arrhythmias, Computational Biology and Modeling of the Heart, Cardiac Electrical Imaging and Mapping

Understanding the mechanisms that underlie normal and abnormal rhythms of the heart at various scales, from the molecular and cellular to the whole organ. The development of novel non-invasive imaging modalities (Electrocardiographic Imaging, ECGI) for the diagnosis and guided therapy of cardiac arrhythmias.
Yoram Rudy´s Profile

Alfonso Bueno-Orovio

British Heart Foundation Intermediate Basic Science Research Fellow

Data-driven modelling and simulation for Cardiovascular Research

Merging of clinical and experimental data and computational modelling techniques for improved characterisation of cardiac electrophysiology.
Alfonso's profile
Ana Minchole

Senior Postdoctoral Researcher
Ana Minchole
Cardiac electrophysiology and biomedical signal processing

To provide selective biomarkers from the electrocardiographic signal for the prediction of cardiac arrhythmias either drug-induced or due to pathological conditions.
Ana's profile
Oliver Britton

Postdoctoral Researcher
Modelling human dorsal root ganglion neurons for pain researchDeveloping models of human nociceptors to understand mechanisms of ion channel mutations involved in pathological pain signalling,and to identify potential therapeutic strategies. Oliver's profile
Elisa Passini

Senior Postdoctoral Researcher
Elisa Passini
Computational cardiac electrophysiology and arrhythmias
In silico drug testing and modelling of human hypertrophic cardiomyopathy
Elisa´s profile

Ernesto Zacur

Postdoctoral Researcher

 Ernesto Quantitative characterization of cardiac tissue microstructure from Diffusion Tensor Imaging.
Development of advanced analytical tools for the quantitative characterization of cardiac anatomy from structural MRI and in cardiac tissue microstructure from DTI.
Xin Zhou

Postdoctoral Researcher

The development and use of mathematical models of human ventricular cell electrophysiology 

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 disorder of ion channels, and drug responses in human..

Xin's profile
Linford Briant

Sir Henry Wellcome Postdoctoral Fellow
Linford B
Mechanisms regulating glucagon secretion from pancreatic alpha-cells Linford's profile

Francesc Levrero

Postdoctoral Researcher


Modelling the electro-mechanical behaviour of the heart

Patient-specific modelling of the coupling between the electrical and mechanical phenomena in the human heart by using medical imaging and finite element simulations in high-performance computing platforms.

Francesc's profile
Aurore Lyon

Postdoctoral Researcher

Computational modelling and simulations for cardiac electrophysiology

Using computational methods (clustering, machine learning, modelling and simulation) to analyse and interpret cardiac electrophysiological data for risk stratification and patient management, with a specific focus on hypertrophic cardiomyopathy.


Francesca Margara

Visiting Fellow, University of Bologna


In silico modeling of mutations

Mutations in the cardiac sodium channel gene, associated with various arrhythmia syndromes.

Pablo Lamata

Visiting Fellow, KCL
Pablo Lamata The development and clinical adoption of solutions exploiting the synergies between image analysis and physiological modelling

Currently focusing on three specific areas: anatomical shape analysis, extraction of diastolic biomarkers (myocardial stiffness and decaying active tension) and non-invasive estimation of central blood pressure.
Pablo's profile
Annamaria Carusi

Visiting Fellow, University of Sheffield Medical Humanities
Philosophical and social aspects of modelling and medicine Annamaria's profile
Karen Barnes

PA to Professor Blanca Rodriguez
 Karen Barnes Personal Assistant to Professor Blanca Rodriguez Karen's profile
Patricia Benito

Project Manager
Project management Patricia's profile
Chris Kelly

(DPhil in Engineering Science, 2012-2016)
Registration of multi-sequence Cardiac MR images

With application to the analysis of pathological cardiac remodelling.
Chris's profile
Tasos Papastylianou

(CDT in Healthcare Innovation, 2012-2016)
Analysis of MRI scans in myocardial infarction Tasos' profile
Iulia Popescu

(CDT in Healthcare Innovation, 2013-2016)
Lulia popescu
Linking cardiac structure to mechanical function using images Iulia's Profile
Benjamin Villard

(CDT in Healthcare Innovation, 2013-2017)
 Assessing myocardial infarction with state-of-the-art Magnetic Resonance Imaging Ben's profile
Jakub Tomek

(DTC in Life Sciences Interface, 2013-2016)
 Jakub Tomek The role of sympathetic nervous system in heart diseaseCombining in silico and experimental research of potential anti-arrhythmic role of sympathetic nervous system in the post-infarction border zone Jakub´s profile
Hector Martinez

(DPhil in Computer Science, 2015-2018)
 Hector M High performance computing and mathematical modeling to simulate cardiac electrophysiological activity and arrhythmic risk Hector´s profile
Cristian Trovato

(DPhil in Computer Science, 2016-2019)
Cristian Trovato
 Drug effects on the cardiac Purkinje system Cristian's profile
Peter Marinov

(DPhil in Computer Science, 2016-2019)
 Peter M.  A modeling and ECGi approach to Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) Peter's profile
Adam  McCarthy

(DPhil in Computer Science, 2016-2019)
 Adam  Machine learning for cardiovascular data Adam's profile
Polina Mamoshina

(DPhil in Computer Science, 2016-2019)
 Polina  In silico and ab initio modeling of drug-induced cardiotoxicity
Detection of cardiotoxicity using supervised and unsupervised machine learning techniques. Analysis of signaling pathway dysregulation linked to drug-induced cardiotoxicity
Polina's profile

Julià Camps

(DPhil in Computer Science, 2017-2020)

 Julia Machine learning for ECG signals Julia's profile

Hao Xu

(DPhil in Computer Science, 2016-2019)

  Biomedical Image Analysis. 

Automated segmentation of Cardiac MRI scans based on deep learning. 
Generative Adversarial Networks to enhance cardiac training databases.