ProfessorSteven Niederer

Professor Steven Niederer’s research focuses on the development and clinical translation of multi-scale computational models of the cardiovascular system. His work sits at the intersection of engineering, physiology, clinical cardiology and machine learning, with the goal of building mechanistic, patient-specific digital twins of the heart that integrate imaging, electrophysiology, biomechanics, haemodynamics and multi-omics data. The overarching aim is to establish quantitative frameworks that explain cardiovascular function across scales, from ion channels and cellular physiology through tissue mechanics to whole-organ performance, and to use these models to inform diagnosis, stratification and therapy.

His research includes the development of cardiac digital twins calibrated from routine clinical data and scaled from individual patients to large cohorts. This includes advances in model reduction, uncertainty quantification, data assimilation and emulator development to make complex biophysical models computationally tractable and clinically deployable. By combining physics-based modelling with machine learning, his group develops hybrid approaches that retain physiological interpretability while achieving the efficiency required for clinical translation and in-silico trials.

A major application area of his work is cardiac arrhythmia, particularly atrial fibrillation and ventricular arrhythmias. He develops computational frameworks to quantify arrhythmic burden, characterise structural substrate such as fibrosis, and predict response to rate or rhythm control strategies. These approaches integrate imaging, electrocardiographic data and mechanistic atrial and ventricular models to support screening strategies, risk stratification and treatment optimisation. His research also connects digital twins with population-scale datasets to inform health policy and evaluate the broader impact of therapeutic interventions.

Professor Niederer’s research addresses cardiac biomechanics and valvular heart disease, combining imaging with myocardial proteomics and transcriptomics to link molecular remodelling to organ-level dysfunction. By embedding omics data within mechanistic models, his work aims to create quantitative bridges between molecular biology and clinical phenotypes. This extends into modelling-informed medicine, where computational physiology is used to accelerate and de-risk drug development through mechanistically grounded simulation.