Dr. Chris Cantwell obtained a First Class honours degree in Mathematics at the University of Warwick in 2005. He subsequently completed an MSc and PhD in Scientific Computing at the University of Warwick's Centre for Scientific Computing, studying the transient growth of small disturbances to fluid flow in a linearly stable regime. He moved to Imperial College London to join Professor Spencer Sherwin’s group, initially developing high-order spectral/hp element methods, before being awarded a 3-year Advanced Training Award from the British Heart Foundation to apply numerical modelling to address challenges in the understanding and treatment of atrial arrhythmias.
Dr. Chris Cantwell's research is centred around developing novel and scalable numerical approaches for efficiently modelling and understanding complex physical processes in the aerodynamics and biomedical domains. Much of his work to date has focused on the efficient implementation and application of spectral/hp element methods for performing high-fidelity numerical simulation and making these tools more accessible to users without a detailed understanding of the numerical methods. However, driven by applications in biomedicine, his research interests now include the fusion of numerical modelling with statistical methods and machine learning. He is a strong proponent of open-source software and is a project leader of the Nektar spectral/hp element framework which acts as a vehicle for much of his research.
et al., 2019, Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling, Computers in Biology and Medicine, Vol:104, ISSN:0010-4825, Pages:339-351
Cantwell CD, Nielsen AS, 2019, A Minimally Intrusive Low-Memory Approach to Resilience for Existing Transient Solvers, Journal of Scientific Computing, Vol:78, ISSN:0885-7474, Pages:565-581
et al., 2018, A novel approach to mapping the atrial ganglionated plexus network by generating a distribution probability atlas, Journal of Cardiovascular Electrophysiology, Vol:29, ISSN:1045-3873, Pages:1624-1634
et al., 2018, Analytical approaches for myocardial fibrillation signals, Computers in Biology and Medicine, Vol:102, ISSN:0010-4825, Pages:315-326
et al., Approximating the solution of Surface Wave Propagation Using Deep Neural Networks, INNS Big Data and Deep Learning 2019, Springer, ISSN:2661-8141