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Programme

Confirmed Speakers

Denis Noble (Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom):
The history of cardiac computational modelling of electrophysiology from relaxation oscillators, to Hodgkin-Huxley, Markov, big data and AI: are we nearly there yet? 

Eva Rog-Zielinska (IEKM, University Medical Center Freiburg, Freiburg, Germany):
3D time-resolved electron microscopy: a contradiction in terms?

Jan Lebert (Cardiac Version Laboratory, University California San Francisco, USA):
Mapping cardiac electrics and mechanics at high spatio-temporal resolution: AI to the rescue?

Sandy Engelhardt (Artificial Intelligence in Cardiovascular Medicine, University Hospital Heidelberg, Heidelberg, Germany):
Cardiac macroscopy: how to see the wood for all the trees

Michael Gotthardt (Translational Cardiology and Functional Genomics, Max Delbrück Center, Berlin, Germany):
From alternative splicing to Frank-Starling: can cardiac mechanics be quantitatively conceptualised bottom-up? 

Andrew Taberner (Auckland Bioengineering Institute, Bioengineering Institute, New Zealand):
Optimised wet-lab instrumentation for dry-lab research into cardiac structure and function: how to engineer the bi-directional cross-talk between the analogue world and its digital representation

Daniel Hook (CEO at Digital Science, Centre for Science and Policy, University of Cambridge, Cambridge, United Kingdom):
Research data management at the interface between the analogue world and its digital representation 

Natalia Trayanova (Department of Biomedical Engineering, Johns Hopkins Medicine, Baltimore, MD, USA):
Clinical translation of cardiac modeling and image analysis: modelling to the rescue!

Blanca Rodriguez (Department of Computer Science, University of Oxford, Oxford, United Kingdom):
Modelling drug effects on cardiac function for personalised medicine: hope or hype... 

Igor Efimov (Cardiovascular Engineering Laboratory, Northwestern University, USA):
Real-time arrhythmia detection and termination using ML-based approaches