Thomas Bury, McGill University
Critical transitions --- abrupt, qualitative changes in a system's dynamics --- occur in a wide range of natural systems, from the human heart to the earth's climate. In recent years, substantial research has focused on identifying early warning signals for these transitions using insights from dynamical systems theory and stochastic processes. In the first part of my talk, I will present our work on combining deep learning with dynamical systems to signal impending critical transitions. I will showcase our approach using data from various disciplines, including physiology, geology, engineering, and paleoclimatology.
In the second part of my talk, we will explore the dynamics of cardiac arrhythmia, a condition that poses many challenges in prediction and risk evaluation. I will present our research that combines mathematical modeling, deep learning, clinical data and laboratory experiments to gain insight into the dynamics associated with the onset of cardiac arrhythmia. I will highlight how advancements in cardiac monitoring technologies are opening up exciting opportunities at the interface of cardiology and mathematics.