Thomas M. Bury

This paper uses reinforcement learning to discover new mechanisms of reentry in excitable media, including subthreshold stimulation strategies that induce unidirectional propagation and reentry.

The paper uses optogenetic stimulation and computational models to analyze phase resetting in human iPSC-derived cardiomyocytes, showing how it explains cardiac arrhythmias and offers strategies to prevent them.

T-World is a computational model of human ventricular myocytes that accurately replicates physiological processes and identifies arrhythmia risks, with applications in drug response prediction.

FateNet predicts cell fate decisions using scRNA-seq data by integrating dynamical systems theory and deep learning, identifying key genes in hematopoiesis.