My research relates to developing computational models and mathematical tools for analyzing pre-clinical and clinical data to better understand complex biological systems and phenomena, with a particular focus on atrial electrophysiology and arrythmias.
I develop computational electrophysiology models and simulation tools to better understand complex mechanisms underlying atrial electrophysiology. By utilizing multiscale computer models, I investigate the intricate interactions between electrophysiological mechanisms at cell, tissue and organ scales. I am particularly interested in how these mechanisms naturally maintain a homeostatic state and, more importantly, the specific failure points that trigger the transition into pathological conditions like atrial fibrillation (AF).
A central pillar of my current work is the integration of high-resolution clinical data for model personalization. I utilize unipolar atrial electrograms recorded during open-heart surgeries to understand electropathology related to AF in different patients or groups. Beyond explaining current observations, I use these in silico models to explore patient-specific AF progression trajectories. This approach is essential for extrapolating information from limited data sets where long-term clinical observations are not available.
Ultimately, my research aims to develop predictive tools that reduce the time and resources needed to answer scientific questions, providing a data-driven foundation for personalized prevention and treatment strategies in cardiac care.
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