My research focuses on using digital health solutions, signal processing, machine learning and ecological momentary assessment to understand the physiological and psychological determinants of symptoms in patients with atrial fibrillation. I am building a research framework for rich data collection using smartphone apps, medical records and wearable sensors. I believe that creating a multidimensional dataset to study atrial fibrillation will yield important insights and serve as model for studying all chronic medical conditions.

Accomplishments and Awards

• 2022 Propelling Original Data Science (PODS) Grant Award: Improving Cardiovascular Disease Detection with a Novel Multi-label Classifier for Electrocardiograms: Capturing Label Uncertainty and Complex Hierarchical Relationships between Output Classes