I am a physician-scientist, cardiac electrophysiologist, and incoming Assistant Professor of Medicine at the University of Pittsburgh / UPMC. My work sits at the intersection of clinical electrophysiology, computational cardiology, signal processing, cardiac imaging, and machine learning, with the goal of improving risk prediction and clinical decision-making for arrhythmia and cardiovascular disease.
My research program focuses on computational cardiac electrophysiology and prediction of arrhythmia and sudden cardiac death, including AI-enabled analysis of ECGs, cardiac imaging, and multimodal clinical data. Current projects include risk stratification for sudden cardiac death and ICD outcomes, early prediction of atrial fibrillation, AI-ECG tools for pulmonary embolism and acute cardiovascular care, and translation of these tools into clinical workflows.
My PhD thesis, under the supervision of Craig Henriquez, PhD, used microscale computational models of cardiac tissue to understand how fibrosis, tissue microstructure, and cellular heterogeneity contribute to abnormal conduction and arrhythmia. That mechanistic foundation continues to inform my current work in arrhythmia risk prediction and computational electrophysiology.
I completed MD/PhD training at Duke University, internal medicine residency at Duke, cardiovascular disease fellowship and clinical cardiac electrophysiology fellowship at UPMC, and T32-supported postdoctoral research at the University of Pittsburgh. I am also an alumnus of The Johns Hopkins University, where I studied Biomedical Engineering and Economics, and co-founded the Triangle Health Innovation Challenge.
