Evangelos K. Oikonomou, MD, DPhil

Evangelos (Evan) Oikonomou is a clinical fellow in cardiovascular medicine, a post-doctoral research fellow in the Cardiovascular Data Science (CarDS) lab, and a member of the ABIM Physician-Scientist Research Pathway at Yale. His work focuses on the intersection of applied computer vision and statistical machine learning, with a specific focus on developing tools for the improved phenotyping of cardiovascular disease using scalable approaches that can be deployed at minimal cost using existing care pathways. He graduated as valedictorian of his class from the University of Athens Medical School in Greece, before pursuing a Ph.D. (D.Phil.) degree at the University of Oxford, where he was recognized with the Radcliffe Department of Medicine Graduate Prize for his scientific work. In 2019 he joined the Physician-Scientist Training Program at the Yale School of Medicine, and he has since completed his internal medicine residency and his core clinical fellowship in cardiology.

He is a recipient of an F32 NRSA fellowship award from NHLBI (National Institutes of Health), and his work has been recognized through numerous Young Investigator Awards sponsored by the American Heart Association, American College of Cardiology, Northwestern Cardiovascular Young Investigator Forum, the European Society of Cardiology and European Association of Preventive Cardiology.

He has led a broad portfolio in applied artificial intelligence in cardiovascular and cardiometabolic medicine. First, he has defined and translated a key interplay between the perivascular adipose tissue and vascular inflammation in humans into a clinically actionable algorithmic tool that can refine cardiovascular risk on routine cardiac CT scans. Second, he has developed and validated deep learning algorithms for the efficient diagnosis of common and rare cardiomyopathies specifically adapted for point-of-care echocardiography. Finally, he has led an extensive body of work on defining treatment effect heterogeneity across clinical trials, with direct implications for evidence translation and the design of new adaptive trials with data-driven predictive enrichment.

His work has been published in several peer-reviewed journals, including the Lancet, Lancet Digital Health, European Heart Journal, JACC, Circulation, JAMA Cardiology, Diabetes Care.