Fellow Focus in Four: Evangelos Oikonomou, MD, DPhil, Cardiovascular Medicine

I was born and grew up in Athens, Greece. My parents are pharmacists who worked night and day in their family-run pharmacy for almost 40 years. My grandfather was a family medicine doctor in rural Greece. Influenced by my family’s impact on their local communities, I wanted to pursue a field that combines my love for biology and discovery with direct patient care.

After completing medical school training in Athens, I moved to the United Kingdom, where I completed a PhD in medical sciences. I have always been interested in biomedical research and innovation, which I consider an integral part of clinical science. In 2019, my wife and I decided to explore our career options in the U.S. because of the strong focus on clinical and research training across most graduate medical education programs. We interviewed across the country and were excited to couples-match at Yale. Five years later, we are still here, having completed our residencies. We are currently pursuing our fellowships—hers in pediatric emergency medicine and mine in cardiovascular medicine.

I interviewed for the Physician-Scientist Training Program and was excited to see the breadth of research across the university, specifically in biomedical data science, cardiovascular imaging, and applied artificial intelligence. During my residency, I met my current mentor, Rohan Khera, MD, MS, and joined the Cardiovascular Data Science (CarDS) Lab, an amazing group of clinicians, data scientists, and computer scientists who share the same vision of improving medical care through new data-driven approaches. I knew Yale was going to guarantee outstanding clinical training in cardiology while continuing to support my academic career goals.

The principal objective of my research focuses on translating novel artificial intelligence methods into valuable and actionable tools that can improve the quality of cardiovascular care for patients. I am particularly interested in how we can combine biomedical knowledge with novel machine-learning methods to define noninvasive biomarkers that would enable the timely detection of cardiovascular disease. This spans different imaging techniques, such as optimizing cardiac ultrasound algorithms to efficiently screen valvular disease in the community or adding a new layer to the traditional interpretation of cardiac CT scans that can provide insights into vascular inflammation. I am also interested in developing and implementing statistical machine-learning tools to design more efficient clinical trials. Of course, this is a team effort, and none of my work would be feasible without the support of my colleagues and mentors across the CarDS Lab and Yale Cardiology.

I love tennis, but I am better at watching than playing. My goal is to complete my own "career Grand Slam" by attending all four Grand Slam finals—I have attended two out of four so far.

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