Director of Data Science at Anumana

I am currently the Director of Data Science at Anumana, a Cambridge‑based digital cardiology company spun out of nference and the Mayo Clinic. Our team is developing cutting‑edge ECG‑AI algorithms that can flag cardiac conditions such as low ejection fraction, hypertrophic cardiomyopathy, and pulmonary hypertension from a standard 12‑lead ECG, enabling earlier intervention and better patient outcomes.

Previously, I served as Director & Head of Data Sciences at nference, where I led projects that mined multi‑modal EHR data to generate real‑world evidence and improve clinical decision‑making. Our COVID‑19 studies were cited by the White House, CDC, and other government agencies.

I earned my Ph.D. from the MIT Operations Research Center in 2019, advised by Prof. Dimitris Bertsimas. My dissertation focused on machine‑learning methods for healthcare datasets with missing or uncertain values, including the co‑development of OptImpute, an optimization‑based imputation technique now used by health‑tech researchers worldwide. I am passionate about using machine learning and mathematical models to improve clinical care. If you are also interested in these types of problems and would like to chat, feel free to reach out!

Outside of work you’ll find me running, weightlifting, solving puzzles, or playing fetch with my dog, Lucy.