I am a physician and clinical AI informaticist at Suki AI, with a background in clinical informatics, research, and software development. My interest in programming and software design started in high school, and while I studied biology in college, I never strayed far from the technical side of things. My dual focus of life sciences and computing shaped everything that followed.
At Georgetown University School of Medicine, I honed my clinical skills while continuing to pursue informatics work on the side. I was drawn to the question of what we could learn from the data already sitting in clinical systems, and how software could help us get there faster.
The COVID-19 pandemic changed the trajectory of my career. There was a massive volume of clinical data and almost no answers. I threw myself into critical care research, studying prone positioning in ventilator management, renal replacement therapy, and novel therapeutics in the ICU. That period cemented my passion for real-world evidence: using the messy, complex data generated by routine clinical care to answer questions that matter right away, not years from now.
After completing a year of Emergency Medicine residency, I transitioned to full-time informatics and real-world evidence research at Atropos Health. There, I used large-scale electronic health record data and causal inference methods, including propensity score matching, retrospective cohort design, and high-dimensional analysis, to study clinical questions across a wide range of specialties. My published research spans cardiology, neurology, endocrinology, critical care, substance use, health equity, and more, including work in the European Heart Journal, Journal of Intensive Care Medicine, and Frontiers in Neurology.
In August 2026, I joined Suki AI as a Clinical AI Informaticist.
My work in AI builds on that foundation in medicine, evidence, and software. My research includes using large language models, retrieval-augmented generation, and agentic systems to answer clinical questions.