I chose the Pitt/CMU MSTP for the cross-collaboration between UPMC's clinical work and Carnegie Mellon's computational prowess. Pitt/CMU is one of the few MSTPs where you can pursue a computational PhD while getting clinical training at a major academic medical center. I wanted to build machine learning models grounded in real clinical problems, and the bridge between CMU's ML faculty and UPMC's massive health system data makes that uniquely possible here.
Outside of the program, I like cooking, fantasy football, and survivor!
- Consultant, McKinsey & Co., 2022-2024
- Duke University, BS in Mathematics, 2022
Education & Training
My research sits at the intersection of machine learning, mathematics, and medicine. I develop interpretable ML models for large-scale health data -- currently spanning ophthalmology (predicting early-onset macular degeneration, identifying systemic associations of epiretinal membrane), infectious disease (personalized models of TB treatment effects), and cancer genomics (growth dynamics of ductal carcinoma in situ). I work with the All of Us dataset and collaborate across departments at both Pitt and CMU.
Outside of research, I created and lead-instruct an Applied ML in Medicine course for medical students and co-founded the Pitt AI in Medicine Student Society. I currently compete in the PuNCH (Pitt Unconventional Challenge - Startup funding) and continue to *try* to build a successful startup with some friends of mine :)