I am a final-year Ph.D. student at MIT in the Clinical and Applied Machine Learning Group, where I am fortunate to be advised by John Guttag.

My research is at the intersection of machine learning, healthcare, and algorithmic fairness, with a focus on developing techniques to understand and update models in the presence of unreliable data. This work has taken many forms and includes:

1. A method to measure health disparities for underreported outcomes.
2. A study on the impact of coarse race reporting on estimates of algorithmic fairness.
3. A method to improve the efficiency of conformal predictors using test-time augmentation.

Before this, I was at MIT for undergrad, where I majored in computer science with a concentration in South Asian studies. I have also spent time working with lovely collaborators at Microsoft Research (x2), Borealis AI, D.E. Shaw Research, Counsyl, and Aetion.