Hi! I am a postdoc at Cornell, working with Emma Pierson. I completed my Ph.D. in the Clinical and Applied Machine Learning group at MIT, where I was lucky to be advised by John Guttag.
Before, I was at MIT for undergrad, where I majored in computer science with a concentration in South Asian studies.
I work on machine learning for healthcare. My current research (often) falls into one or more of these categories:
Measuring human behavior in health datasets
I believe that we can improve healthcare not just by training better predictive models, but also by building better *descriptive* models of how care is currently delivered.
> How can we measure the extent to which diseases are underdiagnosed in different patient subgroups? (NWH 2024)
> How can we measure different patterns of health access? (under review)
> How can we measure overtreatment? (in progress)
Updating and evaluating machine learning models
There is substantial room to improve the ways we update, evaluate, and select machine learning models.
> How can we efficiently update models to be more accurate, robust, and calibrated? (ICCV 2021, under review)
> How can we facilitate semantically-grounded, context-specific evaluation? (CHI 23)
> How can we best evaluate classifiers in the absence of abundant labeled data? (under review)
Promoting health equity
Can we use AI to characterize and mitigate persistent health inequalities? I (and many of my co-authors) would
say yes!
I am especially committed to translating advances in machine learning to women's health.
> How can we better measure the prevalence of intimate partner violence? (NWH 2024)
> What features are important when predicting ovulation? (SR 2023)
> What new opportunities in health equity do large language models enable? (NEJM AI 2025)
Sometimes I describe my interests as “everything but model training”. This is because 1. I am impatient and 2. I believe the road from messy to clean data and the road from trained model to deployment raise important unanswered questions.