I'm a PhD student at the Institute for Computational and Mathematical Engineering at Stanford, advised by Ramesh Johari. I work on glucose modeling for type 1 diabetes, collaborating with Ramesh Johari, Matthew Levine, Emily Fox, Dessi Zaharieva, Rayhan Lal, and Dana Lewis. Recently I've been thinking about causal inference, prediction, machine learning, sequence modeling, hybrid modeling, mechanistic modeling, optimization, uncertainty quantification, and evaluating models for time series data with applications in type 1 diabetes.

Before grad school, I was a Fellow in Computer Science at the Harvard Center for Research on Computation and Society where I worked on causal inference and AI for conservation with Lily Xu, Andrew Perrault, Luke Miratrix, and Milind Tambe. I leveraged quasi-experimental data with natural shocks for causal estimation, coupled with machine learning to infer outcomes and confounders when data is imperfect, noisy, or biased. I also empirically studied bandit algorithms for planning anti-poaching ranger patrols. I enjoy working with real-world data to develop causal inference and machine learning methods for socially impactful problems.

I graduated from Harvard College with a degree in Statistics and Computer Science. Previously, I interned at Meta AI.

NSF Graduate Research Fellowship (2023)

Stanford EDGE Fellowship (2023)

Thomas T. Hoopes Prize for Outstanding Senior Thesis (2022)

Highest Honors in Statistics and Computer Science at Harvard (2022)

CRA Outstanding Undergraduate Researcher Award Honorable Mention (2022)

NCWIT National Collegiate Award (2021)

Best Lightning Paper at NeurIPS Machine Learning for Development Workshop (2020)

AAAI Undergraduate Consortium Scholar (2020)

Harvard College Scholar (2019)