Hybrid physiological modeling and evaluation for health monitoring from wearable data

I'm a PhD candidate at the Institute for Computational and Mathematical Engineering (ICME) at Stanford, advised by Barbara Engelhardt and collaborating with Emily Fox. My research focuses on advancing interpretable, hybrid physiological modeling frameworks by integrating causal biological mechanisms with probabilistic machine learning. My goal is to move beyond standard ML benchmarks to build tools that prioritize real-world clinical utility, safety, and usability. Leveraging continuous wearable sensor data, I currently apply these methods to two domains: Type 1 diabetes decision support and women’s health monitoring. 

Prior to Stanford, I studied Statistics and Computer Science at Harvard and developed methods for causal inference and AI for conservation. My industry experience includes internships at an Apple research team and Meta AI.

NSF Graduate Research Fellowship

Stanford EDGE Fellowship

Thomas T. Hoopes Prize for Outstanding Senior Thesis

Highest Honors in Statistics and Computer Science at Harvard

CRA Outstanding Undergraduate Researcher Award Honorable Mention

NCWIT National Collegiate Award

Best Lightning Paper at NeurIPS Machine Learning for Development Workshop

AAAI Undergraduate Consortium Scholar

Harvard College Scholar