About me

I am a fifth-year Ph.D. student in the Department of Statistics at UC Berkeley, advised by Ryan Tibshirani and Giles Hooker. Before coming to Berkeley, I completed my B.S. at Yale University, where I did NLP research with the late Dragomir Radev.

Previously, I interned in the computer vision group at Apple, designing interpretability methods to analyze failure modes of FaceID. I also worked on computational pathology at Genentech, and speech recognition at a startup in Israel. I am looking for full-time roles starting mid-2026.

Research Interests

My research aims to ensure the safe and effective deployment of machine learning. To that end, my work on black-box interpretability enhances the reliability of SHAP and LIME, and introduces new techniques to understand image models. I also work on uncertainty quantification, with particular focus on rankings and time series.

While I am broadly interested in ML for health, my current applied emphasis is in epidemiology. Through the Delphi Group, I construct analytical and algorithmic tools to track infectious disease risks as they unfold in real-time.

Please feel free to reach out if you’d like to connect!

News

• Aug 2025. Gaussian Rank Verification published in Stat.
• July 2025. Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks presented in the Actionable Interpretability Workshop at ICML.
• July 2025. Statistical Significance of Feature Importance Rankings shared at UAI. Our methods stabilize SHAP, LIME, and other interpretability methods using ideas from Gaussian Rank Verification.
• Dec 2024. Challenges in Estimating Time-Varying Epidemic Severity Rates from Aggregate Data is up on MedRxiv.