About
PhD student in statistics at the University of Washington, advised by Daniela Witten, and an Amazon A.I. Fellow. Currently working on uncertainty quantification after data-driven model/hypothesis selection and large-scale experimentation.
Previously:
- Fulbright scholar with Bernhard Schölkopf at the Max Planck Empirical Inference Department
- Research Scientist and M.S. in Biomedical Engineering with Joshua Vogelstein in the NeuroData Lab.
- B.S. in Applied Mathematics & Statistics from Johns Hopkins University.
Recent updates
- 10/2026 “Infer-and-widen, or not?” has been accepted for publication at Statistical Science!
- 09/2026 I have begun an internship with the Amazon Weblab team in Seattle, working to improve large scale experimentation!
- 04/2026 Our invited review paper “Inference conditional on selection: a review” will appear in the Annual Review of Statistics and Its Application! We argue for conditional guarantees and provide a unifying perspective on strategies to obtain them.
- 01/2026 Our preprint “Post-selection inference for penalized M-estimators via score thinning” is now on arXiv! I am particularly excited by this work as it presents novel Berry-Esseen-type bounds and general, simple-to-implement methodology (see code).
- 12/2025 I will be presenting my work on asymptotic score thinning at the 2025 ICSDS conference in Seville!
- 09/2025 I am giving an invited talk at the RIKEN Workshop on Trustable Data-Driven Science in Tokyo on asymptotic data thinning. See my talk here.
- 09/2025 I have been awarded an Amazon AI PhD Fellowship!
- 08/2025 - “On the minimum strength of (unobserved) covariates to overturn an insignificant result” has been accepted to Statistical Science!
- 06/2025 - “Inference on the proportion of variance explained in principal component analysis” has been accepted to JASA!