About Me
Econometrics and Statistics · University of Wisconsin–Madison
I’m a fourth-year Ph.D. student in Econometrics at the University of Wisconsin–Madison, advised by Jack Porter and Harold D. Chiang.
I develop statistical theory for inference with modern nonparametric and machine learning estimators, using generalized U-statistics as a unifying framework. One strand of my research builds local inference procedures for causal parameters like heterogeneous treatment effects, where I show that the standard remedy of sample splitting can be avoided without sacrificing validity — preserving the scarce local information that makes such problems hard in the first place.
A second strand provides the theoretical foundations for this approach, establishing when the jackknife — simple, assumption-light, and widely used — remains valid for the complex, high-order estimators these procedures rely on, extending classical theory to settings it did not previously cover.
