Research

“We do not have knowledge of a thing until we have grasped its cause.” — Aristotle

I believe the world is shaped by causal forces, and I am committed to understanding their structure through rigorous statistical methodology. Modern datasets are vast and deeply informative; statistical models distill the patterns they contain and illuminate the mechanisms that govern real systems. When principled modeling meets domain knowledge, data cease to be mere observations: they become evidence of how and why things happen.

Guided by this perspective, my research and career interests lie at the intersection of causal inference and high-dimensional statistical modeling.

Benign Overfitting beyond Prediction

Estimation and inference with partially regularized, overparameterized least squares, with applications to covariate adjustment in randomized experiments.