My research focuses on statistically principled machine learning systems for uncertain and dynamic environments.
Conformal Anomaly Detection
Anomaly detectors often produce scores without calibrated uncertainty. I study conformal methods that convert such scores into p-values and enable principled decisions with error control. Recent work includes leave-one-out, bootstrap, and cross-conformal anomaly detectors.
Low-Data and Shifted Settings
Real data often violate the clean exchangeability assumptions behind standard conformal methods. I am interested in weighted and adaptive conformal procedures that remain useful under data scarcity, local non-stationarity, and distribution shift.
Online Error Control
Many applications require decisions while data arrive sequentially. I work with online false discovery rate control and related sequential testing procedures for settings where discoveries need to be made before the full dataset exists.
Research Software
I build Python libraries around these ideas, including nonconform for conformal anomaly detection and online-fdr for online multiple testing.