I work on statistical methods and software for machine learning systems that need to stay reliable under uncertainty. My current focus is conformal inference for anomaly detection, uncertainty quantification, and online false discovery rate control.
I am especially interested in settings where data are scarce, noisy, non-stationary, or arrive sequentially. Recent work studies low-data conformal anomaly detection, shift-aware weighted procedures, and practical Python interfaces that turn anomaly scores into calibrated p-values and FDR-controlled decisions.
In parallel to the theory, I build research software for reproducible experiments and applied workflows. The current toolchain includes nonconform for conformal anomaly detection and online-fdr for sequential multiple testing when hypotheses arrive over time.