Project: A combined framework of machine learning and extreme value theory for anomaly detection and ranking

2021-10-01 – 2025-09-30

Abstract

Anomaly detection is frequently encountered in quantitative research in the life sciences. Conventional machine learning methods are not fully adapted to deal with the identification nor the ranking of rare events. In this PhD track, we aim to combine machine learning methods with principles of extreme value theory, a field in statistics especially suited to build models of extremes.