Professor of Computer Science, RPTU Kaiserslautern-Landau, Germany
- Date & time
- Monday, October 16, 2023 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
Anomaly detection is one of the fundamental topics in machine learning and artificial intelligence. The aim is to find instances deviating from the norm — so-called anomalies. Anomalies can be observed in various scenarios, from attacks on computer or energy networks to critical faults in a chemical factory or rare tumors in cancer imaging data. In my talk, I will first introduce the field of anomaly detection, with an emphasis on deep anomaly detection. Then, I will present recent algorithms and theory for deep anomaly detection, with images as primary data type. I will demonstrate how these methods can be better understood using explainable AI methods. I will show new algorithms for deep anomaly detection on other data types, such as time series, graphs, tabular data, and contaminated data. Finally, I will close my talk with an outlook on exciting future research directions in anomaly detection and beyond.
About the speaker
Marius Kloft is a professor of machine learning at RPTU Kaiserslautern-Landau. His research covers a broad spectrum of machine learning, from mathematical theory and fundamental algorithms to applications in medicine and chemical engineering. He is a recipient of the German National Science Foundation's Emmy-Noether Career Award, and his paper Deep One-Class Classification received the ANDEA Test-of-Time Award.