Real Time Supervised Anomaly Detection
Anomalies are patterns in data not conforming to expected behavior. Discovery of such patterns leads to actionable insights as anomalies often correspond to undesired states e.g. reduced quality or some failure. While automatic finding of anomalies is a challenge in itself, it is usually followed up by another hard task to understand the causes of their occurrence, in order to prevent them from happening again. The possibility to explain the decision of the anomaly detection system not only helps to establish trust but also to identify the root causes. In this talk I investigate this connection and challenges accompanying Deep Learning approaches.
Dr. Sergei Bobrovskyi is a Data Scientist within the Analytics Accelerator team of the Airbus Digital Transformation Office. His work focuses on applications of AI for anomaly detection in time series, spanning various use-cases across Airbus. Prior to Airbus he worked on automated fraud detection for one of the largest e-commerce companies in Germany. Before that he was engaged in various research related positions in the space industry.
Sergei holds a PhD in theoretical physics as well as a physics Diploma from the University of Hamburg. Besides physics he also studied philosophy with an emphasis on the philosophy of mind.