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Anomaly detection is a challenging problem which can greatly benefit from the use of machine learning (ML) methods: unsupervised as well as semi-supervised. ML algorithms must be able to process complex, massive data sets and search for anomalies under extreme conditions (very low signal-to-noise ratio, real-time data, etc). The range of applications for anomaly detection methods is vast, and advances made in one scientific field can frequently be transferred to other disciplines, to the benefit of both parties involved. This event brings together scientists from a range of scientific fields including computer science, statistics, particle physics and astrophysics, as well as cross-cutting areas such as the development of anomaly detection algorithms, medical image analysis, accelerator physics, and others. The goal of this workshop is to start a dialogue between different experts, fostering a collaborative environment where experiences, knowledge, and methodologies can be shared.

machine learning, anomaly detection, particle physics, astronomy, medical imaging, industry,

CIRM – Centre International de Rencontres Mathématiques

A clear emphasis will be put on theoretical and numerical methods oriented towards applications in engineering and the sciences. Experts from applied mathematics will report on potential industrial applications, prototypes, and – where possible – existing use cases.

Komplexe Netzwerke,    Numerische Analysis

Society for Industrial and Applied Mathematics (SIAM)

CIRM – Centre International de Rencontres Mathématiques

Geometrie und Topologie,    Mustererkennung und Bildverarbeitung, Maschinelles Sehen

CIRM – Centre International de Rencontres Mathématiques

CIRM – Centre International de Rencontres Mathématiques

Geometrie und Topologie,    Allgemeine und fachübergreifende Veranstaltugen in Informatik

CIRM – Centre International de Rencontres Mathématiques