AISSAI Anomaly Detection Workshop

Session

Invited

4 mars 2024, 09:55

Clermont-Ferrand

2. A Statistician's View on Model-Independent Searches of New Physics at the Large Hadron Collider

Mikael Kuusela (Carnegie Mellon University)

04/03/2024 09:55

A central goal in experimental high energy physics is to detect new signals that appear as deviations from known Standard Model physics in high-dimensional particle physics data. To do this, one seeks to determine whether there is a statistically significant difference between the distribution of Standard Model background samples and the distribution of the experimental observations, which are...

6. Mysterious Lights: anomaly detection in astronomy

Konstantin Malanchev (University of Illinois at Urbana-Champaign)

04/03/2024 14:00

The discovery of unusual objects drives all scientific fields, and astronomy is no exception, given its diverse range of astrophysical phenomena. In the era of large sky surveys and machine learning, researchers designs automated pipelines to sift through data and identify objects that could enhance our understanding of the Universe.

In this talk, I review the challenges and solutions the...

22. Blind Anomaly Detection in Industrial Time-series

Dr Mazen Alamir (CNRS, Université de Grenoble Alpes, Grenoble-inp)

05/03/2024 09:30

In this talk, the specificities of anomaly detection in industrial time-series is investigated and the key related and often quite hidden concepts are introduced through illustrative examples. More importantly, the challenges associated to the characterization of normality of non cyclic state/context dependent time-series is underlined and the role of so-called dynamic invariants in addressing...

34. Anomaly detection in particle physics

Dr Shikma Bressler (Weizmann Institute of Science)

06/03/2024 14:00

Anomaly detection is mostly considered in searches for either outlier events or accumulation of events disagreeing with a priorly known distribution. The emergence of advanced machine learning (ML) techniques opens many new opportunities of detecting anomalies also in collider experiments. I will review traditional as well as state of the art ML-based anomaly detection concepts and algorithms...

39. A review of unsupervised anomaly detection models for neuroimaging applications

Dr Carole Lartizien (CREATIS laboratory)

07/03/2024 09:30

Statistical machine learning models have becoming state-of-the-art methods in almost all medical imaging applications, including the segmentation of organs or structures of interest and the detections of pathological patterns. Among data-driven methods, fully supervised models remain the most common and performing ones. However, gathering numerous expert-annotated data to train such models is...