Speaker
Description
Experiments at large scale facilities like the European X-ray Free Electron Laser (EuXFEL) generate data at a very large rate, leading experimentalists with data samples potentially including millions of unlabelled images and instrument scientists with online monitoring of data from different sources. The analysis and understanding of such data need to cope with the high data rate, both to improve the operation of the facility and the production of high-quality scientific results. Machine Learning includes many methods that may contribute to the analysis of such large datasets. The EuXFEL has organized a Machine Learning workshop with the goal of brainstorming on solutions to common issues and establishing networks of researchers which could improve the current Machine Learning applications in the facility. This presentation shows a quick summary of the workshop.
