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The Data Science Chair at the University of Würzburg celebrates its data storage infrastructure finally reaching the milestone of 1 PetaByte raw storage capacity.

With further expansion on the horizon for later this year, our Ceph based cluster is getting ready for our future scientific endeavours that are driven by large-scale datasets from various domains, like Natural Language Processing, Environmental Research, Recommender Systems and others.

More information about our cluster can be found at the following link:

In this paper, we investigate if Deep Metric Learning models are prone to background bias and test a method to alleviate such bias.

In our paper, we investigate the performance and interpretability of machine learning approaches when detecting occupational fraud in company data, finding models that give both strong performance and comprehensible decisions.

In our paper we use the Method of Lines together with Neural ODEs to learn spatio-temporal data arising from dynamical systems. We show that the method performs consistently well on a wide range of applications.