Christian Schroth, since April 2020 Research Associate with the Signal Processing Group, wins the Ernst-Blickle-Studienpreis 2020 for his Master’s Thesis on Robust Bayesian Cluster Analysis, which was supervised by Michael Muma. In his work, he dealt with robust cluster analysis and developed highly innovative algorithms of statistical learning to identify clusters within a given data set. To this end, the data are modelled as multivariate random variables, which form point clouds in a vector space, the so-called feature space. Those point clouds are to be explained by a suitable underlying statistical model. Both, the number of clusters, as well as the cluster memberships of the individual data points are unknown. A variety of established approaches to this problem exist, but they often lose their statistical guarantees or break down entirely in the presence of outliers or non-symmetrical spread around the cluster centers.