Since August 2024, I lead the research group Methods for Big Data at the Scientific Computing Center, KIT. I am also an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. I completed my doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards I was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. 

My research specializes in Bayesian learning, a powerful approach that allows to incorporate prior knowledge, quantify uncertainties, and bring insight to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, our methods aim to leverage the best of both worlds. More information about our activities can be found at https://kleinlab-statml.github.io/