Nadja Klein

Nadja Klein is Professor at the Scientific Computing Center at the Karlsruhe Institute of Technology (KIT), where she leads the research group Methods for Big Data. She is also an Emmy Noether Research Group Leader and a member of AcademiaNet as well as almuna of Die Junge Akademie, among other scientific networks. Her contributions to the field have been recognized with the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award.

After completing her doctoral studies in Mathematics, she was a Feodor-Lynen fellow of the Alexander von Humboldt Foundation at the University of Melbourne. She subsequently held the position of Professor of Statistics and Data Science at Humboldt-Universität zu Berlin before joining KIT.

Her research focuses on Bayesian learning, a powerful statistical approach that enables the incorporation of prior knowledge, the quantification of uncertainties, and the interpretation of machine learning models often perceived as “black boxes.” By combining the precision and reliability of Bayesian statistics with the flexibility of machine and deep learning, her work aims to bring together the best of both worlds. More information on the group’s activities is available at: https://kleinlab-statml.github.io/