Wiebke Günther
PhD candidate at the Technical University Berlin

Wiebke Günther has a background in mathematics and works on the quality assessment of neural networks. She is currently focusing on causal inference methods for non-Gaussian and non-stationary data distributions as part of the HGF project CausalFlood. She studied mathematics at Humboldt University Berlin from 2013 to 2019 and completed her Master’s thesis on bilevel optimization for parameter learning in inverse problems involving the wave equation. In 2022, she joined the Causal Inference group at the German Aerospace Center (DLR), Institute of Data Science in Jena as a PhD candidate. Since January 2025, she has also been a PhD Candidate at Technische Universität Berlin.

Mail: wiebke.guenther@tu-berlin.de
Publications: Google Scholar
Official Website: https://guenwi.github.io/