PhD candidate at the German Aerospace Center (DLR), Institute of Data Science Jena
Wiebkes background is in mathematics and quality assessment of neural networks. She is working on causal inference methods for non-gaussian and non-stationary data distributions within the HGF project CausalFlood. Wiebke studied mathematics at Humboldt University Berlin from 2013 and obtained her Master’s degree in 2019. Her thesis was on the topic of bilevel optimization for parameter learning for inverse problems with the wave equation. In 2022, she joined the Causal Inference group at the German Aerospace Center’s Institute of Data Science in Jena as a PhD candidate.