About me

Currently, I am a postdoctoral research at the University of Lausanne, working with Prof. Tom Beucler on AI for climate science as part of AI4PEX. I focus on using machine learning and generative modelling for uncertainty quantification for atmospheric parameterizations.

I received my PhD in Physics in 2026 from University of Bremen, where I worked with Prof. Veronika Eyring. My research focused on developing a physically-consistent, ML-based parameterization for atmospheric radiative transfer for the weather and climate model ICON. Additionally, I used high-resolution simulations to improve the radiation paramterization by representing subgrid-scale cloud effects.

During my PhD, I had the chance to be a visiting research scholar at Columbia University and New York University, where I worked with Prof. Pierre Gentine, Prof. Robert Pincus and Prof. Sara Shamekh.

I received my Master of Science in Physics with a focus on particle physics at RWTH Aachen University in 2021. For my master thesis, I developed a method to unfold the radio signal of air showers induced by ultra high energy cosmic rays. The method is based on autoencoders and normalizing flows. The radio signals are related measurements at the Pierre Auger Observatory.

I received my Bachelor of Science in Physics at RWTH Aachen University in 2019. For my thesis, I developed a method to refine simulated signal traces using CycleGANs.

During my bachelor and master studies, I worked as a research assistant in the group of Prof. Martin Erdmann, where I also worked on my theses. I had the opportunity to support the VISPA team and learn about high performance computing. Additionally, I worked on developing JupyterLab extensions and was responsible for a weather station and its website. I was always interested in Computer Science, which is why I enrolled in some additional classes to learn more about data structures and algorithms.

Outside of research, I like running, yoga, or being creative (sewing, crocheting, and other crafts).