Christopher Bülte

PhD candidate, LMU Munich
I am a PhD candidate at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich under supervision of Prof. Gitta Kutyniok.
My research revolves around applying methods of machine learning and uncertainty quantification to physical problems. I have a strong interest in utilizing neural networks for probabilistic predictions and its underlying theory. Furthermore, I am generally interested in developing novel uncertainty quantification methods for machine learning. Application of the methods include dynamical systems, meteorology or energy systems.
My research interests:
- Physical law learning
- Uncertainty quantification
- Probabilistic predictions
Before the start of my PhD I obtained a Bachelor’s degree in Industrial Engineering and a Masters’s degree in Mathematics from Karlsruhe Institute of Technology.
If you have any questions regarding my research or want to collaborate feel free to contact me anytime.
news
Mar 28, 2025 | Very happy to announce that our paper Probabilistic neural operators for functional uncertainty quantification was accepted in Transactions on Machine Learning Research ![]() |
---|---|
Mar 26, 2025 | Our paper Uncertainty quantification for data-driven weather models was accepted at the AMS journal Artificial Intelligence for the Earth Systems ![]() |
Oct 14, 2024 | Our paper Probabilistic predictions with Fourier neural operators was accepted at the NeurIPS 2024 workshop on Bayesian Decision-making and Uncertainty ![]() |
May 01, 2024 | I am happy to announce that I am now an Associated PhD student at the Konrad Zuse School of Excellence in Reliable AI (relAI) ![]() |
Mar 01, 2024 | I started my PhD at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich ![]() |