Christopher Bülte

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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 about my research or want to collaborate feel free to contact me anytime.

news

Oct 14, 2024 Our paper Probabilistic predictions with Fourier neural operators was accepted at the NeurIPS 2024 workshop on Bayesian Decision-making and Uncertainty :tada:
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) :tada:
Mar 01, 2024 I started my PhD at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich :tada:

selected publications

  1. arXiv
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    Estimation of Spatio-Temporal Extremes via Generative Neural Networks
    Christopher Bülte, Lisa Leimenstoll, and Melanie Schienle
    arXiv preprint arXiv:2407.08668, 2024
  2. Energy & AI
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    Multivariate Time Series Imputation for Energy Data Using Neural Networks
    Christopher Bülte, Max Kleinebrahm, Hasan Ümitcan Yilmaz, and Juan Gómez-Romero
    Energy and AI, 2023
  3. arXiv
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    Uncertainty Quantification for Data-Driven Weather Models
    Christopher Bülte, Nina Horat, Julian Quinting, and Sebastian Lerch
    arXiv preprint arXiv:2403.13458, 2024