About me

In 2024, I completed my PhD at Technische Universität Berlin as part of the MATH+ project AA5-6 under the supervision of Gabriele Steidl (TU Berlin) and Andrea Walther (HU Berlin). For my dissertation I received the MATH+ Dissertation Award 2024. I received my Bachelors´s and Master´s degrees in Mathematics from TU Berlin in 2019 and 2021. My research interests include mathematical image processing, inverse problems and machine learning.

Publications

Stability of Data-Dependent Ridge-Regularization for Inverse Problems.
S. Neumayer and F. Altekrüger (2025).
Inverse Problems, vol. 41, no. 6.
[doi], [arxiv], [code]

Conditional generative models for contrast-enhanced synthesis of T1W and T1 maps in brain MRI.
M. Piening, F. Altekrüger, G. Steidl, E. Hattingen, E. Steidl (2025).
International Symposium on Biomedical Imaging.
[doi], [arxiv], [code]

Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction.
M. Piening, F. Altekrüger, J. Hertrich, P. Hagemann, A. Walther and G. Steidl (2024).
GAMM-Mitteilungen, vol. 47, e202470002.
[doi], [arxiv], [code]

Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel.
P. Hagemann, J. Hertrich, F. Altekrüger, R. Beinert, J. Chemseddine and G. Steidl (2024).
International Conference on Learning Representations 2024.
[openreview], [arxiv], [code], [pitch]

Generative Sliced MMD flows with Riesz kernels.
J. Hertrich, C. Wald, F. Altekrüger and P. Hagemann (2024).
International Conference on Learning Representations 2024.
[openreview], [arxiv], [code], [pitch]

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels.
F. Altekrüger, J. Hertrich and G. Steidl (2023).
International Conference on Machine Learning 2023.
Proceedings of Machine Learning Research, vol. 202, pp. 664-690.
[www], [arxiv], [code], [pitch]

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems.
F. Altekrüger, P. Hagemann and G. Steidl (2023).
Transactions on Machine Learning Research (TMLR).
[openreview]

Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling.
A. Kofler, F. Altekrüger, F. A. Ba, C. Kolbitsch, E. Papoutsellis, D. Schote, C. Sirotenko, F. Zimmermann and K. Papafitsoros (2023).
SIAM Journal on Imaging Sciences, vol. 16(4), pp. 2202-2246.
[doi], [arxiv], [code]

PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
F. Altekrüger, A. Denker, P. Hagemann, J. Hertrich, P. Maass and G. Steidl (2023).
Inverse Problems, vol. 39, no. 6.
[doi], [arxiv], [code]

WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution.
F. Altekrüger and J. Hertrich (2023).
SIAM Journal on Imaging Sciences, vol. 16(3), pp. 1033-1067.
[doi], [arxiv], [code]

Theses

Solving Bayesian Inverse Problems using Neural Networks.
F. Altekrüger (2024).
PhD Thesis, TU Berlin.
[doi]