Dr. Sören Dittmer

Projects

1. Design-KIT: Artificial Intelligence in mechanical component development; TP: Deep Learning for geometry generation of mechanical components (01.10.2020 - 31.03.2022)
2. Magnetic Particle Imaging (since 01.03.2016)

Courses (Selection)complete list

1. Mathematical Methods in Machine Learning (Wintersemester 2023/2024)
2. Mathematical Foundations of AI (Sommersemester 2023)
3. Mathematical Foundations of Deep Learning (Wintersemester 2022/2023)
4. Mathematical Foundations of AI (Wintersemester 2022/2023)
5. Mathematical Foundations of AI (Sommersemester 2022)

Theses (Selection)complete list

1. Contrasting and motivating augmented contrastive learning (Jule Pätzold)
2. Inverse Problems Learning – data specific regularizations using projections (Julius Arkenberg)
3. Unsupervised Denoising von Magnetic-Particle-Imaging-Messungen durch Neuronale Netze (Nikolas Dreverhoff)
4. Differentiable architecture search - Fractional Kernel sizes in convolutional neural networks (Daniel Klosa)

Publications (Selection)complete list

1. S. Dittmer, M. Roberts, J. Gilbey, A. Biguri, .. AIX-COVNET Collaboration, J. Preller, J. H. F. Rudd, J. A. D. Aston, C. Schönlieb.
   Navigating the development challenges in creating complex data systems.
   nature machine intelligence, 5:681-686, Springer Verlag, 2023.

   DOI: 10.1038/s42256-023-00665-x
   online at: https://www.nature.com/articles/s42256-023-00665-x#citeas

2. S. Dittmer, M. Roberts, J. Preller, .. AIX-COVNET Collaboration, J. H. F. Rudd, J. A. D. Aston, C. Schönlieb.
   Reinterpreting survival analysis in the universal approximator age.
   Zur Veröffentlichung eingereicht.
3. C. Arndt, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, J. Nickel.
   Bayesian view on the training of invertible residual networks for solving linear inverse problems.
   Zur Veröffentlichung eingereicht.

   online at: https://www.x-mol.net/paper/article/1682514725633245184

4. T. Shadbahr, M. Roberts, J. Stanczuk, J. Gilbey, P. Teare, S. Dittmer, M. Thorpe, R. V. Torne, E. Sala, P. Lio, M. Patel, .. AIX-COVNET Collaboration, J. H. F. Rudd, T. Mirtti, A. Rannikko, J. A. D. Aston, J. Tang, C. Schönlieb.
   The impact of imputation quality on machine learning classifiers for datasets with missing values.
   Communication medicine, 3, Springer Verlag, 2023.

   DOI: 10.1038/s43856-023-00356-z
   online at: https://www.nature.com/articles/s43856-023-00356-z#citeas

5. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
   Invertible residual networks in the context of regularization theory for linear inverse problems.
   Inverse Problems, 39(12), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online at: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660