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Bild Dr. Sören Dittmer

Dr. Sören Dittmer

Research Assistant WG Industrial Mathematics, Research Training Group π3

Room: MZH 2240
Email: sdittmer@math.uni-bremen.de
Phone: (0421) 218-63806


  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)

Theses (Selection)complete list

  1. Differentiable architecture search - Fractional Kernel sizes in convolutional neural networks (Daniel Klosa)

Publications (Selection)complete list

  1. S. Dittmer, T. Kluth, M. Henriksen, P. Maaß.
    Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2007.01593

  2. S. . Mukherjee, S. Dittmer, Z. . Shumaylov, S. Lunz, O. Öktem, C. Schönlieb.
    Learned convex regularizers for inverse problems.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2008.02839

  3. S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
    A Deep Prior Approach to Magnetic Particle Imaging.
    Machine Learning for Medical Image Reconstruction 2020.
    Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Eds.), pp. 113-122, 2020.

    DOI: 10.1007/978-3-030-61598-7_11

  4. S. Dittmer.
    On deep learning applied to inverse problems - A chicken-and-egg problem.
    Dissertationsschrift, Universität Bremen, 2020.
  5. S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
    Regularization by architecture: A deep prior approach for inverse problems.
    Journal of Mathematical Imaging and Vision, :456-470, Springer Verlag, 2020.

    DOI: 10.1007/s10851-019-00923-x
    online at: http://link.springer.com/article/10.1007/s10851-019-00923-x