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Bild  Maximilian Schmidt

Maximilian Schmidt

Research Assistant WG Industrial Mathematics, Research Training Group π3

Room: MZH 2450
Email: maximilian.schmidt@uni-bremen.de
Phone: (0421) 218-63826
ORCID iD:  0000-0001-8710-1389

CV

Theses

Master Thesis (12/2018)

Investigation of Neural Capsule Networks in the Context of Image Classification and Semantic Segmentation
German title: Untersuchung Neuronaler Kapsel-Netze im Kontext der Bildklassifikation und Semantischen Segmentierung
Supervisors: Prof. Dr. Dr. h.c. Peter Maaß, Dr. Jens Behrmann
University of Bremen

Bachelor Thesis (09/2016)

Classification on MALDI Data with Non-Negative Matrix Factorization and Supervised Learning
German title: Klassifikation auf MALDI-Daten mit nichtnegativer Matrixfaktorisierung und überwachtem Lernen
Supervisors: Prof. Dr. Dr. h.c. Peter Maaß, Delf Lachmund
University of Bremen

Work & Research

PhD Student RTG π3 (since 09/2019)

R3-10: Learning Tikhonov Functionals for Inverse Problems
Center for Industrial Mathematics
University of Bremen

Research Assistant (since 01/2019)

Deep Learning & Inverse Problems
Center for Industrial Mathematics
University of Bremen

Internship (08/2017 - 10/2017)

Size Reduction of Artificial Neural Networks
Bosch, Research Department
Hildesheim, Germany

Student Assistant (08/2014 - 12/2018)

Machine Learning & MALDI Imaging
Center for Industrial Mathematics
University of Bremen

Research Areas

Projects

  1. Neural networks in MALDI imaging (since 01.10.2016)

Courses (Selection)complete list

  1. Computerpraktikum (Wintersemester 2019/2020)
  1. Übungen Mathematical Foundations of Machine Learning (Sommersemester 2019)

Theses (Selection)complete list

  1. Out of Distribution Detection for Purity Assessment of Pellets using Neural Networks (Jannik Wildner)

Publications (Selection)complete list

  1. D. Otero Baguer, J. Leuschner, M. Schmidt.
    Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
    Inverse Problems, 36(9), IOPscience, 2020.

    DOI: https://doi.org/10.1088/1361-6420/aba415

  2. A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
    Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
    ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Vienna, Austria.

    online at: https://invertibleworkshop.github.io/accepted_papers/index.html

  3. M. Schmidt.
    Around the clock - capsule networks and image transformations.
    PAMM.
    To appear in Proceedings in Applied Mathematics and Mechanics.
  4. J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
    The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.
    Zur Veröffentlichung eingereicht.

    online at: arXiv:1910.01113

  5. C. Etmann, M. Schmidt, J. Behrmann, T. Boskamp, L. Hauberg-Lotte, A. Peter, R. Casadonte, J. Kriegsmann, P. Maaß.
    Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/1912.05459