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Bild Dr. Maximilian Schmidt

Dr. Maximilian Schmidt

Research Assistant WG Industrial Mathematics, Research Training Group π3

Room: MZH 2050
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. Active learning for semantic segmentation in digital pathology (Jannik Wildner)
  2. Out of Distribution Detection for Purity Assessment of Pellets using Neural Networks (Jannik Wildner)

Publications (Selection)complete list

  1. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
    Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2302.02181

  2. T. Lütjen, F. Schönfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
    Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
    Zur Veröffentlichung eingereicht.

    online at: https://aps.arxiv.org/abs/2307.10474

  3. C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
    Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
    Applied Mathematics for Modern Challenges, 1(2), 2023.

    DOI: 10.3934/ammc.2023007

  4. J. Behrmann, M. Schmidt, J. Wildner, P. Maaß, S. Schmale.
    Purity Assessment of Pellets Using Deep Learning.
    German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Eds.), Mathematics in Industry, pp. 29-34, Springer Verlag, 2022.

    DOI: 10.1007/978-3-030-81455-7_6

  5. M. Schmidt.
    Hybrid Deep Learning: How Combining Data-Driven and Model-Based Approaches Solves Inverse Problems in Computed Tomography and Beyond.
    Dissertationsschrift, Universität Bremen, 2022.

    DOI: 10.26092/elib/1941