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ZeTeM > About ZeTeM > Staff > Prof. Dr. Dr. h.c. Peter Maaß

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Bild Prof. Dr. Dr. h.c. Peter Maaß

Prof. Dr. Dr. h.c. Peter Maaß

Leader of WG Industrial Mathematics
Head of Center for Industrial Mathematics

Room: MZH 2250
Email: pmaass@math.uni-bremen.de
Phone: (0421) 218-63801

Research Areas

Leader of Projects

  1. AGENS - Analytical-generative Networks for System Identification (01.04.2020 - 31.03.2023)
  2. HYDAMO - Hybrid data-driven and model-based simulation of complex flow problems in the automotive industry (01.04.2020 - 31.03.2023)
  3. SPAplus: Small data problems in digital pathology and measures accompanying the programme (01.04.2020 - 31.03.2023)
  4. DIAMANT - Digital Image Analysis and Imaging Mass Spectrometry to Differentiate Non-small Cell Lung Cancer (01.01.2020 - 31.12.2022)
  5. EU-ROMSOC: Project ''Data Driven Model Adaptations of Coil Sensitivities in MR Systems'' (01.11.2017 - 30.04.2021)
  6. BMBF-MPI²: Model-based parameter identification in magnetic particle imaging (01.12.2016 - 30.11.2019)
  7. DFG-Graduiertenkolleg: π³ Parameter Identification – Analysis, Algorithms, Applications (01.10.2016 - 31.03.2021)
  8. Neural networks in MALDI imaging (since 01.10.2016)
  9. SFB 1232: Farbige Zustände - TP P02: Heuristische, statistische und analytische Versuchsplanung (01.07.2016 - 30.06.2020)
  10. Magnetic Particle Imaging (since 01.03.2016)

Courses (Selection)complete list

  1. Funktionalanalysis (Sommersemester 2020)
  2. Oberseminar: Deep Learning, Inverse Probleme und Datenanalyse (Sommersemester 2020)
  3. Mathematische Grundlagen der Datenanalyse und Bildverarbeitung (Wintersemester 2019/2020)
  4. Oberseminar Inverse Probleme (Wintersemester 2019/2020)
  5. Machine Learning (Sommersemester 2018)

betreute/begutachtete Dissertationen (Selection)complete list

  1. Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging (Daniel Otero Baguer)
  2. Recurrence Quantification Compared to Fourier Analysis for Ultrasonic Non-Destructive Testing of Fibre Reinforced Polymers” (Carsten Brandt)
  3. Double Backpropagation with Applications to Robustness and Saliency Map Interpretability (Christian Etmann)
  4. Principles of Neural Network Architecture Design:Invertibility and Domain-Knowledge (Jens Behrmann)
  5. Quantum Frames and Uncertainty Principles arising from Symplectomorphisms (Daniel Lantzberg)

Theses (Selection)complete list

  1. Invertible U-Nets for Memory-Efficient Backpropagation (Nick Heilenkötter)
  2. Application of Neural Networks For Solving Inverse Problems (Alexander Denker)
  3. Ein universelles Approximationstheorem für einschichtige neuronale Netze (Meira Iske)
  4. Deep Learning for Picking Seismic Arrival Times at Neumayer Station, Antarctica (Louisa Granzow)
  5. Joint-Motion und Bildrekonstruktion für Magnetic Particle Imaging in 2D und 3D (Dennis Zvegincev)

Patents

  2. P. Maaß, J. H. Kobarg, F. Alexandrov, P. Vandergheynst, M. Goldabaee.
    Verfahren zum rechnergestützten Verarbeiten von räumlich aufgelösten Hyperspektraldaten, insbesondere von Massenspektrometriedaten.
    German Patent and Trade Mark Office DE102013207402A1,
    Number of registration: 1020132074, Date of registration: 24.04.2013.
    Published in patent bulletin no.: on 30.10.2014.
  4. P. Maaß, J. Oetjen, L. Hauberg-Lotte, F. Alexandrov, D. Trede.
    Verfahren zur rechnergestützten Analyse eines oder mehrerer Gewebeschnitte des menschlichen oder tierischen Körpers.
    German Patent and Trade Mark Office DE102014224916A1,
    Number of registration: 1020142249, Date of registration: 04.12.2014.
    Published in patent bulletin no.: on 06.09.2016.
    US Patent & Trademark Office, US 20160163523 A1,
    Anmeldenummer: 14/959967 , Anmeldedatum: 04.12.2014.
    Veröffentlicht am 09.06.2016
    Intellectual Property Office, GB 2535586,
    Anmeldenummer: GB1521058.6, Anmeldedatum: 30.11.2015.
    Veröffentlicht am 24.08.2016
    Institut national de la propriété industrielle, FR 3029671 A1,
    Anmeldenummer: FR1561774, Anmeldedatum: 03.12.2015.
    Veröffentlicht am 10.06.2016
  6. D. Trede, P. Maaß, H. Preckel.
    Method for analysing the effect of a test substance on biological and/or biochemical samples.
    US Patent and Trademark Office US2011/0098198 A1,
    Number of registration: 2011009819, Date of registration: 29.04.2009.
    Published in patent bulletin no.: PCT/EP09/55187 on 28.04.2011.
  8. D. Trede, P. Maaß, F. Alexandrov.
    Verfahren und Vorrichtung zur rechnergestützten Verarbeitung eines digitalisierten Bildes sowie maschinenlesbarer Datenträger.
    German Patent and Trade Mark Office 10 2011 003 242.8,
    Number of registration: 102011003, Date of registration: 27.01.2011.
    Published in patent bulletin no.: on 02.08.2012.
  10. D. Trede, P. Maaß, H. Preckel.
    Verfahren zur Analyse der Wirkung einer Testsubstanz auf biologische und/oder biochemische Proben.
    European Patent Office EP2128815,
    Number of registration: 8155784, Date of registration: 07.05.2008.
    Published in patent bulletin no.: 2009/49 on 02.12.2009.
  12. P. Maaß, A. K. Louis.
    Verfahren und Vorrichtung zur dreidimensionalen Computertomographie.
    German Patent and Trade Mark Office DE19623271A1,
    Number of registration: 19623271, Date of registration: 31.05.1996.
    Published in patent bulletin no.: 1997/49 on 04.12.1997.
  14. P. Maaß.
    Verfahren zur Segmentierung von Zeichen.
    German Patent and Trade Mark Office DE19533585C1,
    Number of registration: 19533585, Date of registration: 01.09.1995.
    Published in patent bulletin no.: 1997/02 on 09.01.1997.

Publications (Selection)complete list

  1. T. Boskamp, D. Lachmund, R. Casadonte, L. Hauberg-Lotte, J. H. Kobarg, J. Kriegsmann, P. Maaß.
    Using the chemical noise background in MALDI mass spectrometry imaging for mass alignment and calibration.
    Analytical Chemistry, 92(1):1301-1308, 2020.

    DOI: 10.1021/acs.analchem.9b04473
    online at: https://doi.org/10.1021/acs.analchem.9b04473

  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. 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

  4. M. Beckmann, P. Maaß, J. Nickel.
    Error analysis for filtered back projection reconstructions in Besov spaces.
    Erscheint in Inverse Problems
  5. T. H. Nguyen, D. Nho Hào, P. Maaß, L. Colombi Ciacchi.
    Mathematical aspects of catalyst positioning in lithium/air batteries.
    Inverse Problems, 36(4), 2020.

    DOI: 10.1088/1361-6420/ab47e6