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Bild Prof. Dr. Dr. h.c. Peter Maaß

Prof. Dr. Dr. h.c. Peter Maaß

Leader of WG Industrial Mathematics

Room: MZH 2250
Email: pmaass@math.uni-bremen.de
Phone: (0421) 218-63801
ORCID iD:  0000-0003-1448-8345

Research Areas

Leader of 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. SPAplus: Small data problems in digital pathology and measures accompanying the programme (01.04.2020 - 31.03.2023)
  3. AGENS - Analytical-generative Networks for System Identification (01.04.2020 - 31.03.2023)
  4. HYDAMO - Hybrid data-driven and model-based simulation of complex flow problems in the automotive industry (01.04.2020 - 31.03.2023)
  5. DIAMANT - Digital Image Analysis and Imaging Mass Spectrometry to Differentiate Non-small Cell Lung Cancer (01.01.2020 - 31.12.2022)
  6. Studie zur Qualitätsbewertung, Standardisierung und Reproduzierbarkeit von Daten der bildgebenden MALDI-Massenspektrometrie – MALDISTAR (01.07.2019 - 30.06.2022)
  7. EU-ROMSOC: Project ''Data Driven Model Adaptations of Coil Sensitivities in MR Systems'' (01.11.2017 - 30.04.2021)
  8. BMBF-MPI²: Model-based parameter identification in magnetic particle imaging (01.12.2016 - 30.11.2019)
  9. DFG-Graduiertenkolleg: π³ Parameter Identification – Analysis, Algorithms, Applications (01.10.2016 - 31.03.2021)
  10. Neural networks in MALDI imaging (since 01.10.2016)

Courses (Selection)complete list

  1. Mathematische Grundlagen des maschinellen Lernens (Sommersemester 2021)
  2. Nicht-linearen inversen Probleme: Analysis, Anwendungen und Algorithmen (Wintersemester 2020/2021)
  3. Oberseminar: Deep Learning, Inverse Probleme und Datenanalyse (Wintersemester 2020/2021)
  4. Funktionalanalysis (Sommersemester 2020)
  5. Oberseminar: Deep Learning, Inverse Probleme und Datenanalyse (Sommersemester 2020)

betreute/begutachtete Dissertationen (Selection)complete list

  1. On deep learning applied to inverse problems - A chicken-and-egg problem (Sören Dittmer)
  2. Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging (Daniel Otero Baguer)
  3. Recurrence Quantification Compared to Fourier Analysis for Ultrasonic Non-Destructive Testing of Fibre Reinforced Polymers” (Carsten Brandt)
  4. Double Backpropagation with Applications to Robustness and Saliency Map Interpretability (Christian Etmann)
  5. Principles of Neural Network Architecture Design:Invertibility and Domain-Knowledge (Jens Behrmann)

Theses (Selection)complete list

  1. Long-Term Time Series Forecasting and Uncertainty Estimation with Bayesian Neural Networks (David Erzmann)
  2. Using Neural Networks to Denoise CT Images (Rudolf Herdt)
  3. Out of Distribution Detection for Purity Assessment of Pellets using Neural Networks (Jannik Wildner)
  4. Differentiable architecture search - Fractional Kernel sizes in convolutional neural networks (Daniel Klosa)
  5. Invertible U-Nets for Memory-Efficient Backpropagation (Nick Heilenkötter)


  1. 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.
  2. 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
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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. J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
    LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
    Scientific Data, 8(109), 2021.

    DOI: 10.1038/s41597-021-00893-z

  2. J. Le Clerc Arrastia, N. Heilenkötter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner , J. Schaller , P. Maaß.
    Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma.
    MDPI Journal of Imaging, 71 7(4), Meisenbach Verlag, Bamberg, 2021.

    DOI: 10.3390/jimaging7040071

  3. S. Dittmer, C. Schönlieb, P. Maaß.
    Ground Truth Free Denoising by Optimal Transport.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2007.01575

  4. 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

  5. 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