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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. AGENS - Analytical-generative Networks for System Identification (01.04.2020 - 31.03.2023)
  3. HYDAMO - Hybrid data-driven and model-based simulation of complex flow problems in the automotive industry (01.04.2020 - 31.03.2023)
  4. SPAplus: Small data problems in digital pathology and measures accompanying the programme (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. Deep Learning for Inverse Problems (Sommersemester 2024)
  2. Mathematical Foundations of Machine Learning (Sommersemester 2024)
  3. Inverse Problems in Imaging (Wintersemester 2023/2024)
  4. Mathematical Foundations of AI (Sommersemester 2023)
  5. Mathematical Foundations of Machine Learning (Sommersemester 2023)

betreute/begutachtete Dissertationen (Selection)complete list

  1. Equivariant Deep Learning for 3D Topology Optimization (David Erzmann)
  2. 3D Image Analysis and Microstructure Models for Simulation of Materials Properties. (Dascha Dobrovolskij)
  3. Invertible Neural Networks and Normalizing Flows for Image Reconstruction. (Alexander Denker)
  4. Unsupervised Deep Machine Learning Methods to Discriminate Icequakes in Seismological Data from Neumayer Station, Antarctica. (Louisa Kinzel)
  5. On the Interplay between Deep Learning Partial Differential Equations and Inverse Problems (Derick Nganyu Tanyu)

Theses (Selection)complete list

  1. A different approach of the Deep Image Prior on CT-Imaging (Pegah Golchian)
  2. Inversion of the Modulo Radon Transform via direct Fourier Reconstruction Methods (Meira Iske)
  3. Das universelle Approximationsproblem für neuronale Netze und numerische Tests für niedrig-dimensionale inverse Probleme (Malte Lorenzen)
  4. Long-term Forecasting of Energy Consumption Data using Attention-based Neural Networks (Cécile Pot d'or)
  5. Optimal Filter Functions in X-Ray Computed Tomography (Judith Nickel)

Patents

  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. D. Nganyu Tanyu, J. Ning, A. Hauptmann, B. Jin, P. Maaß.
    Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2310.18636

  2. A. Denker, I. Singh, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maaß, S. Arridge.
    Score-Based Generative Models for PET Image Reconstruction.
    Erscheint in Machine Learning for Biomedical Imaging

    online at: https://arxiv.org/abs/2308.14190

  3. F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
    PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
    Inverse Problems, 39(6), 2023.

    online at: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

  4. 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

  5. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
    Invertible residual networks in the context of regularization theory for linear inverse problems.
    Inverse Problems, 39(12), IOPscience, 2023.

    DOI: 10.1088/1361-6420/ad0660
    online at: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660