Prof. Dr. Dr. h.c. Peter Maaß

Research Areas

• Inverse problems
• Machine learning
• signal and image analysis in life sciences
• computational engineering
• system theory and parameteridentifikation

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 2025)
2. Challenges in Inverse Problems (Wintersemester 2024/2025)
3. Mathematical Methods in Machine Learning (Wintersemester 2024/2025)
4. Advanced Topics in Image Processing – The Beauty of Variational Calculus (Wintersemester 2024/2025)
5. Modelling Project (Part 2) (Wintersemester 2024/2025)

betreute/begutachtete Dissertationen (Selection)complete list

1. Regularization Theory for Special Neural Network Architectures Applied on Inverse Problems (Clemens Arndt)
2. Equivariant Deep Learning for 3D Topology Optimization (David Erzmann)
3. 3D Image Analysis and Microstructure Models for Simulation of Materials Properties. (Dascha Dobrovolskij)
4. Invertible Neural Networks and Normalizing Flows for Image Reconstruction. (Alexander Denker)
5. Unsupervised Deep Machine Learning Methods to Discriminate Icequakes in Seismological Data from Neumayer Station, Antarctica. (Louisa Kinzel)

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

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. D. Nganyu Tanyu, I. Michel, A. Rademacher, J. Kuhnert, P. Maaß.
   Parameter Identification by Deep Learning of a Material Model for Granular Media.
   GEM -- International Journal on Geomathematics, 15(13), 2024.

   DOI: 10.1007/s13137-024-00253-0
   online at: https://arxiv.org/abs/2307.04166

2. J. G. Maaß, R. Herdt, L. Kinzel, M. Walther, H. Fröhlich, T. Schubert, C. Schaaf, P. Maaß.
   Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models.
   The Journal of the Acoustical Society of America, 156(4):2448-2466, 2024.

   DOI: 10.1121/10.0030473

3. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   How GAN Generators can Invert Networks in Real-Time.
   The 15th Asian Conference on Machine Learning - ACML 2023, 11.11.-14.11.2023.
   PMLR, 222:422-437, 2024.

   online at: https://proceedings.mlr.press/v222/herdt24a.html

4. P. Jansen, J. Le Clerc Arrastia, D. Otero Baguer, M. Schmidt, J. Landsberg, J. Wenzel, M. Emberger, D. Schadendorf, E. Hadaschik, P. Maaß, K. G. Griewank.
   Deep learning based histological classification of adnex tumors.
   European Journal of Cancer, 113431 196, 2024.

   DOI: 10.1016/j.ejca.2023.113431

5. R. Herdt, P. Maaß.
   Visualize and Paint GAN Activations.
   2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 22.09.-25.09.2024, London, UK.
   IEEE, 2024.

   DOI: 10.1109/MLSP58920.2024.10734757