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. Neural networks in MALDI imaging (since 01.10.2016)
10. DFG-Graduiertenkolleg: π³ Parameter Identification – Analysis, Algorithms, Applications (01.10.2016 - 31.03.2021)

Courses (Selection)complete list

1. Mathematische Grundlagen der Datenanalyse und Bildverarbeitung (Wintersemester 2021/2022)
2. Oberseminar Deep Learning (Sommersemester 2021)
3. Mathematische Grundlagen des maschinellen Lernens (Sommersemester 2021)
4. Nichtlineare inverse Probleme: Analysis, Anwendungen und Algorithmen (Wintersemester 2020/2021)
5. Oberseminar: Deep Learning, Inverse Probleme und Datenanalyse (Wintersemester 2020/2021)

betreute/begutachtete Dissertationen (Selection)complete list

1. Regularization of ill-posed inverse problems with tolerances and sparsity in the parameter space (Georgia Sfakianaki)
2. On deep learning applied to inverse problems - A chicken-and-egg problem (Sören Dittmer)
3. Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging (Daniel Otero Baguer)
4. Recurrence Quantification Compared to Fourier Analysis for Ultrasonic Non-Destructive Testing of Fibre Reinforced Polymers” (Carsten Brandt)
5. Double Backpropagation with Applications to Robustness and Saliency Map Interpretability (Christian Etmann)

Theses (Selection)complete list

1. Long-term Forecasting of Energy Consumption Data using Attention-based Neural Networks (Cécile Pot d'or)
2. Optimal Filter Functions in X-Ray Computed Tomography (Judith Nickel)
3. Statistical Inversion of the Radon Transform with Normalizing Flows (Jakob Lehmann)
4. Residuale Transformationen und Divergenzen in Bayes’schen Neuronalen Netzen (Niklas Koenen)
5. Identifizierung des basalen Reibungsparameters eines Eisschildmodells in Polarregionen (Lea-Sophie Höyns)

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. 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.
   International Journal on Magnetic Particle Imaging, 7(1), 2021.

   online at: https://journal.iwmpi.org/index.php/iwmpi/article/view/148

2. 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

3. 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

4. A. Denker, M. Schmidt, J. Leuschner, P. Maaß.
   Conditional Invertible Neural Networks for Medical Imaging .
   MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 pp., 2021.

   DOI: 10.3390/jimaging7110243

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