Michael Petry

E-Mail: michael.petry@tum.de
Phone: 4285
Room: 9377.01.113
Address: Airbus Defence and Space GmbH
Michael Petry
Abteilung Telecom Processing Germany (TSTCG-TL2)
Willy-Messerschmitt-Straße 9
82024 Taufkirchen
## Research Interests * Deep Learning * Information Theory * AI-designed PHY communication * ML-augmented Network orchestration * Finite-Element-Method physical simulations ## Projects * **Machine Learning for Telecommunication Satellites (MaLeTeSa)**: Todo Description. ## Publications
  1. Krstova, A., Hegwein, F., Lera, M. C. D., Ales, F., Petry, M., Ali, R., Mallah, M., Hili, L., Ghiglione, M., & Werner, M. (2023). On-Board Anomaly Detection on a Flight-Ready System. EDHPC 2023 - European Data Handling and Data Processing Conference. [PDF] [BibTeX]
  2. Koch, A., Dax, G., Petry, M., Gomez, H., Raoofy, A., Saroliya, U., Ghiglione, M., Furano, G., Werner, M., Trinitis, C., & Langer, M. (2023). Reference Implementations for Machine Learning Application Benchmark. EDHPC 2023 - European Data Handling and Data Processing Conference. [PDF] [BibTeX]
  3. Koch, A., Petry, M., Ghiglione, M., Raoofy, A., Dax, G., Furano, G., Werner, M., Trinitis, C., & Langer, M. (2023). Machine Learning Application Benchmark. 20th ACM International Conference on Computing Frontiers (CF ’23), May 9–11, 2023, Bologna, Italy. https://doi.org/10.1145/3587135.3592769 [PDF] [BibTeX]
  4. Petry, M., Gest, P., Koch, A., Ghiglione, M., & Werner, M. (2023). Accelerated Deep-Learning inference on FPGAs in the Space Domain. Computing Frontiers 2023. [PDF] [BibTeX]
  5. Petry, M., Koch, A., Werner, M., Hoch, U., Helfers, T., & Wiest, R. (2023). Machine Learning on Telecommunication Satellite. DATA SYSTEMS IN AEROSPACE - 2023 DASIA. [PDF] [BibTeX]
## Other Publications
  1. Dumitru, C. O., Schwarz, G., Dax, G., Vlad, A., Ao, D., & Datcu, M. (2020). Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches. In H. R. Arabnia, K. Daimi, R. Stahlbock, C. Soviany, L. Heilig, & K. Brussau (Eds.), Principles of Data Science (pp. 207–231). Springer Nature Switzerland AG. https://elib.dlr.de/138139/ [Online] [BibTeX]
  2. Dumitru, C. O., Schwarz, G., Ao, D., Dax, G., Karmakar, C., & Datcu, M. (2020). Selection of Reliable Machine Learning Algorithms for Geophysical Applications. EGU 2020. https://elib.dlr.de/138129/ [Online] [BibTeX]

© 2020 M. Werner