Andreas Koch

E-Mail: andreas.c.koch@tum.de
Phone:
Room: 9377.01.109
Address: Airbus Defence and Space GmbH
Andreas Koch
Abteilung Telecom Processing Germany (TSTCG-TL2)
Willy-Messerschmitt-Straße 9
82024 Taufkirchen

Research Interests

  • Deep Learning
  • Streaming Anomaly Detection on Satellite Telemetry
  • Satellite Payload Data Processing
  • AI hardware acceleration on FPGA

Projects

  • Machine Learning Application Benchmark (MLAB): A European Space Agency (ESA)-funded activity by Airbus Defence and Space GmbH, Technical University of Munich and Orbital Oracle Technologies with the aim of creating a machine learning benchmark suitable for the space domain. It consists of multiple applications spanning anomaly detection, radio modulation classification, object detection for optical and multispectral images in order to detect ships, airplanes and wildfires, and land use / land cover classification. The benchmark combines aspects from both hardware and machine learning by offering different submission modes, allowing the comparison of either the machine learning model or the hardware platform.

  • Anomaly Detection Anomaly Prognosis (ADAP): This was a project led by Airbus Defence GmbH in cooperation with Frauenhofer Institute for Integrated Circuits (IIS) in order to develop a machine learning (ML) based anomaly detection system for spacecraft telemetry on space-grade hardware. It included the compilation of a new labeled spacecraft anomaly detection dataset, the development of ML models, the implementation of an on-board computer with the capability of supervising ML applications and finally the accelerated deployment of ML models on space-grade hardware.

  • Machine Learning for Telecommunication Satellites (MaLeTeSa): This Airbus Defence and Space project aims to develop a custom machine learning module for satellite communication processors and is funded by Deutsches Zentrum für Luft- und Raumfahrt (DLR). To this end, various ML approaches are researched and applications include anomaly detection in telemetry data, identification of different users of the radio frequency spectrum and optimization of communication networks.

Publications

  1. Petry, M., Koch, A., & Werner, M. (2023). Envisioning Physical Layer Flexibility Through the Power of Machine-Learning. GC Wkshps. [PDF] [BibTeX]
  2. Petry, M., Wuwer, G., Koch, A., Gest, P., Ghiglione, M., & Werner, M. (2023). Accelerated Deep-Learning Inference on the Versal adaptive SoC in the Space Domain. 2023 European Data Handling & Data Processing Conference (EDHPC), 1–8. https://doi.org/10.23919/EDHPC59100.2023.10396011 [Online] [BibTeX]
  3. Koch, A., Krstova, A., Hegwein, F., De Lera, M. C., Ales, F., Petry, M., Ali, R., Mallah, M., Hili, L., Ghiglione, M., & Werner, M. (2023). On-Board Anomaly Detection on a Flight-Ready System. 2023 European Data Handling & Data Processing Conference (EDHPC), 1–4. https://doi.org/10.23919/EDHPC59100.2023.10395967 [PDF] [Online] [BibTeX]
  4. 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. 2023 European Data Handling & Data Processing Conference (EDHPC), 1–3. https://doi.org/10.23919/EDHPC59100.2023.10396582 [PDF] [Online] [BibTeX]
  5. 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]
  6. 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]
  7. 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]
  8. Zhu, X. X., Wang, Y., Kochupillai, M., Werner, M., Häberle, M., Hoffmann, E. J., Taubenböck, H., Tuia, D., Levering, A., Jacobs, N., & others. (2022). Geoinformation harvesting from social media data: A community remote sensing approach. IEEE Geoscience and Remote Sensing Magazine, 10(4), 150–180. [BibTeX]

Other Publications

  1. Hagerer, G., Szabo, D., Koch, A., Dominguez, M. L. R., Widmer, C., Wich, M., Danner, H., & Groh, G. (2021). End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis. https://doi.org/10.48550/ARXIV.2111.02326 [Online] [BibTeX]

© 2020 M. Werner