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
• Anomaly Detection in Satellite Telemetry
• Satellite Payload Data Processing
• AI hardware acceleration

Projects

• 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. 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]

   @article{2023_MLAB2_Andreas,
     author = {Koch, Andreas and Dax, Gabriel and Petry, Michael and Gomez, Harvey and Raoofy, Amir and Saroliya, Urvij and Ghiglione, Max and Furano, Gianluca and Werner, Martin and Trinitis, Carsten and Langer, Martin},
     title = {Reference Implementations for Machine Learning Application Benchmark},
     journal = {EDHPC 2023 - European Data Handling and Data Processing Conference},
     year = {2023},
     month = oct,
     project = {mlab}
   }
   

2. 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]

   @article{2023_MLAB_Andreas,
     author = {Koch, Andreas and Petry, Michael and Ghiglione, Max and Raoofy, Amir and Dax, Gabriel and Furano, Gianluca and Werner, Martin and Trinitis, Carsten and Langer, Martin},
     title = {Machine Learning Application Benchmark},
     doi = {10.1145/3587135.3592769},
     isbn = {979-8-4007-0140-5/23/05},
     booktitle = {20th ACM International Conference on Computing Frontiers (CF
     '23), May 9--11, 2023, Bologna, Italy},
     publisher = {ACM},
     year = {2023},
     month = may,
     project = {mlab}
   }
   

3. 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]

   @article{2023_Computing_Frontiers_Versal_Michael,
     title = {Accelerated Deep-Learning inference on FPGAs in the Space Domain},
     journal = {Computing Frontiers 2023},
     author = {Petry, Michael and Gest, Petrick and Koch, Andreas and Ghiglione, Max and Werner, Martin},
     year = {2023}
   }
   

4. 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]

   @article{2023_Dasia_Michael,
     title = {Machine Learning on Telecommunication Satellite},
     journal = {DATA SYSTEMS IN AEROSPACE - 2023 DASIA},
     author = {Petry, Michael and Koch, Andreas and Werner, Martin and Hoch, Uwe and Helfers, Tim and Wiest, Richard},
     year = {2023}
   }
   

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]

   @article{https://doi.org/10.48550/arxiv.2111.02326,
     doi = {10.48550/ARXIV.2111.02326},
     url = {https://arxiv.org/abs/2111.02326},
     author = {Hagerer, Gerhard and Szabo, David and Koch, Andreas and Dominguez, Maria Luisa Ripoll and Widmer, Christian and Wich, Maximilian and Danner, Hannah and Groh, Georg},
     keywords = {Computation and Language (cs.CL), Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
     title = {End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis},
     publisher = {arXiv},
     year = {2021},
     copyright = {Creative Commons Attribution 4.0 International}
   }