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

Petry, M., Koch, A., & Werner, M. (2023). Envisioning Physical Layer Flexibility Through the Power of Machine-Learning. GC Wkshps. [PDF] [BibTeX]
```
@article{2023_globecom_bm_petry,
  title = {Envisioning Physical Layer Flexibility Through the Power of Machine-Learning},
  author = {Petry, Michael and Koch, Andreas and Werner, Martin},
  journal = {GC Wkshps},
  year = {2023},
  month = dec
}
```

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]

@article{2023_edhpc_versal_petry,
  author = {Petry, Michael and Wuwer, Gabriel and Koch, Andreas and Gest, Patrick and Ghiglione, Max and Werner, Martin},
  booktitle = {2023 European Data Handling & Data Processing Conference (EDHPC)},
  title = {Accelerated Deep-Learning Inference on the Versal adaptive SoC in the Space Domain},
  year = {2023},
  month = oct,
  pages = {1-8},
  keywords = {Adaptation models;Adaptive systems;Parallel processing;Throughput;Hardware;System-on-chip;Field programmable gate arrays;fpga;hardware accelerator;neural network;machine learning;AMD-Xilinx Versal;roofline model},
  doi = {10.23919/EDHPC59100.2023.10396011},
  isbn = {978-9-09-037924-1},
  url = {https://doi.org/10.23919/EDHPC59100.2023.10396011},
  location = {Juan Les Pins, France}
}

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]

@article{2023_ADAP_Andreas,
  author = {Koch, Andreas and Krstova, Alisa and Hegwein, Florian and De Lera, Mario Castro and Ales, Filippo and Petry, Michael and Ali, Rashid and Mallah, Maen and Hili, Laurent and Ghiglione, Max and Werner, Martin},
  title = {On-Board Anomaly Detection on a Flight-Ready System},
  booktitle = {2023 European Data Handling & Data Processing Conference (EDHPC)},
  isbn = {978-9-09-037924-1},
  doi = {10.23919/EDHPC59100.2023.10395967},
  url = {https://doi.org/10.23919/EDHPC59100.2023.10395967},
  year = {2023},
  month = oct,
  keywords = {Machine learning algorithms;Satellites;Telemetry;Anomaly detection;Hardware acceleration;Research and development;Matlab;Machine learning;anomaly detection;FPGA;PUS;co-processor;on-board AI},
  pages = {1-4},
  location = {Juan Les Pins, France}
}

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]

@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},
  booktitle = {2023 European Data Handling & Data Processing Conference (EDHPC)},
  isbn = {978-9-09-037924-1},
  doi = {10.23919/EDHPC59100.2023.10396582},
  url = {https://doi.org/10.23919/EDHPC59100.2023.10396582},
  year = {2023},
  month = oct,
  keywords = {Earth;Satellites;Publishing;Machine learning;Benchmark testing;Hardware;Telemetry;Machine learning;neural networks;benchmark;FPGA;datasets;power consumption},
  pages = {1-3},
  location = {Juan Les Pins, France},
  project = {mlab}
}

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}
}
```
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}
}
```
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}
}
```
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. [PDF] [BibTeX]
```
@article{zhu2022geoinformation,
  title = {Geoinformation harvesting from social media data: A community remote sensing approach},
  author = {Zhu, Xiao Xiang and Wang, Yuanyuan and Kochupillai, Mrinalini and Werner, Martin and H{\"a}berle, Matthias and Hoffmann, Eike Jens and Taubenb{\"o}ck, Hannes and Tuia, Devis and Levering, Alex and Jacobs, Nathan and others},
  journal = {IEEE Geoscience and Remote Sensing Magazine},
  volume = {10},
  number = {4},
  pages = {150--180},
  year = {2022},
  publisher = {IEEE}
}
```

Other Publications

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}
}