Contact
Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA)
Fabricestraße 8
01099 Dresden
Journals and Conferences:
2024
- G. Siedel, E. Gupta, and A. Morozov, “A practical approach to evaluating the adversarial distance for machine learning classifiers.” 2024.
2023
- G. Siedel, S. Vock, and A. Morozov, “Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions,” May 2023.
2022
- G. Siedel, S. Vock, A. Morozov, and S. Voß, “Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers,” in The IJCAI-ECAI-22 Workshop on Artificial Intelligence Safety (AISafety), 2022, 2022.
2021
- G. Siedel, S. Vock, and S. Voß, “An overview of the research landscape in the field of safe machine learning,” in International Mechanical Engineering Congress and Exposition, IMECE, 2021.
Georg Siedel is an external PhD student at IAS Stuttgart, employed with BAuA, the German Federal Research Institute for Occupational Health and Safety. His research project is about risk assessment of Cyber-Physical Systems, where he focuses on dependability aspects of Machine Learning components within those systems. In particular, methods to improve and evaluate robustness, generalization ability and performance under distributional shift are in focus.