• 10/2020 - Now: Research associate in the Critical Information Infrastructures research group at the Karlsruhe Institute of Technology, Germany
• 11/2019 - 01/2020: Teaching Assistant in Constraint Programming for Combinatorial Optimisation at Uppsala Universitet, Sweden
• 10/2018 - 07/2020: Master of Science in Computer Science at Uppsala Universitet, Sweden
• 10/2017 - 07/2018: Software Developer for Embedded Systems at SIEMENS AG in Karlsruhe
• 10/2014 - 09/2017: Bachelor of Science in Applied Computer Science at Baden-Wuerttemberg Cooperative State University, Karlsruhe
• 09/2014 - 09/2017: Cooperative Education Student at SIEMENS AG in Karlsruhe

• Privacy Preserving Machine Learning
• Technical Privacy Measures (e.g. Differential Privacy)
• Privacy Enhancing Technologies
• Federated Learning

2022

• Hasebrook, N., Morsbach, F., Kannengießer, N., Franke, J., Hutter, F., & Sunyaev, A. (2022). Why Do Machine Learning Practitioners Still Use Manual Tuning? A Qualitative Study. Preprint. https://doi.org/10.5445/IR/1000143445

2021

• Morsbach, F., Dehling, T., & Sunyaev, A. (2021). Architecture Matters: Investigating the Influence of Differential Privacy on Neural Network Design. Presented at NeurIPS 2021 Workshop on Privacy in Machine Learning (PriML 2021). https://doi.org/10.5445/IR/1000140769
• Morsbach, F., & Toor, S. (2021). DecFL: An Ubiquitous Decentralized Model Training Protocol and Framework Empowered by Blockchain. Proceedings of the 3rd ACM International Symposium on Blockchain and Secure Critical Infrastructure, 61–70. https://doi.org/10.1145/3457337.3457842