I’m Fabian Huch, researcher at the Chair for Logic and Verification.
My research is focused around the intersection of Interactive Theorem Proving with Software Engineering and Machine Learning, as well as maintaining and improving the archive of formal proofs.
Contact
| {huch} AT [in.tum.de] | |
| Address | Boltzmannstraße 3, 85748 Garching |
| Office | 00.09.062 |
| Phone | +49 (89) 289 17336 |
| Office Hours | By appointment |
Publications
- Huch, F. (2024). Isabelle as Systems Platform: Managing Automated and Quasi-interactive Builds. To appear in Isabelle Workshop.
- Huch, F., Wenzel, M. (2024). Distributed Parallel Build for the Isabelle Archive of Formal Proofs. In International Conference of Interactive Theorem Proving. doi:10.4230/LIPIcs.ITP.2024.22
- Huch, F., Stathopoulos, Y. (2023). Formalization Quality in Isabelle. In International Conference on Intelligent Computer Mathematics. doi:10.1007/978-3-031-42753-4_10
- Huch, F. (2022). Formal Entity Graphs as Complex Networks: Assessing Centrality Metrics of the Archive of Formal Proofs. In International Conference on Intelligent Computer Mathematics. doi:10.1007/978-3-031-16681-5_10
- MacKenzie, C., Huch, F., Vaughan, J., & Fleuriot, J. (2022). Re-imagining the Isabelle Archive of Formal Proofs. In International Conference on Intelligent Computer Mathematics. doi:10.1007/978-3-031-16681-5_11
- Huch, F., & Bode, V. (2022). The Isabelle Community Benchmark. In Practical Aspects of Automated Reasoning. doi:10.48550/arXiv.2209.13894
- Megdiche, Y., Huch, F., & Stevens, L. (2022). A linter for isabelle: implementation and evaluation. In Isabelle Workshop. doi:10.48550/arXiv.2207.10424
- Huch, F. (2021). Structure in Theorem Proving: Analyzing and Improving the Isabelle Archive of Formal Proofs. In Workshop papers at 14th Conference on Intelligent Computer Mathematics. doi:10.48550/arXiv.2209.13305
- Huch, F., & Krauss, A. (2020). FindFacts: a scalable theorem search. In Isabelle Workshop. doi:10.48550/arXiv.2204.14191
- Huch, F., Golagha, M., Petrovska, A., & Krauss, A. (2018). Machine learning-based run-time anomaly detection in software systems: An industrial evaluation. In IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation. doi:10.1109/MALTESQUE.2018.8368453