• Rubehn, A., Nieder, J., Forkel, R., and List, J.-M. (2024). Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats. arXiv preprint 2405.04271, to appear in Proceedings of the 2024 Meeting of the Society for Computation in Linguistics (SCiL). https://doi.org/10.48550/arXiv.2405.04271
• Rubehn, A. (2022). A feature-based neural model of sound change informed by global lexicostatistical data. Master's thesis, Eberhard Karls Universität Tübingen. https://doi.org/10.15496/publikation-94055

I am a PhD student within the „ProduSemy“ project and focus on computer-assisted, data-driven methods for historical linguistics. I aim at advancing comparative historical linguistics by the means of intelligent algorithmic methods that can alleviate researchers’ workload by processing large-scale data efficiently.

I have studied Computational Linguistics, General Linguistics, and Latin at the University of Tübingen. Within my MA thesis project I have trained a neural network that estimates global probabilities for arbitrary sound changes. Additionally, I have years of working experience as a software developer for EtInEn (Etymological Inference Engine), a software for historical linguists that is being developed at the Linguistic Department in Tübingen. Based on statistical methods, EtInEn automates several routine tasks like (among others) cognate detection, sound law inference, or phonetic reconstruction, and interactively assists users in exploring ideas and developing etymological theories.