I am a PhD student and research assistant at the Chair of Computational Linguistics at the University of Augsburg, supervised by Prof. Annemarie Friedrich. My research focuses on natural language processing in low-resource settings, with a particular interest in making NLP systems and generative AI-powered dialogue applications accessible to speakers of dialects and non-standard language varieties.

Before joining academia full-time, I worked as a Junior Data Scientist at CHECK24, where I developed and improved RAG-based chatbot and mailbot systems, built agentic LLM frameworks for automated customer service, and introduced generative AI components into production dialogue pipelines.

Research Interests

My work sits at the intersection of low-resource NLP, dialogue systems, and language variation. I am broadly interested in:

• Low-resource NLP — robust models for languages and varieties with limited training data
• Narrative detection, fact checking & misinformation detection — understanding how information is framed and verified in text
• Human-centric and trustworthy NLP — building systems that are reliable, interpretable, and fair
• NLP for dialects and non-standard languages — extending the reach of NLP tools beyond standard written language

The question that ties these threads together: How can we make generative AI-powered dialogue systems and NLP applications safely accessible — especially for dialectal and non-standard language speakers?

Background

I hold an M.Sc. in Computational Linguistics (grade 1.11) and a B.Sc. in Computational Linguistics (grade 1.58), both from Ludwig-Maximilians-University Munich, as well as a B.A. in English Studies. My master’s thesis investigated zero-shot transfer learning for slot and intent detection in Upper German dialects; my bachelor’s thesis in computational linguistics focused on multilingual gold standard creation for case marker extraction.