CV

Education

• Ph.D., Computer Science, Universität Konstanz, 2025 - 2029 (expected)
• M.Sc., Computer Science, Hochschule Karlsruhe (HKA), 2022 - 2024
• B.A.Sc., Computer Science, Hochschule Karlsruhe (HKA), 2018 - 2022
• Electrical Engineering and Information Technology, Karlsruher Institut für Technologie (KIT), 2017 - 2018
• Abitur, Landesgymnasium für Musik Wernigerode, 2008 - 2016

Work experience

• January 2025 - Present: Research Assistant
  • Universität St.Gallen (HSG), Sankt Gallen, Switzerland
  • Full-Time Research Assistant and PhD candidate
  • Research: Part of the DS-NLP Lab, researching Transformer Architecture and Pre-training/Foundation Models
  • Teaching Assistant: Statistics for Data Scientists (BA CS), NLP with LLMs (MA CS)
• June 2024 - July 2024: Working Student - Data and AI
  • esentri, Ettlingen, Germany
  • Review and documentation of the Master’s thesis as well as development of a product POC
• December 2023 - June 2024: Master Thesis - Fine-Tuning LLMs for Code Generation
  • esentri, Ettlingen, Germany
  • Master Thesis: “Fine-tuning of large-language models on a single GPU for a specific code syntax”
  • Development of a code assistant for Esentri’s proprietary Domain-Driven Design Framework
• November 2023 - December 2023: Data Science and Machine Learning
  • esentri, Ettlingen, Germany
  • Working student as preparation for master thesis
• September 2021 - February 2022: Bachelor Thesis - IT Security
  • 1&1 Mail and Media, Karlsruhe, Germany
• March 2020 - March 2021: Consulting Services
  • CAS Software AG, Karlsruhe, Germany
• June 2017 - July 2017: IT-Internship
  • Dell Technologies, Halle (Saale), Germany

Publications

Is More Data Worth the Cost? Dataset Scaling Laws in a Tiny Attention-Only Decoder

Wiegand, G.-H., Raichle, L., Staedeli, R., Handschuh, S., Hrycej, T., & Bermeitinger, B. (2026). "Is More Data Worth the Cost? Dataset Scaling Laws in a Tiny Attention-Only Decoder." IEEE International Conference on Data Science (SDS 2026), Zürich, Switzerland.

A Convexity-Dependent Two-Phase Training Algorithm for Deep Neural Networks

Hrycej, T., Bermeitinger, B., Pavone, M., Wiegand, G.-H., & Handschuh, S. (2025). "A Convexity-Dependent Two-Phase Training Algorithm for Deep Neural Networks." Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 78–86.

Leveraging Generative AI for Enhancing Domain-Driven Software Design

Wiegand, G.-H., Stepniak, F., & Baier, P. (2024). "Leveraging Generative AI for Enhancing Domain-Driven Software Design." Proceedings of the Upper-Rhine Artificial Intelligence Symposium, 2024, 41–50.