Anna Bodonhelyi


Anna Bodonhelyi

Wissenschaftliche Mitarbeiterin, Doktorandin

Technische Universität München

TUM School of Social Sciences and Technology

Lehrstuhl Human-Centered Technologies for Learning

Besucheradresse:
Marsstraße 20-22
80335 München

Postanschrift:
Arcisstraße 21
80333 München

Raum: 257

Sprechzeiten: nach Vereinbarung

Tel.: +49 89 289 24343

E-Mail: anna.bodonhelyi@tum.de

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Bio

I am a dedicated Ph.D. candidate at the Technical University of Munich specializing in machine learning, having earned dual master's degrees from the Budapest University of Technology and Economics in Mechatronics and the Technical University of Munich in Robotics, Cognition, Intelligence. With a solid foundation in interdisciplinary fields, in my Ph.D. research, I actively engage in exploring the dynamic realm of human-AI interaction, seeking to enhance the symbiotic relationship between individuals and artificial intelligence through innovative methodologies such as federated learning, computer vision, generative AI, and privacy-enhancing technologies.

Werdegang

  • 2016 – 2020: B.Sc. in Mechatronics Engineering at Budapest University of Technology and Economics, Hungary
  • 2018 – 2019: ERASMUS Semester at Karlsruhe Institute of Technology, Germany
  • 2020 – 2022: M.Sc. in Mechatronics Engineering at Budapest University of Technology and Economics, Hungary
  • 2020 – 2023: M.Sc. in Robotics, Cognition, Intelligence at Technical University of Munich, Germany
  • 2023 – now: PhD Candidate at Technical University of Munich, Germany

Forschungsschwerpunkt

  • Machine Learning
  • Computer Vision
  • GenAI
  • HCI
  • LLMs

Publications

  • Bodonhelyi, A., Bozkir, E., Yang, S., Kasneci, E., & Kasneci, G. (2024). User Intent Recognition and Satisfaction with Large Language Models: A User Study with ChatGPT. arXiv preprint arXiv:2402.02136.
    arxiv.org/pdf/2402.02136.pdf
  • Wang, M., Bodonhelyi, A., Bozkir, E., & Kasneci, E. (2024). TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. arXiv preprint arXiv:2401.12012.
    https://arxiv.org/html/2401.12012v3