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
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., Thaqi, E., Özdel, S., Bozkir, E., & Kasneci, E. (2024). From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant. arXiv preprint arXiv:2409.15843. https://arxiv.org/pdf/2409.15843
- 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, March). TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 14, pp. 15546-15554). https://doi.org/10.1609/aaai.v38i14.29481
Also, check Google Scholar for an up-to-date list.
Outreach Activities
-
10.23 Organization of WiDS Munich event
-
11.23 TUM EdTech Webinar: "Generative AI in Academia"
-
02.24 AAAI Poster presenation in Vancouver
-
07.24 Seminar Berufliche Bildung: "Videogeneration with AI"
-
08.24 Workshop in Elmau :"Kreativ mit KI"