Luka Biedebach
Luka Biedebach is a Postdoctoral Researcher at Reykjavik University. She defended her PhD thesis at Reykjavik University in 2025. Her research focus is on unsupervised machine learning in sleep research. In her PhD thesis, she explored different sleep data types, as well as different unsupervised learning methods.
Luka Biedebach first joined the Sleep Revolution in 2021. During this time, she created an unsupervised reconstruction-based anomaly detection model that classifies breathing sequences as her master thesis. She then graduated from the University of Mannheim with a Master Degree in Data Science in.
Research Interests
- Sleep
- Unsupervised Learning
- Health Data
Teaching
- SLEEP (Spring Semester 2023-2024)
- Digital Health (Fall Semester 2023-2024)
- Applications of Digital Health (Fall Semester 2025 -2026)
Publications
Biedebach L., Friðgeirsdóttir K. Ý.,Carpinelli C., Isberg A. P., Helgadóttir H.,Arnardóttir E. S., Saavedra J. M., Islind A. S. Mining Association Rules From a Multimodal Dataset of a Digital Therapeutics Application for Sleep Improvement Through a Healthy Lifestyle: Quantitative Study
JMIR Form Res 2026;10:e75358
Biedebach, L., Ferreira-Santos, D., Stefanos, M.-A., Lindhagen, A., Pires, G.N., Arnardóttir, E.S., Islind, A. S. (2025) Unsupervised Machine Learning in Sleep Research: A Scoping Review, Sleep.
Biedebach, L., Óskarsdóttir, M., Arnardottir, E. S., & Islind, A. S. (2023) Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning. 44th International Conference on Information Systems.
Biedebach, L., Óskarsdóttir, M., Arnardóttir, E.S., Sigurdardóttir, S., Clausen, M. V., Sigurdardóttir, S. Þ., Serwatko, M. & Islind, A.S. (2023) Anomaly detection in sleep: detecting mouth breathing in children. Data Mining and Knowledge Discovery.
Biedebach, L., Rusanen, M., Þórðarson, B., Arnadóttir, E. S., Óskarsdóttir, M., Nikkonen, S., … & Islind, A. S. (2023).,Towards a Deeper Understanding of Sleep Stages through their Representation in the Latent Space of Variational Autoencoders. 56th Hawaii International Conference on System Sciences, 3111-3121.