Luka Biedebach
Luka Biedebach first joined the Sleep Revolution in 2021 to write her master thesis on identifying paediatric mouth breathing during sleep. During this time, she created an unsupervised reconstruction-based anomaly detection model that classifies breathing sequences. She then graduated from the University of Mannheim with a Master Degree in Data Science. Exploring all the possibilities of applying machine learning in sleep research raised her interest to dive deeper into this field.
In 2022 she came back to Reykjavik University as Ph.D. Candidate within the Sleep Revolution. 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. She defended her PhD in April 2025 and is now working as a Postdoctoral Researcher at Reykjavik University.
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)
Publications
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.