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 in 2022. 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. She currently researches on applying unsupervised learning on sleep EEGs and sleep quality measurements from wearable sensors.
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.