Annotated data is a scarce resource. Unsupervised and self-supervised learning techniques enable researchers to discover structure in unlabelled data sets.
Dr. Sebastian Damrich
Sebastian is a PostDoc in the Department of Data Science at the Hertie Institute for AI in Brain Health at the University of Tübingen, where he develops and analyses machine learning models for biomedical data. His focus is on unsupervised and self-supervised methods for representation learning, in particular clustering, dimensionality reduction and visualization, but he has worked on image segmentation as well. With a background in Mathematics, he is also interested geometric deep learning and topological data analysis.
Annotated data is a scarce resource. Unsupervised and self-supervised learning techniques enable researchers to discover structure in unlabelled data sets.