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 Early Career Research Group Leader at the Hertie AI, 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.
Present Positions And Title
Group Leader
Research Group
Email Address
Phone
Career
| Period | Institution | Role |
|---|---|---|
| Since 2023 | University of Tübingen | PostDoc |
| 2018 - 2022 | Heidelberg University & University Hospital Heidelberg | PhD Student |
| 2018 | SAP SE | Working Student |
Academic Education
| Year | Degree | Institution | Field of Study |
|---|---|---|---|
| 2022 | PhD | Heidelberg University | Computer Science (interdisciplinary with Mathematics) |
| 2018 | MSc | Heidelberg University | Mathematics |
| 2016 | MASt | University of Cambridge | Mathematics |
| 2015 | BSc | Heidelberg University | Mathematics |