Annotated data is a scarce resource. Unsupervised learning enables researchers to discover structure in unlabelled data sets.

Dr. Sebastian Damrich

Sebastian Damrich is an Early Career Research Group Leader at the Hertie AI, where he develops and analyses machine learning models for neuroscience and 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. He is a member of the ELLIS and the SIAM societies.

Annotated data is a scarce resource. Unsupervised learning enables researchers to discover structure in unlabelled data sets.

Schmors, L., Gonschorek, D., Böhm, J. N., Qiu, Y., Zhou, N., Kobak, D., ... & Berens, P.

TRACE: Contrastive learning for multi-trial time-series data in neuroscience.

Frangos, S. M., Damrich, S., Gueiber, D., Sanchez, C. P., Wiedemann, P., Schwarz, U. S., ... & Lanzer, M.

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Nazari, P., Damrich, S., Hamprecht, F.A.

Geometric Autoencoders – What You See is What You Decode

Damrich, S., Böhm, J. N., Hamprecht, F. A., & Kobak, D.

Dr. Sebastian Damrich

TRACE: Contrastive learning for multi-trial time-series data in neuroscience.

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Geometric Autoencoders – What You See is What You Decode

From t-SNE to UMAP with contrastive learning