Let’s try to make sense of data by mapping it into two dimensions.

Jan Niklas Böhm

Nik was a PhD student in the Department of Data Science at the Hertie Institute for AI in Brain Health at the University of Tübingen and the IMPRS-IS graduate school. He was interested in dimensionality reduction techniques for high-dimensional data. Nik has finished his PhD in 2025.

Let’s try to make sense of data by mapping it into two dimensions.

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.

Böhm, J. N., Berens, P., & Kobak, D.

Unsupervised visualization of image datasets using contrastive learning

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

From t-SNE to UMAP with contrastive learning

Böhm, J. N., Berens, P., & Kobak, D.

Jan Niklas Böhm

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

Unsupervised visualization of image datasets using contrastive learning

From t-SNE to UMAP with contrastive learning

Attraction-repulsion spectrum in neighbor embeddings