Hertie AI Independent Research Groups

01
Kaltenecker, D., Horvath, I., Al-Maskari, R., Chen, Y., Kolabas, Z. I., Hoeher, L., ...Kofler,F. ..., & Ertürk, A. (2026)

A deep-learning framework reveals whole-body perturbations at cell level

May 20, 2026 | Nature, 1-11
02
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.

Dec 03, 2025 | NeurIPS 2025
03
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.

Mar 25, 2025 | Communications Biology, 8(1), 487.
04
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.

Mar 25, 2025 | Communications Biology, 8(1), 487.
05
Nazari, P., Damrich, S., Hamprecht, F.A.

Geometric Autoencoders – What You See is What You Decode

Jul 23, 2023 | International Conference on Machine Learning 2023
06
Damrich, S., Böhm, J. N., Hamprecht, F. A., & Kobak, D.

From t-SNE to UMAP with contrastive learning

May 30, 2023 | Proceedings of the International Conference on Learning Representations (ICLR)