Publications

1. Bao, S., Alonso, L., Wang, S., Gensheimer, J., De, R., & Carvalhais, N. (2023).
Toward robust parameterizations in ecosystem-level photosynthesis models.
Journal of Advances in Modeling Earth Systems, 15, e2022MS003464. https://doi.org/10.1029/2022MS003464External link

2. ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine, P., &
Winkler, A. J. (2023). Hybrid modeling of evapotranspiration: Inferring stomatal
and aerodynamic resistances using combined physics-based and machine
learning. Environmental Research Letters, 18, 034039. https://doi.org/10.1088/1748-9326/acbbe0External link

3. Jiang, S., Sweet, L., Blougouras, G., Brenning, A., Li, W., Reichstein, M.,
Denzler, J., Shangguan, W., Yu, G., Huang, F., & Zscheischler, J. (2024). How
interpretable machine learning can benefit process understanding in the
geosciences. Earth’s Future, 12, e2024EF004540. https://doi.org/10.1029/2024EF004540External link

4. Kahlmeyer, P., Fischer, M., & Giesen, J. (2025). Dimension reduction for symbolic
regression. Proceedings of the 39th AAAI Conference on Artificial Intelligence
(AAAI). https://doi.org/10.1609/aaai.v39i17.33947External link

5. Kahlmeyer, P., Giesen, J., Habeck, M., & Voigt, H. (2024). Scaling up unbiased
search-based symbolic. Proceedings of the 33rd International Joint Conference
on Artificial Intelligence (IJCAI), 4264-4272. https://doi.org/10.24963/ijcai.2024/471External link

6. Körschens, M., Bucher, S. F., Bodesheim, P., Ulrich, J., Denzler, J., &
Römermann, C. (2024). Determining the community composition of herbaceous
species from images using convolutional neural networks. Ecological Informatics,
80, 102516. https://doi.org/10.1016/j.ecoinf.2024.102516External link

7. Lawonn, K., Meuschke, M., Eulzer, P., Mitterreiter, M., Giesen, J., & Günther, T.
(2023). GRay: Ray casting for visualization and interactive data exploration of
Gaussian mixture models. IEEE Transactions on Visualization and Computer
Graphics, 29(1), 526-536. https://doi.org/10.1109/TVCG.2022.3209374External link

8. Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creutzig, F., Fearnley, C.
J., Han, B., Kornhuber, K., Rahaman, N., Schölkopf, B., Tárraga, J. M., Vinuesa,
R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D. C., &
Weldemariam, K. (2025). Early warning of complex climate risk with integrated
artificial intelligence. Nature Communications, 16, Article 2564. https://doi.org/10.1038/s41467-025-57640-wExternal link

9. Stein, G., Shadaydeh, M., Penzel, N., Blunk, J., & Denzler, J. (2025).
CausalRivers – Scaling up benchmarking of causal discovery for real-world timeseries.
International Conference on Learning Representations. https://openreview.net/pdf?id=wmV4cIbgl6External link

10. Wang, Z., Goetz, J., & Brenning, A. (2022). Transfer learning for landslide
susceptibility modelling using domain adaptation and case-based reasoning.
Geoscientific Model Development, 15, 8765–8784. https://doi.org/10.5194/gmd-15-8765-2022External link