Max Planck Gesellschaft

Christian Reimers

Postdoc at
room: C1.016 (tower)
phone: +49 3641 576212

Research interests

  • Machine learning
  • Causal Modeling
  • Statistical Learning Theory

My work at BGI

Academic background

since 2021

PostDoc at the Department Biogeochemical Integration at the Max Planck Institute for Biogeochemistry


Doctoral Candidate at Computer Vision Group, Friedrich Schiller University Jena

and Climate Informatics Group, Institute of Data Science, German Aerospace Cen- ter, Jena, Germany.
on the Topic of "Understanding Deep Learning"


Internship at Network Dynamics Group at Max Planck Institute for Dynamics and

Self-Organization, Göttingen.


M.Sc. in Mathematics at Georg-August-University in Göttingen
Master thesis titel:"Almost Prime Zeros of Forms"


B.Sc. in Mathematics at Georg-August-University in Göttingen
''Bachelor thesis title: "Hooley’s solution of a problem of Hardy

and Littlewood"''



Reimers, C. and Bodesheim, P. and Runge, J. and Denzler J. (2021) Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data DAGM German Conference on Pattern Recognition (DAGM-GCPR). (accepted for publication)

Penzel, N., Reimers, C., Brust, C.-A. and Denzler, J. (2021) Investigating the Consistency of Uncertainty Sampling in Deep Active Learning DAGM German Conference on Pattern Recognition (DAGM-GCPR). (accepted for publication)

Tibau, X. A., Reimers, C., Requena‐Mesa, C., & Runge, J. (2021). Spatio‐temporal Autoencoders in Weather and Climate Research. Deep learning for the Earth Sciences: With Applications and R, Second Edition, 186-203.

Reimers, C., Penzel, N., Bodesheim, P., Runge, J and Denzler, J. (2021). Conditional dependence tests reveal the usage of ABCD rule features and bias variables in automatic skin lesion classification. CVPR ISIC Skin Image Analysis Workshop (CVPR-WS).


Reimers, C, Runge, J and Denzler, J. (2020). Determining the Relevance of Features for Deep Neural Networks. ECCV

Reimers, C, Requena-Mesa, C. (2020). Deep Learning – an Opportunity and a Challenge for Geo- and Astrophysics. Knowledge Discovery in Big Data from Astronomy and Earth Observation


Reimers, C, Runge, J , Denzler, J. (2019) Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps. Climate Informatics Workshop 2019 proceedings.


Tibau, X-A*, Requena-Mesa, C*, Reimers, C*, Denzler, J, Eyring, V, Reichstein, M and Runge, J. (2018) SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data. Climate Informatics Workshop 2018 proceedings.

Directions | Disclaimer | Data Protection | Contact | Internal | Webmail | Local weather | PRINT | © 2011-2021 Max Planck Institute for Biogeochemistry