Max Planck Gesellschaft

Christian Reimers

Postdoc at
room: C1.016 (tower)
phone: +49 3641 576212
email: creimers@bgc-jena.mpg.de



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


2017-2021

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"

2015-2016

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

Self-Organization, Göttingen.

2013-2017

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


2011-2013

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

and Littlewood"''


Publications

2021

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).

2020

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

2019

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

2018

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.

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