Dr. Christian Reimers

PostDoc

Department Biogeochemical Integration (BGI)
Atmosphere-Biosphere Coupling, Climate and Causality
+49 3641 57-8923

creimers@...

B3.01

MPG Publications

Curriculum Vitae

Research interests

Machine learning
Causal Modeling
Statistical Learning Theory

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.