Dr. Christian Reimers

Research Interests

• Machine learning
• Causal Modeling
• Statistical Learning Theory

Academic Background

• Since 2021: PostDoc at the Department Biogeochemical Integration, 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 Center, Jena, Germany. Topic: "Understanding Deep Learning"
• 2015-2016: Internship at Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization, Göttingen
• 2013-2017: M.Sc. in Mathematics, Georg-August-University, Göttingen. Master thesis title: "Almost Prime Zeros of Forms"
• 2011-2013: B.Sc. in Mathematics, Georg-August-University, Göttingen. Bachelor thesis title: "Hooley’s solution of a problem of Hardy and Littlewood"

Publications

2021

• Reimers, C., Bodesheim, P., Runge, J., & 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., & 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., & 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., & 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, & Runge, J. (2018) SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data. Climate Informatics Workshop 2018 proceedings.