Christopher J. Anders is a research associate at the Berlin Institute for the Foundations of Learning and Data and a postdoctoral researcher in the Machine Learning at TU Berlin since 2018. His research encompasses explainable machine learning, specifically the detection and mitigation of spurious correlations and the robustness of feature attribution methods, software for machine learning, and machine learning for physical sciences (lattice field theory and variational quantum eigensolver). He received his B.Sc. and M.Sc. in Computer Science at Technische Universität Berlin in 2016 and 2018 respectively. He received his Ph.D. in Computer Science at Technische Univsersität Berlin in 2024.