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Dr. Christopher J. Anders


Technische Universität Berlin
Machine Learning

Marchstraße 23, 10587 Berlin

Christoph Anders BIFOLD

Dr. Christopher J. Anders

Postdoctoral Researcher

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.

  • Adversarial Machine Learning
  • Covariate Shift
  • Deep Learning
  • Explainable AI
  • Feature Attribution
  • Gaussian Process Regression
  • ML for Physical Sciences
  • Probabilistic ML
  • Quantum Computing
  • Representation Learning
  • Robust Machine Learning
  • Software for Machine Learning
  • Spurious Correlations

Kim Andrea Nicoli, Christopher Anders, Lena Funcke, Karl Jansen, Shinichi Nakajima

NeuLat: a toolbox for neural samplers in lattice field theories

December 27, 2023

Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima

Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories

December 08, 2023

Kim Andrea Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kuhn, Klaus-Robert Muller, Paolo Stornati, Pan Kessel, Shinichi Nakajima

Physics-Informed Bayesian Optimization of Variational Quantum Circuits

September 21, 2023

Wojciech Samek, Gregoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

March 04, 2021

Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati

On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

January 19, 2021

Machine Learning| Sep 23, 2023

CORALS at Tokyo Biennale 2023

CORALS is a kinetic sound sculpture by the Italian media artist Marco Barotti. The installation was created as part of the BIFOLD Artist in Residence Program and is now being exhibited at one of the largest art fairs in Asia.