Machine Learning Group

The Machine Learning Group at TU Berlin is chaired by Prof. Dr. Klaus-Robert Müller and focuses on methodological and theoretical improvements as well as applications in machine learning. As for methodology, some of our research areas are explainable AI, probabilistic ML, and multimodal learning. As for applications, exemplary domains are quantum chemistry, digital pathology, and biomedical sensing.

Selected Research Areas

Explainable AI Probabilistic ML Multimodal Learning
Quantum Chemistry Digital Pathology Biomedical Sensing Computational Neuroscience Digital Humanities

Recent Publications

Christoph Engel, Lorenz Linhardt, Marcel Schubert
January 2024 Artificial Intelligence and Law
Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism.
DOI
Pattarawat Chormai, Jan Herrmann, Klaus-Robert Müller, Grégoire Montavon
January 2024 IEEE Transactions on Pattern Analysis and Machine Intelligence
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces.
DOI
Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek
January 2024 Pattern Recognition
Explainable AI for time series via virtual inspection layers.
DOI
See all publications

News

Feb 2024: The AQTIVATE Workshop kicks off on Monday, Feb 12th 2024, at Technische Universität Berlin, offering an immersive dive into Machine Learning with a focus on applications in science. Organized by BIFOLD and the Machine Learning Group, the workshop covers everything from basic principles to specialized uses in physics and chemistry. Further details and updates are available here.