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

J Thorben Frank, Oliver T Unke, Klaus-Robert Müller, Stefan Chmiela
January 2024 Nature Communications
A Euclidean transformer for fast and stable machine learned force fields.
Jonas Dippel, Niklas Prenißl, Julius Hense, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, Oliver Buchstab, David Horst, Maximilian Alber, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen
January 2024 NEJM AI
AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics.
DOI URL
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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.