Machine Learning
Interpretable ML Methods, Data Modeling, Anomaly Detection
The Distinguished Research Group of Prof. Dr. Klaus-Robert Müller is concentrating on the development of robust and interpretable machine learning methods for learning from complex structured and non-stationary data and the fusion of heterogeneous multi-modal data sources. A special focus lies on the efficient modeling of non-stationary, heterogeneous and structured data sources with deep learning and kernel methods. He and his team also work on theoretically sound incorporation of a priori knowledge from the application domain as well as the detection of anomalies in structured data. The resulting models are expected not only to be accurate, but also to explain their nonlinear decisions, quantify decision uncertainties, and create new knowledge about the studied data. In addition, Klaus-Robert Müller has been pursuing a long history of bringing machine learning into the sciences, which has helped to arrive at genuinely novel insights. In the last decade his attention has focused primarily on quantum chemistry, cancer research as well as computational neuroscience.
Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Dicke superradiant heat current enhancement in circuit quantum electrodynamics
Paper Forecast NeurIPS 2024
Several BIFOLD research groups participate in the 48th Annual Conference on Neural Information Processing Systems in Vancouver, Canada, taking place from December 10 to 15, 2024.
Klaus-Robert Müller on "Highly Cited Researchers" list
Since 2019, Prof. Dr. Klaus-Robert Müller has consistently appeared on the "Highly Cited" list, affirming his lasting contribution to groundbreaking research.
Researcher Spotlight: Dr. Lisa Raithel
Dr. Lisa Raithel is working at the integration of natural language processing (NLP) and medicine. Recently completing her PhD, she dives deep into the multilingual analysis of health data to uncover the real-world effects of medications.
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