Machine Learning and Intelligent Data Analysis
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.
Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Masaru Ishii, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller, Frederick Klauschen
Morphological and molecular breast cancer profiling through explainable machine learning
Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, Klaus-Robert Müller
Optimizing spatial filters for robust eeg single-trial analysis
Yann A. LeCun, Léon Bottou, Genevieve B. Orr, Klaus-Robert Müller
Efficient backprop, neural networks: Tricks of the trade
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller
A unifying review of deep and shallow anomaly detection
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld
Fast and accurate modeling of molecular atomization energies with machine learning
Shaping Berlin's scientific community
The two BIFOLD initiators and co-directors, Prof. Dr. Volker Markl and Prof. Dr. Klaus-Robert Müller, are considered by the newspaper Berliner Tagesspiegel to be among the 100 most important figures in Berlin's scientific community.
Photo recap: All Hands Meeting 2023
On October 9 and 10, 2023, BIFOLD welcomed the other Geman AI centers (ScaDS.AI Dresden/Leipzig, Lamarr Institute, Tübingen AI Center, MCML, and the DFKI) in Berlin. The annual meeting featured guests, partners, visitors, and researchers from all over Germany.
AI centers are the foundation of the German AI ecosystem
On October 9th and 10th, 2023, the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin invited scientists from the university AI competence centers (BIFOLD, ScaDS.AI Dresden/Leipzig, Lamarr Institute, Tübingen AI Center, and MCML) and the DFKI to Berlin to present and discuss the latest results of their research on the EUREF campus.
Director
Doctoral Researcher
Management
Doctoral Researcher
Postdoctoral Researcher
Doctoral Researcher
Doctoral Researcher
Doctoral Researcher
Doctoral Researcher
Doctoral Researcher
Postdoctoral Researcher
Doctoral Researcher
Postdoctoral Researcher