Banner Banner

Machine Learning and Intelligent Data Analysis


Prof. Dr. Klaus-Robert Müller


Technische Universität Berlin
Marchstraße 23, 10587 Berlin

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

March 08 , 2021

Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, Klaus-Robert Müller

Optimizing spatial filters for robust eeg single-trial analysis

January 31 , 2008

Yann A. LeCun, Léon Bottou, Genevieve B. Orr, Klaus-Robert Müller

Efficient backprop, neural networks: Tricks of the trade

November 14 , 2012

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

February 04 , 2021

Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld

Fast and accurate modeling of molecular atomization energies with machine learning

January 31 , 2012

October 16, 2023

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.

C: BIFOLD/Michael Setzpfandt
October 11, 2023

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. 

C: BIFOLD/Michael Setzpfandt
October 10, 2023

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.

Christopher J. Anders

Doctoral Researcher

Andrea Gerdes BIFOLD Management

Andrea Gerdes


Adrian Hill BIOFLD Doctoral Researcher

Adrian Hill

Doctoral Researcher

Dr. Mina Jamshidi Idaji BIFOLD researcher

Dr. Mina Jamshidi Idaji

Postdoctoral Researcher

Jonas Lederer BIFOLD researcher

Jonas Lederer

Doctoral Researcher

Lukas Muttenthaler

Doctoral Researcher

Farnoush Rezaei Jafari Bifold researcher

Farnoush Rezaei Jafari

Doctoral Researcher

Thomas Schnake

Doctoral Researcher

Parastoo Semnani

Doctoral Researcher

Robert Vandermeulen Bifold researcher

Dr. Robert Vandermeulen

Postdoctoral Researcher

Ludwig Winkler

Doctoral Researcher

Dr. Andreas Ziehe Bifold Researcher

Dr. Andreas Ziehe

Postdoctoral Researcher