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Machine Learning and Intelligent Data Analysis

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Lead
Prof. Dr. Klaus-Robert Müller

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Technische Universität Berlin
Marchstraße 23, 10587 Berlin

Interpretable ML methods, Data modeling, Anomaly dection 

 

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, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller, Frederick Klauschen

Morphological and molecular breast cancer profiling through explainable machine learning

March 08 , 2021
https://www.nature.com/articles/s42256-021-00303-4

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

Optimizing spatial filters for robust eeg single-trial analysis

January 31 , 2008
https://my.ece.utah.edu/~ece6534/notes/2017_ece6534_lecture28.pdf

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
https://ieeexplore.ieee.org/abstract/document/9347460

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
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.108.058301

Christopher J. Anders

Doctoral Researcher

BIFOLD researcher Florian Bley

Florian Bley

Doctoral Researcher

Stefan Blücher

Doctoral Researcher

Jonas Dippel

Doctoral Researcher

Oliver Eberle Bifold Berlin Postdoctoral Researcher

Dr. Oliver Eberle

Postdoctoral Researcher

Andrea Gerdes BIFOLD Management

Andrea Gerdes

Management

Dennis Grinwald

Doctoral Researcher

Julius Hense

Doctoral Researcher

Stefaan Hessmann BIFOLD researcher

Stefaan Hessmann

Doctoral Researcher

Adrian Hill BIOFLD Doctoral Researcher

Adrian Hill

Doctoral Researcher

Dr. Mina Jamshidi Idaji BIFOLD researcher

Dr. Mina Jamshidi Idaji

Postdoctoral Researcher

Tom Kaufmann BIFOLD researcher

Tom Lukas Kaufmann

Doctoral Researcher

Jonas Lederer BIFOLD researcher

Jonas Lederer

Doctoral Researcher

Bifold researcher Hannah Marienwald

Hannah Marienwald

Doctoral Researcher

Julius Martinetz

Doctoral Researcher

Dr. Osman Musa Bifold researcher

Dr. Osman Musa

Postdoctoral Researcher

Lukas Muttenthaler

Doctoral Researcher

Philip Naumann Bifold Researcher

Philip Naumann

Doctoral Researcher

Farnoush Rezaei Jafari Bifold researcher

Farnoush Rezaei Jafari

Doctoral Researcher

Thomas Schnake

Doctoral Researcher

Robert Vandermeulen Bifold researcher

Dr. Robert Vandermeulen

Postdoctoral Researcher

Ludwig Winkler

Doctoral Researcher

Ping Xiong

Doctoral Researcher

Dr. Andreas Ziehe Bifold Researcher

Dr. Andreas Ziehe

Postdoctoral Researcher