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Prof. Dr. Grégoire Montavon


Freie Universität Berlin
Explaining Deep Neural Networks

Arnimallee 7, 14195 Berlin

© Montavon

Prof. Dr. Grégoire Montavon

Research Junior Group Lead



Prof. Dr. Grégoire Montavon is a Guest Professor at the Freie Universität Berlin and a Research Group Lead in the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He received a Masters degree in Communication Systems from École Polytechnique Fédérale de Lausanne in 2009, and a Ph.D. degree in Machine Learning from the Technische Universität Berlin in 2013.

His current research focuses on methods of explainable AI (XAI) for deep neural networks and unsupervised learning, and on closing the gap between existing XAI methods and practical desiderata. This includes using XAI to build more trustworthy machine learning models and using XAI to extract actionable insights from complex datasets.

Jointly with his colleagues, he contributed to Layer-Wise Relevance Propagation (LRP), an efficient method for explaining the predictions of large deep neural networks. He and his co-authors also contributed to the "Neuralization-Propagation" framework which rewrites popular unsupervised learning models as functionally equivalent neural networks for explainability purposes, and higher-order extensions of LRP (BiLRP and GNN-LRP) which enable the identification of joint features contributions in models with product structures.

  • 2013 Dimitris N. Chorafas Award
  • 2020 Pattern Recognition Best Paper Award
  • 2022 Digital Signal Processing Best Paper Award

  • Explainable AI
  • Machine Learning
  • Data Science

Scholar,  European Laboratory for Learning and Intelligent Systems (ELLIS), 2021–now  

Farnoush Rezaei Jafari, Grégoire Montavon, Klaus-Robert Müller, Oliver Eberle

MambaLRP: Explaining Selective State Space Sequence Models

June 11, 2024

Simon Letzgus, Klaus-Robert Müller, Grégoire Montavon

XpertAI: uncovering model strategies for sub-manifolds

March 12, 2024

Frederick Klauschen, Jonas Dippel, Philipp Keyl, Philipp Jurmeister, Michael Bockmayr, Andreas Mock, Oliver Buchstab, Maximilian Alber, Lukas Ruff, Grégoire Montavon & Klaus-Robert Müller

Explainable artificial intelligence in pathology

February 05, 2024

Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek

Explainable AI for Time Series via Virtual Inspection Layers

February 02, 2024

Florian Bley, Sebastian Lapuschkin, Wojciech Samek, Grégoire Montavon

Explaining Predictive Uncertainty by Exposing Second-Order Effects

January 30, 2024

Explainable AI| Feb 23, 2024

Call for XAI-Papers!

Two research groups associated with BIFOLD take part in the organization of the 2nd World Conference on Explainable Artificial Intelligence. Each group is hosting a special track and has already published a Call for Papers. Researchers are encouraged to submit their papers by March 5th, 2024.

Machine Learning| Nov 14, 2022

DSP Best Paper Prize

BIFOLD researchers Prof. Klaus-Robert Müller, Prof. Wojciech Samek and Prof. Grégoire Montavon were honored by the journal Digital Signal Processing (DSP) with the 2022 Best Paper Prize. The DSP mention of excellence highlights important research findings published within the last five years.

BIFOLD Update| Feb 23, 2021

2020 pattern recognition best paper award

A team of scientists from TU Berlin, Fraunhofer Heinrich Hertz Institute (HHI) and University of Oslo has jointly received the 2020 “Pattern Recognition Best Paper Award” and “Pattern Recognition Medal” of the international scientific journal Pattern Recognition. The award committee honored the publication “Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition” by Dr. Grégoire Montavon and Prof. Dr. Klaus-Robert Müller from TU Berlin, Prof. Dr. Alexander Binder from University of Oslo, as well as Dr. Wojciech Samek and Dr. Sebastian Lapuschkin from HHI.

Machine Learning| Sep 23, 2020

Using machine learning to combat the coronavirus

A joint team of researchers from TU Berlin and the University of Luxembourg is exploring why a spike protein in the SARS-CoV-2 virus is able to bind much more effectively to human cells than other coronaviruses. is funding the research with 125,000 US dollars.

BIFOLD Update| Aug 06, 2020

An overview of the current state of research in BIFOLD

Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.