Dr. Grégoire Montavon is a Guest Professor at the Freie Universität Berlin and a Senior Research 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.

  • Explainable AI
  • Neural Networks
  • Unsupervised Learning