Exploring the Frontiers of AI-driven Research

Machine learning models have demonstrated a vast capacity to learn complex phenomena from data and to reproduce them accurately, for example by providing precise predictions of molecular properties or by generating human language in natural conversations. However, due to their black-box nature, such models often resemble oracles with vast stores of knowledge rather than scientific tools capable of reporting or communicating the reasoning underlying their predictions. This limitation constrains their potential impact in scientific applications, where understanding why a model arrives at a prediction can be as important as the prediction itself and can substantially accelerate scientific discovery and understanding.

In this workshop, we seek to develop a broader and more systematic understanding of how the explanation and interpretation of artificial intelligence models can enable AI-driven research from both theoretical and applied perspectives. In particular, we aim to explore how AI-driven research can be enriched beyond model validation and trust assessment. The workshop will include invited talks, poster sessions, and dedicated time for discussion, fostering informal exchange across disciplines such as molecular science, medicine, digital humanities, geoscience, and related fields.

Schedule

Confirmed Speaker

The workshop is currently in the planning phase. So far, we are pleased to announce that the following renowned speakers have confirmed their participation. 
The speaker list will be continuously updated in the coming days.

• Dr. Bruno Andreis, University of Oxford, Department of Engineering Science
• Prof. Przemysław Biecek, Warsaw University of Technology, Department of CAD/CAM Systems Design and Computer-Aided Medicine
• Dr. Jochen Büttner, Max Planck Institute for Geoanthropology Jena
• Prof. Dr. Gustau Camps-Valls, Universitat de València, Image and Signal Processing
• Dr. Maximilian Dax, Research group leader at ELLIS Institute and MPI in Tübingen
• Prof. Dr. Begüm Demir, BIFOLD/TU Berlin, Remote Sensing Image Analysis
• Prof. Dr. Martin Hebart, Professor of Computational Cognitive Neuroscience, Justus Liebig University Giessen / Max Planck Institute for Human Cognitive and Brain Sciences Leipzig
• Dr. Anna Hedström, postdoctoral fellow at the ETH AI Center of ETH Zürich
• Kevin Höllring, PhD, postdoctoral researcher, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Department of Chemistry and Pharmacy
• Lorenz Hufe, University of Oxford, Fraunhofer HHI
• Prof. Dr. Dagmar Kainmüller, Max-Delbrück-Centrum für Molekulare Medizin (MDC), Biomedical Image Analysis
• Prof. Dr. Frederick Klauschen, Ludwig-Maximilians-Universität Munich, Director and Chair, Pathology & Dean of Research 
• Univ.-Prof. Dipl.-Ing. Dr.techn. Dominik Kowald, Know Center Research GmbH and University of Graz, Professor for AI-based Information Retrieval in Digital Humanities 
• Dr. rer. nat. Sebastian Lapuschkin, Head of Explainable Artificial Intelligence Group, Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institute
• Prof. Dr. Anatole von Lilienfeld, University of Toronto, Professor & Clark Chair of Advanced Materials at the Vector Institute
• Dr. med. Philipp Keyl, Junior Group Leader, Institute of Pathology, LMU Munich
• Prof. Dr. Marina Meila, Cheriton School of Computer Science, University of Waterloo and Canada CIFAR Chair in AI, Vector Institute
• Prof. Dr. Carolin Müller, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Department of Chemistry and Pharmacy
• Prof. Dr. Tomaso Poggio, Massachusetts Institute of Technology, CSAIL, McDermott Professor in Brain Sciences
• Prof. Iyad Rahwan, Director of the Max Planck Institute for Human Development
• Prof. Dr. Katharina Rohlfing, Paderborn University, AG Psycholinguistik
• Prof. Dr. Alexandre Tkatchenko, University of Luxembourg, Professor in Theoretical Condensed Matter Physics
• Prof. Dr. Carlos Zednik, Eindhoven University of Technology, Industrial Engineering and Innovation Science