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Wir, das Institute of AI in Management an der LMU München, sind begeistert von KI und den dynamischen Entwicklungen in diesem Bereich. Deshalb möchten wir Einblicke aus erster Hand in die aktuelle Forschungsarbeit von angesehenen Wissenschaftler:innen aus der ganzen Welt, geben. Wir freuen uns jedes Semester großartige Gastredner:innen für unsere Vortragsreihe gewinnen zu können.
Alle Vorträge finden online statt und sind per Zoom erreichbar für jeden, der Interesse hat. Unser Ziel ist es einen Überblick über aktuelle Trends in der KI Forschung zu geben. Die Vorträge finden immer am Donnerstag statt und bestehen aus ca. 45-60 Minuten Präsentation, gefolgt von Diskussion, Feedback und Q&A. Wir freuen uns darauf, Sie herzlich begrüßen zu dürfen.
Alle Informationen zu Terminen, Gastredner:innen und ihren Themen, inkl. Zoom Links werden im Laufe der Zeit auf dieser Veranstaltungsseite veröffentlicht.
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Die Terminserie ist eine gemeinsame Initiative mit Partnern von führenden nationalen Universitäten, unter der Leitung von Prof. Stefan Feuerriegel, LMU München:
- Prof. Anne-Sophie Mayer, LMU Munich
- Prof. Markus Weinmann, University of Cologne
- Prof. Stefan Lessmann, Humboldt University Berlin
- Prof. Mathias Kraus, Friedrich-Alexander University Erlangen-Nuremberg
- Prof. Niklas Kühl, University of Bayreuth
- Dr. Michael Vössing, Karlsruhe Institute of Technology
- Prof. Oliver Müller, University of Paderborn
- Prof. Nicolas Pröllochs, Justus-Liebig-University Gießen
- Prof. Christian Janiesch, TU Dortmund
- Prof. Gunther Gust, University of Würzburg
- Prof. Tobias Brandt, University of Münster
- Prof. Yash Raj Shrestha, University of Lausanne
- Prof. Burkhardt Funk, Leuphana University Lüneburg
- Prof. Nadja Klein, TU Dortmund
- Prof. Martin Spindler, University of Hamburg
- Prof. Niki Kilbertus, TU Munich
- Prof. Stefan Bauer, TU Munich
- Prof. Henner Gimpel, University of Hohenheim
- Prof. Alexander Benlian, TU Darmstadt
- Prof. Oliver Hinz, Goethe University, Frankfurt
- Prof. Ekaterina Jussupow, TU Darmstadt
Do. 13.11.2025
Gast: Julie Josse, French National Institute for Research in Digital Science and Technology (Inria)
Präsentation: Personalized Care Through Causal & Federated Learning: From Data to Decisions
Abstract: Generalization methods offer a powerful solution to one of the key drawbacks of randomized controlled trials (RCTs): their limited representativeness. By enabling the transport of treatment effect estimates to target populations subject to distributional shifts, these methods are increasingly recognized as the future of meta-analysis, the current gold standard in evidence-based medicine. Yet most existing approaches focus on the risk difference, overlooking the diverse range of causal measures routinely reported in clinical research. To address this gap, we propose a unified framework for transporting a broad class of first-moment population causal effect measures under covariate shift. We will then address scenarios where multiple clinical trials and real world data are available and explore how causal federated learning can be used to aggregate evidence across these sources.
Uhrzeit: 12:00 CET
Sprache: Englisch
Do. 20.11.2025
Gast: Melody Huang, Political Science and Statistics & Data Science, Yale University
Präsentation: Distilling Heterogeneous Treatment Effects: Stable Subgroup Estimation in Causal Inference
Abstract: Recent methodological developments have introduced new black-box approaches to better estimate heterogeneous treatment effects; however, these methods fall short of providing interpretable characterizations of the underlying individuals who may be most at risk or benefit most from receiving the treatment, thereby limiting their practical utility. In this work, we introduce a novel method, causal distillation trees (CDT), to estimate interpretable subgroups. CDT allows researchers to fit any machine learning model of their choice to estimate the individual-level treatment effect, and then leverages a simple, second-stage tree-based model to 'distill' the estimated treatment effect into meaningful subgroups. As a result, CDT inherits the improvements in predictive performance from black-box machine learning models while preserving the interpretability of a simple decision tree. We derive theoretical guarantees for the consistency of the estimated subgroups using CDT, and introduce stability-driven diagnostics for researchers to evaluate the quality of the estimated subgroups. We illustrate our proposed method on a randomized controlled trial of antiretroviral treatment for HIV from the AIDS Clinical Trials Group Study 175 and show that CDT out-performs state-of-the-art approaches in constructing stable, clinically relevant subgroups. This is co-authored work with Tiffany Tang and Ana Kenney.
Uhrzeit: 17:00 CET
Sprache: Englisch
Do. 19.02.2026
Gast: Emma Pierson, Electrical Engineering & Computer Science, UC Berkeley
Uhrzeit: 17:00 CET