AI Keynote Series

We, the Institute of AI in Management at LMU Munich, are excited about AI in management and the dynamic developments in this field. That is why we would like to provide first-hand insights into the latest research work, granted by high profile scientists from all over the world. We are very honoured to be able to win great guest speakers for the keynotes every semester.

All session will be available via Zoom for everyone who is interested. We aim to provide an overview of current trends in AI research. The sessions, on Thursdays, consist of 45-60 minutes of presentation, followed by discussion, feedback and QA. We are looking forward to seeing you there.

All information on dates, times, speakers and their topics, incl. Zoom links will be published on this event page when we get closer to the dates.

You are invited to sign up for our newsletter, through which we communicate all upcoming events in this series. Please follow the link to our registration page.

The series is a joint initiative, led by LMU Munich (Prof. Stefan Feuerriegel) together with co-hosts from leading national universities:

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

Thursday 13.11.2025

Speaker: Julie Josse, French National Institute for Research in Digital Science and Technology (Inria)

Topic: 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.

Time: 12:00 CET

Thursday, 20.11.2025

Speaker: Melody Huang, Political Science and Statistics & Data Science, Yale University

Topic: 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.

Time: 17:00 CET

Thursday 08.01.2026

Speaker: Murat Kocaoglu, Department of Computer Science, Johns Hopkins University

Topic: Causal Inference with Deep Generative Models

Abstract: Causal knowledge is central to solving complex decision-making problems in many fields, from engineering and medicine to cyber-physical systems. Causal inference has also recently been identified as a key capability to remedy some of the issues modern machine learning systems suffer from, such as explainability and generalization. In this talk, we first provide a short introduction to the principles of causal modeling. Next, we discuss how deep neural networks can be used to obtain a causal representation to solve complex high-dimensional and distributed causal inference problems using deep generative models. Finally, we show how the proposed methods can be applied for causal invariant prediction and to evaluate black-box conditional generative models.

Bio: Murat Kocaoglu received his B.S. degree in Electrical - Electronics Engineering with a minor degree in Physics from the Middle East Technical University in 2010, and M.S. degree from the Koc University, Turkey in 2012 and Ph.D. degree from The University of Texas at Austin in 2018. He was a Research Staff Member in the MIT-IBM Watson AI Lab in IBM Research, Cambridge, Massachusetts from 2018 to 2020, and an assistant professor at Purdue University in the School of Electrical and Computer Engineering from 2021 to 2025. He is currently an assistant professor in the Department of Computer Science at Johns Hopkins University, where he leads the CausalML Lab. He received Adobe Data Science Research Award in 2022, NSF CAREER Award in 2023, Amazon Research Award in 2024. His current research interests include causal inference, deep causal generative models, and information theory.

Time: 17:00 CET

Zoom Link

Thursday 12.02.2026

Speaker: Ruoxuan Xiong, Data & Decision Sciences, Emory University

Time: 17:00 CET

Zoom Link

Thursday 19.02.2026

Speaker: Emma Pierson, Electrical Engineering & Computer Science, UC Berkeley

Präsentation: Using New Data to Answer Old Questions

Abstract: The explosion of new data sources has created new opportunities, and necessitated new machine learning methods, to answer old questions in the health and social sciences. This talk discusses three stories under this theme: first, using image data to quantify inequality in policing; second, using text data to interpretably predict target variables and characterize disparities; and third, using address data to infer fine-grained migration patterns.

Time: 17:00 CET

Zoom Link