Invited Speakers
We are excited to announce the Invited Speakers for ACM EAAMO ‘26. Both keynotes take place at the main building of LMU Munich; see the program at a glance for the full schedule.
Moritz Hardt #
Max Planck Institute for Intelligent Systems, Tübingen, Germany
Keynote 1: Thursday, November 5, 17:00–18:00
Biography: Moritz Hardt is a director at the Max Planck Institute for Intelligent Systems in Tübingen, where he leads the Social Foundations of Computation group, and an honorary professor at the Tübingen AI Center and the University of Tübingen. Prior to joining the institute, he was Associate Professor for Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research contributes to the scientific foundations of machine learning and algorithmic decision making from a social perspective. He is a co-author of the textbooks Fairness and Machine Learning: Limitations and Opportunities (MIT Press) and Patterns, Predictions, and Actions: Foundations of Machine Learning (Princeton University Press).
Mohsen Bayati #
Stanford Graduate School of Business, USA
Keynote 2: Friday, November 6, 16:30–17:30
From Automating Expertise to Scaling Oversight #
Abstract: As AI agents capably complete tasks that used to require substantial human expertise, a new challenge emerges: determining when human oversight remains necessary. We begin with a clinical prediction problem where extracting meaningful information from unstructured medical notes requires extensive expert effort. We show how multi-agent language models can automate this process while achieving comparable predictive performance at a fraction of the human effort. We then turn to a broader question: when should autonomous agents act independently, and when should they seek human intervention? Building on game-theoretic and statistical frameworks, we explore how agents and overseers can coordinate their decisions. We also discuss how effective oversight can be maintained as AI capabilities continue to advance.
Biography: Mohsen Bayati is the Carl and Marilynn Thoma Professor of Operations, Information and Technology at the Stanford Graduate School of Business. He works on the mathematics of deploying AI in high-stakes settings: alignment and human oversight, experiment design under interference, and sequential decision-making. His research has appeared in Nature Medicine, PNAS, Management Science, Operations Research and NeurIPS, and has been recognized with the INFORMS Pierskalla and Applied Probability Society best paper awards and an NSF CAREER award.