17

Dec

Teaser image to Part 1: Dynamics of Strategic Agents and Algorithms as PDEs

Munich AI Lectures

Part 1: Dynamics of Strategic Agents and Algorithms as PDEs

Franca Hoffmann, California Institute of Technology

   17.12.2024

   4:00 pm - 5:30 pm

   Senatssaal, LMU Munich, Geschwister-Scholl-Platz 1, Munich

We are thrilled to invite you to the upcoming Munich AI Lecture featuring two distinguished researchers Prof. Holger Hoos from RWTH Aachen University and Prof. Franca Hoffmann from California Institute of Technology. The lecture is organized by the Chair of Mathematics of Information Processing with support by MCML.

In the first talk, Franca Hoffmann will explore how to understand and predict the complex dynamics that emerge when algorithms interact with strategic populations. She will present a game-theoretic framework for infinite-dimensional games, modeled through coupled partial differential equations, to capture these interactions.

About Franca Hoffmann

Franca Hoffmann obtained her master’s in mathematics from Imperial College London (UK) and holds a PhD from the Cambridge Centre for Analysis at University of Cambridge (UK). She held the position of von Kármán instructor at Caltech from 2017 to 2020, then joined University of Bonn (Germany) as Bonn Junior Professor and Quantum Leap Africa in Kigali, Rwanda (African Institute for Mathematical Sciences) as AIMS-Carnegie ResearchChair in Data Science, before arriving at the California Institute of Technology as Assistant Professor in 2022.

Organized by:

Bavarian AI Agency


Related

Link to Practical Causal Reasoning as a Means for Ethical ML

Colloquium  •  25.06.2025  •  LMU Department of Statistics and via zoom

Practical Causal Reasoning as a Means for Ethical ML

25.06.25, 4:15-5:45 pm: Isabel Valera, Uni Saarbrücken explores fairness in ML and introduces DeCaFlow, a causal model for counterfactuals.


Link to Veridical Data Science and PCS Uncertainty
Quantification

Colloquium  •  11.06.2025  •  LMU Department of Statistics and via zoom

Veridical Data Science and PCS Uncertainty Quantification

11.06.25, 4:15-5:45 pm: Bin Yu, UC Berkeley on how PCS improves AI reliability by tackling hidden uncertainty in data science decisions.