How Machines Explore, Conjecture, and Discover Mathematics – 12.02.2026

With the latest edition of the Munich AI Lectures a space is once again opening up for fundamental questions at the intersection of artificial intelligence and science. Together with the Ludwig Maximilian University of Munich, especially the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence, a lecture is being held to show how AI is increasingly becoming part of the scientific discovery process.

The focus of the lecture is on the role of AI as a partner in mathematical research. Approaches from the AI4Math initiative are presented, combining optimization, machine learning, and mathematical structure to unlock highly complex search spaces. Using the Hadwiger-Nelson problem as an example, it is explained how neural networks can be used to transform mixed discrete-continuous issues into differentiable optimization problems and explore new solution spaces.

To the presenter: Prof. Dr. Sebastian Pokutta is Vice President of the Zuse Institute Berlin (ZIB) and a Professor of Mathematics at the TU Berlin, with a research focus on Artificial Intelligence and Optimization. He leads, among other things, the Excellence Cluster MATH+ and the Research Campus MODAL and has previously worked in academia and industry, including at MIT, IBM ILOG, and Georgia Tech. His work has been recognized with numerous awards, including the Gödel Prize, the STOC Test of Time Award, and the NSF CAREER Award.

Here you can find the LMU room finder: https://www.lmu.de/raumfinder/#/

• Organizer: Ludwig Maximilian University of Munich, Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence

• Event language: English

• Target group: Researchers, students, scientists from mathematics, computer science, and artificial intelligence, as well as other AI enthusiasts