We are pleased to invite you to an upcoming Munich AI Lecture that opens space for fundamental questions at the intersection of artificial intelligence and mathematics. How Machines Explore, Conjecture, and Discover Mathematics examines how AI systems are increasingly becoming active contributors to the scientific discovery process.

This edition of the Munich AI Lectures Series is held in collaboration with Ludwig Maximilian University of Munich, in particular the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence, and highlights recent advances in AI-driven approaches to mathematical research.

The lecture presents methods from the AI4Math initiative that combine optimization, machine learning, and mathematical structure to navigate highly complex search spaces. Using the Hadwiger–Nelson problem as a central example, the talk illustrates how neural networks can transform mixed discrete–continuous problems into differentiable optimization tasks, enabling the exploration of new and previously inaccessible solution spaces.

Date & Time
Thursday, February 12, 2026
05:00–06:30 PM (Europe/Berlin)

Location
Munich AI Lecture
Geschwister Scholl Platz 1, Room D209
80539 Munich, Germany

About the speaker

Sebastian Pokutta is Vice President of the Zuse Institute Berlin and Professor of Mathematics at TU Berlin, with a research focus on artificial intelligence and optimization. He leads major research initiatives including the Excellence Cluster MATH+ and the Research Campus MODAL. His academic and industry experience includes positions 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.

More information about the Munich AI Lectures Series can be found here

We look forward to welcoming you to this Munich AI Lecture.