Mathematisches Kolloquium 

Vorträge am Mittwoch

Einblicke in die Wunder und Schönheit der Mathematik

Das Mathematische Kolloquium findet an ausgewählten Terminen in jedem Semester statt (Mittwoch, 16.15 Uhr, Hörsaal E29).

Es gibt jeweils (um 15:00 Uhr - im Seminarraum E19) eine Einführung in das jeweilige Thema (insbesondere für Studierende etc.). 

Zudem ist (ab 15.30 Uhr - ebenfalls im Seminarraum E19) ein Institutstee vorgesehen.

Die Vorträge sollen jeweils für ein breites Publikum in der Fakultät interessant sein.

 

Wintersemester 2025/2026

Mittwoch, 29. Oktober 2025, 16.15 Uhr

 

Enrique Zuazua (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Enrique Zuazua is the Chair for Dynamics, Control, Machine Learning and Numerics - Alexander von Humboldt Professorship, at the Department of Mathematics of the Friedrich-Alexander-Universität Erlangen-Nürnberg. He is also Chief Algorithm Scientist at Sherpa.ai, a member of the Basque Academy "Jakiunde", and of the Academia of Europaea.
He holds a degree in Mathematics (1984) from the University of the Basque Country, and a dual Ph.D. degree from the same university (1987) and the Université Pierre et Marie Curie, Paris (1988). In 1990 he became Professor of Applied Mathematics at the Universidad Complutense de Madrid, to later move to the Autonomous University of Madrid (UAM) in 2001. He was awarded three Advanced Grants of the European Research in 2010, 2016 and 2022.
He has published over 300 articles, supervised over 30 PhD students and a broad network of master students, post-doctoral researchers and research and management technicians. His fields of expertise in the broad area of Applied Mathematics include Partial Differential Equations, Systems Control, Numerical Analysis and Machine Learning.

 

Machine Learning: A Mathematician’s Perspective

Abstract: Machine Learning has emerged as one of the most transformative forces in science and technology. Beneath its powerful algorithms lie mathematical foundations deeply rooted in classical disciplines such as Applied Mathematics and Systems Control. This lecture adopts a mathematician’s perspective to examine why Machine Learning works so effectively and how its data-driven paradigms can be rigorously integrated into traditional analytical frameworks.
We will revisit the historical and conceptual links between Machine Learning and Systems Control—also known as Cybernetics—a field shaped by the pioneering ideas of Ampère and Wiener. Their parallel evolution reveals a deep mathematical unity and highlights the power of mathematics to model complex systems and drive innovation.
This dual perspective is mutually enriching. On one hand, Machine Learning raises fundamental mathematical questions that challenge and inspire the mathematical community. On the other, it offers opportunities to expand the scope of classical applied mathematics by developing hybrid methodologies that integrate data-driven insights.
The lecture will conclude by outlining promising directions for future research at the intersection of Machine Learning, Applied Mathematics, and Control Theory.

 

Mittwoch, 26. November 2025, 16.15 Uhr

Scott Armstrong (Sorbonne Université, Paris)

Scott Armstrong is a mathematician specializing in partial differential equations and probability. His research focuses on the behavior of solutions of PDEs with random or rapidly oscillating coefficients. Scott Armstrong is currently a CNRS research director at the Laboratoire Jacques-Louis Lions at Sorbonne Université in Paris. He previously held faculty positions at the University of Wisconsin–Madison, Université Paris Dauphine and NYU's Courant Institute. He completed his Ph.D. at UC Berkeley in 2009 under Lawrence C. Evans. He recently received an ERC Advanced Grant for his project ReGroStaFT, focused on applications of stochastic homogenization methods to problems in mathematical physics. He has authored over 50 research papers and collaborates widely in Europe and North America.

 

Coarse-graining, renormalization, and anomalous diffusion

Abstract: I will discuss the large-scale/ long-time behavior of a Brownian particle in a random, incompressible (divergence-free) vector field. If this vector field has correlations which decay sufficiently slowly, then the behavior of the particle is superdiffusive instead of diffusive. In particular, after time $t$, the particle will typically be a much larger distance than $\sqrt{t}$ from the origin. This phenomenon was explained in the physics literature in the late 1980s, using heuristic renormalization group arguments, as a divergence of effective diffusivity as we zoom out to large length scales. I will discuss recent mathematical innovations based on some analytic/PDE methods that allow us to make some of these renormalization group arguments rigorous.
(This is joint work with A. Bou-Rabee and T. Kuusi.)

 

Mittwoch, 04. Februar 2026, 16.15 Uhr

Nina Gantert (Technische Universität München)

Nina Gantert is a professor of mathematics at the Technical University of Munich (TUM), specializing in probability theory. Her research focuses on stochastic processes, large deviations, and random media, with particular interest in random walks in random environments as models for transport in disordered systems. She also explores applications of probability in physics and biology.
After studying at ETH Zürich, she received her PhD from the University of Bonn in 1991. Following postdoctoral positions in Haifa and Paris, she completed her habilitation at TU Berlin in 2000. Before joining TUM in 2011, she held faculty positions at the Karlsruhe Institute of Technology and the University of Münster.
In 2016, she was elected Fellow of the Institute of Mathematical Statistics (IMS).

Consensus and disagreement in opinion dynamics

Abstract: We discuss several models of opinon dynamics, where agents tend to align their opinions with their neighbours when they interact. We start with the classical voter model, where opinions can have only two values. Then we explain the Deffuant model and present several open conjectures. We give some results and conjectures about the compass model which is a variant of the Deffuant model where opinions take values on the unit circle. Finally we discuss the averaging process on infinite graphs.
The results were obtained in collaboration with Markus Heydenreich and Timo Vilkas.

Vorschau

Sommersemester 2026

Mittwoch, 22. April 2026, 16.15 Uhr

tba

 

Mittwoch, 10. Juni 2026, 16.15 Uhr

Gitta Kutyniok (Ludwig-Maximilians-Universität München)

Gitta Kutyniok ist Inhaberin des Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence an der LMU München. Sie studierte und promovierte an der Universität Paderborn, habilitierte sich in Gießen, worauf eine Karrierephase an US-amerikanischen Universitäten, u.a. Princeton University, Stanford University und Yale University folgte, bevor sie Professuren in Osnabrück und an der TU Berlin innehatte. Gitta Kutyniok erhielt zahlreiche Preise und war eingeladene Sprecherin auf dem Europäischen Mathematikerkongress 2021, dem Internationaler Mathematikerkongress 2022 und dem International Congress on Industrial and Applied Mathematics 2023. Sie ist Mitglied der Berlin-Brandenburgischen Akademie der Wissenschaften und der Europäischen Akademie der Wissenschaften und Künste, sowie Fellow der SIAM und des IEEE und seit kurzem Vizepräsidentin für Wissenschafts- und Forschungskooperationen des Bundesverbandes KI-Transformation.

Titel / Abstract: tba

 

Mittwoch, 15. Juli 2026, 16.15 Uhr

tba

 

Bisheriges Programm

Sommersemester 2025

Poster Mathematisches Kolloquium (pdf)

 

Mittwoch, 21. Mai 2025, 16.15 Uhr

Karl-Theodor Sturm (Rheinische Friedrich-Wilhelms-Universität Bonn)

Karl-Theodor Sturm obtained his PhD 1989 from the University of Erlangen-Nuremberg under the supervision of Heinz Bauer. Visiting and research positions led him to the universities of Stanford, Zurich, and Bonn as well as to the MPI Leipzig. In 1994, he was awarded a Heisenberg fellowship of the DFG. Since 1997, he is a professor for mathematics at the University of Bonn. He is member of the Academia Europaea since 2022. From 2012 – 2019 he was the Spokesperson of the Excellence Cluster Hausdorff Center for Mathematics, and since 2024 he now serves as the Director of the Hausdorff Research Institute for Mathematics. The focus of his research is in Stochastic and Geometric Analysis. He gained particular attention with his work on “Analysis on local Dirichlet spaces” as well as with his pioneering work on synthetic Ricci bounds for metric measure spaces.In 2016, he was awarded an ERC Advanced Grant for his research project “Metric measure spaces and Ricci curvature – analytic, geometric, and probabilistic challenges”.

 

Optimal transport and synthetic geometry — analytic, geometric and stochastic aspects

Abstract: Metric measure spaces with synthetic Ricci bounds have attracted great interest in recent years, accompanied by spectacular breakthroughs and deep new insights. In this survey, I will provide a brief introduction to the concept of lower Ricci bounds as introduced by Lott–Villani and myself, and illustrate some of its geometric, analytic, and probabilistic consequences, among them Li–Yau estimates, coupling properties for Brownian motions, sharp functional and isoperimetric inequalities, rigidity results, and structural properties like rectifiability. In particular, I will explain its crucial interplay with the heat flow and its link to the curvature-dimension condition formulated in functional-analytic terms by Bakry–Émery. This equivalence between the Lagrangian and the Eulerian approach then will be further explored in various recent research directions.

 

Mittwoch, 11. Juni 2025, 16.15 Uhr

Bernd Sturmfels (Max-Planck-Institut für Mathematik in den Naturwissenschaften (MPI MiS), Leipzig)

Bernd Sturmfels ist Direktor am Max-Planck-Institut für Mathematik in den Naturwissenschaften in Leipzig. Zuvor war er Professor für Mathematik, Statistik und Computerwissenschaften an der UC Berkeley. Bernd Sturmfels betreute über 65 Doktorand*innen und eine Vielzahl von Postdocs. Er ist Autor von zwölf Büchern und über 300 Forschungsartikeln, in Kombinatorik, kommutativer Algebra, algebraischer Geometrie und deren Anwendungen in Bereichen wie Statistik, Optimierung, Computerbiologie und Grundlagenphysik.

 

Gram Matrices for Isotropic Vectors

Abstract: We discuss the algebraic geometry of low rank symmetric matrices that have zero blocks along the main diagonal. In theoretical physics, these arise as Gram matrices for kinematic variables in quantum field theories.

"Vorprogramm" im Seminarraum E19: 15:00 Uhr: Einführung in das Thema (insbesondere für Studierende) / 15:30 Uhr: Institutstee

 

Wintersemester 2024/2025

Mittwoch, 30. Oktober 2024, 16.15 Uhr, Hörsaal E29
Angkana Rüland (Rheinische Friedrich-Wilhelms-Universität Bonn)

Angkana Rüland absolvierte ihren Master sowie ihren PhD (bei Herbert Koch) an der Universität Bonn. Nach zwei Jahren als Postdoc an der Universität Oxford wechselte sie zurück nach Deutschland als unabhängige Nachwuchsgruppenleiterin am Max-Planck-Institut für Mathematik in Leipzig, bevor sie Professorin an der Universität Heidelberg wurde. Seit 2023 hat sie einen Hausdorff Chair am Hausdorff Center for Mathematics an der Rheinischen Friedrich-Wilhelms-Universität Bonn inne. Angkana Rüland wurde 2023 mit dem Calderón Prize ausgezeichnet und in 2024 mit New Horizons in Mathematics Prize.

Nachtrag: Angkana Rüland ist eine der Gottfried-Wilhelm-Leibniz-Preisträger*innen des Jahre 2025.

Scaling Laws for Shape-Memory Alloys: Between Rigidity and Flexibility

Abstract: Shape-memory alloys are materials which display a striking thermodynamic behaviour. They undergo a solid-solid phase transition in which symmetry is reduced upon the passage from the high to the low temperature phase. This gives rise to a "memory"' for these materials and to fascinating microstructures determined by a rich energy landscape. In this talk we will approach these structures through the calculus of variations and scaling laws. This allows us to thus rigorously capture some of the complexity of the underlying microstructures. A particular emphasis will be on the analysis of a striking dichotomy between extreme rigidity and flexibility in the mathematical description of these materials.

 

Mittwoch, 04. Dezember 2024, 16.15 Uhr, Hörsaal E29
Achtung: Der Vortrag muss krankheitsbedingt leider verschoben werden. Ein neuer Termin wird rechtzeitig bekanntgegeben.

Jean-Christophe Mourrat (École normale supérieure de Lyon)

Jean-Christophe Mourrat absolvierte sein Studium an der École normale supérieure in Paris und an der Universität Paris-Sud, bevor er an der Universität Aix-Marseille und der Universidad Catolica de Chile promovierte. Nach drei Jahren als Postdoc an der EPFL Lausanne erhielt er eine CNRS-Stelle. Von 2019 bis 2021 war er Professor am Courant Institut (New York University), bevor er als CNRS-Forschungsdirektor nach Lyon zurückkehrte. Er ist Träger des Rollo-Davidson-Preises und des Marc-YOR-Preises.

 

Spin glasses with multiple types

Abstract: Spin glasses are models of statistical mechanics in which a large number of elementary units, called spins, interact in a disordered manner. They can serve as simplified models for a broad class of problems encountered in theoretical computer science, combinatorics, or high-dimensional statistics. In the simplest case, called the SK model, there are direct interactions between all pairs of spins. The first part of the talk will present the main ideas underpinning the analysis of this model. However, some models that are very similar remain less well-understood. For instance, one can consider a bipartite version of the SK model, in which the spins are divided into two groups, and direct interactions are only present between spins of different groups. I will highlight some of the challenges posed by models of this type, and discuss some partial progress.

 

 

Mittwoch, 15. Januar 2025, 16.15 Uhr, Hörsaal E29 ("Vorprogramm" ab 15.00 Uhr im E19)

László Erdős (Institute of Science and Technology Austria (ISTA))

László Erdős studierte an der Loránd-Eötvös-Universität und promovierte an der Princeton University. Nach Postdoc-Phasen an der ETH Zürich und am Courant Institute in New York wurde er Professor an der Georgia Tech (USA). Im Jahr 2003 wechselte er an die Ludwig-Maximilians-Universität München. Seit 2013 ist er Professor am Institute of Science and Technology Austria. Er war eingeladener Redner auf dem Internationalen Mathematiker-Kongress, ist Empfänger eines ERC Advanced Grant und zahlreicher Preise wie des Erwin-Schrödinger-Preises und des Leonard Eisenbud Preis.

 

Universality phenomenon for random matrices

Abstract: Large random matrices tend to exhibit universal fluctuations. Beyond the well-known Wigner-Dyson and Tracy-Widom eigenvalue distributions, we overview other universality results for Hermitian and non-Hermitian matrices. We discuss the emergence of normal distribution involving eigenvectors, especially the random matrix version of quantum unique ergodicity. We also explain why results on non-Hermitian random matrices are much harder than their Hermitian counterparts and highlight our new methods to tackle them.

Ablauf:
15.00 Uhr im Raum E19: Einleitung in das Thema für Studierende / 15.30 Uhr im Raum E19: Institutstee / 16.15 Uhr im Hörsaal E29: Vortrag

 

Kolloquien und Oberseminare

Weitere Vorträge finden z.B. in Oberseminaren in den verschiedenen Arbeitsgruppen statt.

Oberseminare