Zurich colloquium in mathematics

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20. September 2022
16:30-18:15

Prof. Dr. Jean-Pierre Serre
Collège de France

Details

Titel	Cohomological invariants in topology and algebra
Referent:in, Affiliation	Prof. Dr. Jean-Pierre Serre, Collège de France
Datum, Zeit	20. September 2022, 16:30-18:15
Ort	KO2 F 150

Cohomological invariants in topology and algebra

1. November 2022
16:30-18:15

Prof. Dr. Thomas Nikolaus
Universität Münster

Details

Titel	Algebraic K-Theory and higher algebra
Referent:in, Affiliation	Prof. Dr. Thomas Nikolaus, Universität Münster
Datum, Zeit	1. November 2022, 16:30-18:15
Ort	KO2 F 150
Abstract	This talk is about algebraic K-theory groups (defined by Quillen in the early 1970s). We will review the definition and motivation behind those groups and explain some applications. Then we try to summarise what is known in terms of computations and explain some recent breakthroughs (based on so-called trace methods). One of the central tools used to achieve this progress is`higher categorical algebra' in the sense of Waldhausen, Lurie and others. As an sample application we cover the recent results on the K-theory of Z/p^n obtained in joint work with Anteau and Krause.

Algebraic K-Theory and higher algebraread_more

22. November 2022
16:30-18:15

Prof. Dr. Michael Bronstein
University of Oxford

Details

Titel	Physics-inspired learning on graphs
Referent:in, Affiliation	Prof. Dr. Michael Bronstein, University of Oxford
Datum, Zeit	22. November 2022, 16:30-18:15
Ort	KO2 F 150
Abstract	Abstract: The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs. We argue that the very “node-and-edge”-centric mindset of current graph deep learning schemes may hinder future progress in the field. As an alternative, we propose physics-inspired “continuous” learning models that open up a new trove of tools from the fields of differential geometry, algebraic topology, and differential equations so far largely unexplored in graph ML.

Physics-inspired learning on graphsread_more

13. Dezember 2022
15:15-17:00

Prof. Dr. Julia Wolf
University of Cambridge

Details

Titel	When is a mathematical object well-behaved?
Referent:in, Affiliation	Prof. Dr. Julia Wolf, University of Cambridge
Datum, Zeit	13. Dezember 2022, 15:15-17:00
Ort	KOL G 217

When is a mathematical object well-behaved?

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