DIAMANT Symposium Autumn 2026

by | Mar 24, 2026

Event Details

The Diamant symposium of Fall 2026 will take place on Thursday 19 and Friday 20 November in De Werelt (Lunteren).

Confirmed invited speakers are Reinis Cirpons (INRIA Nantes), Jan Goedgebeur (KU Leuven), Vivian Kuperberg (IST Austria), and Gaurav Rattan (University of Twente).

How to contribute a talk

PhD students and postdocs are warmly welcomed to submit a contributed talk (15-20 mins.) to the symposium. In the registration form (see below) there is an option to submit title and abstract. Alternatively, after registration, a title and abstract can be sent at a later date. Please register your talk no later than October 16, 2026.

Speakers and abstracts

INVISIBLE

Reinis Cirpon (INRIA Nantes)

How to build a mathematical database

A mathematical database is a systematic collection of data about
mathematical objects. Well known examples include the ATLAS of finite groups
and the On-Line Encyclopedia of Integer sequences. Some of the earliest
examples of such databases include tables of trigonometric functions,
such as Ptolemy’s table of chords tabulated during the 2nd century AD,
and have been vital for the advancement of mathematics. Though the basic
concept of mathematical databases is old, they are no less relevant in the
modern day, as they enable efficient counterexample search, and can help
with the formulation of meaningful conjectures.

Recently, together with coauthors, we built a database of decidability results
for one-relation monoids, building on the earlier work of Pedersen in 1989.
Roughly speaking, the word problem asks whether two words can be made equal,
with respect to a given set of rules for manipulating them. For one-relation
monoids, only a single rule allowing for the exchange of two fixed subwords is
given. It is a longstanding open problem whether every one-relation monoid has
a decidable word problem. In contrast, the analogous problem for one-relator
groups is known to be decidable by a result of Magnus in 1930.

In this talk we will go over some recent examples of mathematical databases,
discuss their utility for theory-building and talk about the design decisions
we made when creating our own database and verifying its correctness via
mathematical formalization.

Jan Goedgebeur (KU Leuven)

An introduction to computational graph theory and generation algorithms

Computers are often used in combinatorics to determine if combinatorial objects with given structural or extremal properties exist as these existence problems are often too complex to solve by hand. This is done by designing and implementing generation algorithms which construct combinatorial objects from a given class (typically avoiding the generation of isomorphic copies) and analysing the resulting objects.

In this talk we will give an introduction to computational graph theory and the design of generation algorithms in particular. We will also give concrete examples of how these generation algorithms have helped to gain new insights and solve problems in mathematics and in chemistry.

Vivian Kuperberg (IST Austria)

Counting irreducible polynomials in sectors and linear algebra
In this talk I’ll focus on a counting technique available only in the polynomial setting: namely, using analogs of the Chebotarev density theorem to count irreducible polynomials in a parametrized family. This technique seems to be unique to the polynomial setting, where specializing to a prime is effectively the same as choosing an element of the family. I will give an overview of the technique and talk about applying it to a new problem where the Galois-theoretic computations require substantial input from algebraic combinatorics.
 
Joint with A. Fehm and E. Waxman.

Gaurav Rattan (University of Twente)

Can Neural Networks Understand Graphs?
Modern deep learning methods have been remarkably successful at learning from text and image data. Graphs, however, present a peculiar challenge: There is no canonical ordering of their vertices, whereas the learnable information is hidden in the combinatorial structure. The quest for powerful graph learning methods has spawned intense activity over the last decade, right at the interface of discrete mathematics, graph algorithms and machine learning.
 
In this talk, we will describe how a wide variety of classical discrete mathematical tools, ranging from graph invariants and mathematical logic to graph homomorphisms, can guide the design and analysis of powerful graph learning methods. Our starting point will be the fundamental Graph Isomorphism problem: When can two graphs be efficiently distinguished? We present our results showing how the Weisfeiler–Leman algorithm, a simple message-passing procedure for distinguishing graphs, yields a theoretical framework for delineating the learning capacity of graph neural networks.

Sign up for this event

Registration has opened. Please fill in this form to register. The registration deadline is October 31, 2026.

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