Past Events

Schedule Part I: Invited talks by visitors (25 min + 5 min Q) 10:30 Coffee, tea & cookies; (intro’s and tech set-up) 11:00 Carsten Beta, (U Potsdam) Composite active matter – how motile cells move passive micro-cargo 11: 30 Arik Yochelis (Ben Gurion...

Borsuk’s number b(n) is the smallest integer such that any set of diameter 1 in the n-dimensional space can be covered by b(n) sets of a smaller diameter. Exponential upper bounds on b(n) were first obtained by Shramm (1988) and later by Bourgain and...

The space of homomorphisms from the fundamental group of a compact surface to a Lie group is a remarkably rich and versatile object, playing a key role in mathematical developments spanning disciplines of algebra, analysis, geometry, and mathematical...

In this talk I will present several eigenvalue bounds on the independence number and the distance chromatic number of graph powers. We will see how to use polynomials and mixed-integer linear programming in order to optimize such bounds. Some...

The 20th Annual Alberta Mathematics Dialogue (AMD) 2023 will be held May 4- 5 (Thurs-Fri) 2023 at Mount Royal University, Calgary, Alberta. This event, formerly held jointly as the North-South Dialogue in Mathematics (which began in 2001 as a...

In 2016, Gay and Kirby showed that every compact smooth 4-manifold can be decomposed into three standard pieces. A particularly nice feature of this decomposition is that the smooth topology of the 4-manifold can be encoded by curves on a surface. In...

TBA

It is well-known that quadratic forms can be diagonalized over fields and that they are in a one-to-one correspondence with bilinear forms; the algebraic theory of quadratic forms is build on these two properties. But there is a catch -- they require...

In order to positively narrow the educational gap between the Indigenous communities and the rest of the population, there needs to be a continuous and long-term intervention for change. By leaving behind the philosophy of reduced expectations...

Remarkable recent successes of deep learning have heavily relied on very large neural networks. Time and time again, larger networks have shown better performance than smaller networks trained on comparably sized data sets. Since large networks...