Past Events

During 2005-2006, Arveson and Douglas formulated a challenging conjecture in multivariable operator theory regarding the essential normality of compressed shifts in the usual Hilbert spaces of analytic functions, say, Bergman spaces on strongly...

Back in the old days, before the invention of Apple Wireless Airpods, people were more familiar with the tendency of the headphones in their pockets to become knotted. It seems natural to expect that the probability that a random curve is tangled...

SFU's Association for Women in Mathematics Student Chapter, in collaboration with the Department of Mathematics EDI Advisory Group, is hosting a series of events for the International Women in Mathematics Day. The celebration begins with a Women’s...

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...

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...

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...