Past Events

In 2004 Rosas and Sagan asked whether there was a way to define a basis in the algebra of symmetric functions in noncommuting variables, NCSym, having properties analogous to the classical Schur functions, in the algebra of symmetric functions, Sym...

Cancer is a complex disease with many unknown features. The evolution of tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. To overcome some of the outstanding...

In the finite-dimensional situation, Lie's third theorem provides a correspondence between Lie groups and Lie algebras. Going from the latter to the former is the more complicated construction, requiring a suitable representation, and taking...

A number of results in symbolic dynamics, principally Krieger’s embedding theorem and Boyle’s lower-entropy factor theorem, give conditions for the existence of an injective or surjecive sliding block code from one subshift to another, in terms of...

A Landau-Ginzburg (LG) model is a triplet of data (X, W, G) consisting of a regular function $W:X\to\mathbb{C}$ from a quasi-projective variety $X$ with a group $G$ acting on $X$ leaving $W$ invariant. One can build an analogue of Hodge theory and...

Given a finite simple graph $G$, an {\em odd cover of $G$} is a collection of complete bipartite graphs, or bicliques, in which each edge of $G$ appears in an odd number of bicliques and each non-edge of $G$ appears in an even number of bicliques. We...

The last 10-15 years have seen several works aimed at understanding the behaviour of scale-invariant norms near a potential blow-up time/finite maximal time of existence for a variety of PDE. Most of these results and proofs are qualitative and...

In this talk, we will first review the concept of inverse-closedness for a pair of algebras and its connection with an important property of groups known as being Hermitian (or symmetric). This property appears when one considers inverse-closedness...

We consider the problem of learning an unknown function f from given data about f. The learning problem is to give an approximation fˆ to f that predicts the values of f away from the data. There are numerous settings for this learning problem...