Let w(x_1,...,x_r) be a word in a free group. For any group G, w induces a word map w:G^r-->G. For example, the commutator word w=xyx^(-1)y^(-1) induces the commutator map. If G is finite, one can ask what is the probability that w(g_1,...,g_r)=e, for a random tuple (g_1,...,g_r) of elements in G.

In the setting of finite simple groups, Larsen and Shalev showed there exists epsilon(w)>0 (depending only on w), such that the probability that w(g_1,...,g_r)=e is smaller than |G|^(-epsilon(w)), whenever G is large enough (depending on w).

In this talk, I will discuss analogous questions for compact groups, with a focus on the family of unitary groups; For example, given r independent Haar-random n by n unitary matrices A_1,...,A_r, what is the probability that w(A_1,...,A_r) is contained in a small ball around the identity matrix?

Based on a joint work with Nir Avni and Michael Larsen.  

The two-colored Temperley-Lieb algebra is a generalization of the Temperley-Lieb algebra. The analogous two-colored Jones-Wenzl projector plays an important role in the Elias-Williamson construction of the diagrammatic Hecke category. In this talk, I will give conditions for the existence and rotatability of the two-colored Jones-Wenzl projector in terms of the invertibility and vanishing of certain two-colored quantum binomial coefficients. As a consequence, we prove that Abe’s category of Soergel bimodules is equivalent to the diagrammatic Hecke category in complete generality.

 

Tensor decomposition is an unsupervised learning methodology that has applications in a wide variety of domains, including chemometrics, criminology, and neuroscience. We focus on low-rank tensor decomposition using  canonical polyadic or CANDECOMP/PARAFAC format. A low-rank tensor decomposition is the minimizer according to some nonlinear program. The usual objective function is the sum of squares error (SSE) comparing the data tensor and the low-rank model tensor. This leads to a nicely-structured problem with subproblems that are linear least squares problems which can be solved efficiently in closed form. However, the SSE metric is not always ideal. Thus, we consider using other objective functions. For instance, KL divergence is an alternative metric is useful for count data and results in a nonnegative factorization. In the context of nonnegative matrix factorization, for instance, KL divergence was popularized by Lee and Seung (1999). We can also consider various objectives such as logistic odds for binary data, beta-divergence for nonnegative data, and so on. We show the benefits of alternative objective functions on real-world data sets. We consider the computational of generalized tensor decomposition based on other objective functions, summarize the work that has been done thus far, and illuminate open problems and challenges. This talk includes joint work with David Hong and Jed Duersch.