
Martin Tassy
Tue Feb 28, 2017
11:00 am
Previous works have shown that arctic circle phenomenons and limiting behaviors of some integrable discrete systems can be explained by a variational principle. In this talk we will present the first results of the same type for a nonintegrable discrete system: graph homomorphisms form Z^d to a regular tree. We will also explain how the technique...

Greg Lawler
Fri Feb 24, 2017
2:00 am
For a smooth curve, the natural paraemtrization
is parametrization by arc length. What is the analogue
for a random curve of fractal dimension d? Typically,
such curves have Hausdorff dmeasure 0. It turns out
that a different quantity, Minkowski content, is the
right thing.
I will discuss results of this type...

Larry Goldstein
Tue Feb 7, 2017
11:00 pm
Normal approximation for recovery of structured unknowns in high dimension: Steining the Steiner formula Larry Goldstein, University of Southern California Abstract Intrinsic volumes of convex sets are natural geometric quantities that also play important roles in applications. In particular, the discrete probability distribution L(VC) given by...

Elliot Paquette
Tue Jan 24, 2017
11:00 am
Suppose one wants to calculate the eigenvalues of a large, nonnormal matrix. For example, consider the matrix which is 0 in most places except above the diagonal, where it is 1. The eigenvalues of this matrix are all 0. Similarly, if one conjugates this matrix, in exact arithmetic one would get all eigenvalues equal to 0. ...

Yves Le Jan
Tue Jan 17, 2017
11:00 am
After presenting a short survey of Markov loops, we will introduce related random fields and study their properties.

Steven Heilman
Tue Nov 29, 2016
11:00 pm
The seminal invariance principle of MosselO'DonnellOleszkiewicz implies the following. Suppose we have a multilinear polynomial Q, all of whose partial derivatives are small. Then the distribution of Q on i.i.d. uniform {1,1} inputs is close to the distribution of Q on i.i.d. standard Gaussian inputs. The case that Q is a linear function...

Zhongyang Li
Mon Nov 21, 2016
12:00 pm
A configuration in the 12 model is a subgraph of the hexagonal lattice, in which each vertex is incident to 1 or 2 edges. By assigning weights to configurations at each vertex, we can define a family of probability measures on the space of these configurations, such that the probability of a configuration is proportional to the...