Previous Conferences & Workshops

Geometric and functional inequalities are fundamental in various mathematical areas, such as the calculus of variations, partial differential equations, and geometry. Classic examples encompass the isoperimetric inequality, Sobolev inequalities, and...

I will discuss joint work in progress with Will Sawin on function field multiple Dirichlet series constructed based on a set of axioms from algebraic geometry. These series have applications to moments of Dirichlet L-functions for characters of...

Open book decompositions provide a topological decomposition of a given manifold. We focus on dimension three. While the definition seems to be purely topological, it encodes information about fibered knots, surface dynamics, contact structures of...

K-theory arose in the 1950s from Grothendieck’s formulation of the Riemann-Roch theorem – that is, from attempts to calculate spaces of sections of vector bundles on a variety X via intersection theory on X.  An equivariant version was introduced...

What are some of the ways in which binary-valued functions can accurately approximate continuous-valued ones? This talk will be a gentle exposition of the mathematics of "noise-shaping quantization" presented through motivating applications. We will...

Geometric and functional inequalities are fundamental in various mathematical areas, such as the calculus of variations, partial differential equations, and geometry. Classic examples encompass the isoperimetric inequality, Sobolev inequalities, and...

K-theory arose in the 1950s from Grothendieck’s formulation of the Riemann-Roch theorem – that is, from attempts to calculate spaces of sections of vector bundles on a variety X via intersection theory on X.  An equivariant version was introduced...

The fundamental group of an n-dimensional closed hyperbolic manifold admits a natural isometric action on the hyperbolic space $H^n$. If n is at most 3 or the manifold is arithmetic of simplest type, then the group also admits many geometric actions...

Boosting is a fundamental and widely used method in machine learning, which determines that weak learnability of binary functions implies strong learnability. Traditionally, boosting theory has primarily focused on symmetric 0-1 loss functions...