Title: Understanding sieve via additive combinatorics

Date: 01/11/2017

Time: 4:10 PM - 5:00 PM

Place: C304 Wells Hall

Speaker: Xuancheng Fernando Shao, Oxford

Many of the most interesting problems in number theory can be phrased under the general framework of sieve problems. For example, the ancient sieve of Eratosthenes is an algorithm to produce primes up to a given threshold. Sieve problems are in general very difficult, and a class of clever techniques have been discovered in the last 100 years to yield stronger and stronger results. In this talk I will discuss the significance of understanding general sieve problems, and present a novel approach to study them via additive combinatorics. This is joint work with Kaisa Matomaki.

Title: Projective coordinates for the analysis of data

Date: 01/12/2017

Time: 2:00 PM - 2:50 PM

Place: C304 Wells Hall

Speaker: Jose Perea, MSU

Barcodes - the persistent homology of data - have been shown to be effective quantifiers of multi-scale structure in finite metric spaces. Moreover, the universal coefficient theorem implies that (for a fixed field of coefficients) the barcodes obtained with persistent homology are identical to those obtained with persistent cohomology. Persistent cohomology, on the other hand, is better behaved computationally and allows one to use convenient interpretations such as the Brown representability theorem. We will show in this talk how one can use persistent cohomology to produce maps from data to (real and complex) projective space, and conversely, how to use these projective coordinates to interpret persistent cohomology computations.

Title: Random discrete structures: Phase transitions, scaling limits, and universality

Date: 01/13/2017

Time: 4:10 PM - 5:00 PM

Place: C304 Wells Hall

Speaker: Sanchayan Sen, McGill University

The aim of this talk is to give an overview of some recent results in two interconnected areas:
a) Random graphs and complex networks: The last decade of the 20th century saw significant growth in the availability of empirical data on networks, and their relevance in our daily lives. This stimulated activity in a multitude of fields to formulate and study models of network formation and dynamic processes on networks to understand real-world systems.
One major conjecture in probabilistic combinatorics, formulated by statistical physicists using non-rigorous arguments and enormous simulations in the early 2000s, is as follows: for a wide array of random graph models on n vertices and degree exponent \tau>3, typical distance both within maximal components in the critical regime as well as on the minimal spanning tree on the giant component in the supercritical regime scale like n^{\frac{\tau\wedge 4 -3}{\tau\wedge 4 -1}}. In other words, the degree exponent determines the universality class the random graph belongs to. The mathematical machinery available at the time was insufficient for providing a rigorous justification of this conjecture.
More generally, recent research has provided strong evidence to believe that several objects, including
(i) components under critical percolation,
(ii) the vacant set left by a random walk, and
(iii) the minimal spanning tree,
constructed on a wide class of random discrete structures converge, when viewed as metric measure spaces, to some random fractals in the Gromov-Hausdorff-Prokhorov sense, and these limiting objects are universal under some general assumptions. We report on recent progress in proving these conjectures.
b) Stochastic geometry: In contrast, less precise results are known in the case of spatial systems. We discuss a recent result concerning the length of spatial minimal spanning trees that answers a question raised by Kesten and Lee in the 90's, the proof of which relies on a variation of Stein's method and a quantification of the classical Burton-Keane argument in percolation theory.
Based on joint work with Louigi Addario-Berry, Shankar Bhamidi, Nicolas Broutin, Sourav Chatterjee, Remco van der Hofstad, and Xuan Wang.