Seminars and Colloquia by Series

A tale of models for random graphs

Series
Combinatorics Seminar
Time
Thursday, January 17, 2019 - 12:00 for 1.5 hours (actually 80 minutes)
Location
Skiles 005
Speaker
Jeong Han KimKorea Institute for Advanced Study (KIAS)
Since Erdős–Rényi introduced random graphs in 1959, two closely related models for random graphs have been extensively studied. In the G(n,m) model, a graph is chosen uniformly at random from the collection of all graphs that have n vertices and m edges. In the G(n,p) model, a graph is constructed by connecting each pair of two vertices randomly. Each edge is included in the graph G(n,p) with probability p independently of all other edges. Researchers have studied when the random graph G(n,m) (or G(n,p), resp.) satisfies certain properties in terms of n and m (or n and p, resp.). If G(n,m) (or G(n,p), resp.) satisfies a property with probability close to 1, then one may say that a `typical graph’ with m edges (or expected edge density p, resp.) on n vertices has the property. Random graphs and their variants are also widely used to prove the existence of graphs with certain properties. In this talk, two problems for these categories will be discussed. First, a new approach will be introduced for the problem of the emergence of a giant component of G(n,p), which was first considered by Erdős–Rényi in 1960. Second, a variant of the graph process G(n,1), G(n,2), …, G(n,m), … will be considered to find a tight lower bound for Ramsey number R(3,t) up to a constant factor. (No prior knowledge of graph theory is needed in this talk.)

Matrix Estimation with Latent Permutations

Series
Job Candidate Talk
Time
Thursday, January 17, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Cheng MaoYale University
A wide variety of applied tasks, such as ranking, clustering, graph matching and network reconstruction, can be formulated as a matrix estimation problem where the rows and columns of the matrix are shuffled by a latent permutation. The combinatorial nature of the unknown permutation and the non-convexity of the parameter space result in both statistical and algorithmic challenges. I will present recent developments of average-case models and efficient algorithms, primarily for the problems of ranking from comparisons and statistical seriation. On the statistical side, imposing shape constraints on the underlying matrix extends traditional parametric approaches, allowing for more robust and adaptive estimation. On the algorithmic front, I discuss efficient local algorithms with provable guarantees, one of which tightens a conjectured statistical-computational gap for a stochastically transitive ranking model.

Fluctuation of ergodic sums over periodic orbits

Series
CDSNS Colloquium
Time
Thursday, January 17, 2019 - 10:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Manfred DenkerPenn State University
The fluctuations of ergodic sums by the means of global and local specifications on periodic points will be discussed. Results include a Lindeberg-type central limit theorems in both setups of specification. As an application, it is shown that averaging over randomly chosen periodic orbits converges to the integral with respect to the measure of maximal entropy as the period approaches infinity. The results also suggest to decompose the variances of ergodic sums according to global and local sources.

Dynamics and Topology of Contact 3-Manifolds with negative $\alpha$-Sectional Curvature: Lecture 1

Series
Geometry Topology Student Seminar
Time
Wednesday, January 16, 2019 - 14:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Surena HozooriGeorgia Institute of Technology
In this series of (3-5) lectures, I will talk about different aspects of a class of contact 3-manifolds for which geometry, dynamics and topology interact subtly and beautifully. The talks are intended to include short surveys on "compatibility", "Anosovity" and "Conley-Zehnder indices". The goal is to use the theory of Contact Dynamics to show that conformally Anosov contact 3-manifolds (in particular, contact 3-manifolds with negative $\alpha$-sectional curvature) are universally tight, irrducible and do not admit a Liouville cobordism to tight 3-sphere.

Sparse domination and the strong maximal function

Series
Analysis Seminar
Time
Wednesday, January 16, 2019 - 13:55 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Alexander BarronBrown University
There has been recent interest in sparse bounds for various operators that arise in harmonic analysis. Perhaps the most basic "sparse" result is a pointwise bound for the dyadic Hardy-Littlewood maximal function. It turns out that the direct analogue of this result does not hold if one adds an extra dilation parameter: the dyadic strong maximal function does not admit a pointwise sparse bound or a sparse bound involving L^1 forms (both of which hold in the one-parameter setting). The proof is based on the construction of a certain pair of extremal point sets. This is joint work with Jose Conde-Alonso, Yumeng Ou, and Guillermo Rey.

Synchronization of pendulum clocks and metronomes

Series
Applied and Computational Mathematics Seminar
Time
Monday, January 14, 2019 - 01:55 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Prof. Guillermo GoldszteinGT School of Math
In 1665, Huygens discovered that, when two pendulum clocks hanged from a same wooden beam supported by two chairs, they synchronize in anti-phase mode. On the other hand, metronomes synchronize in-phase when oscillating on top of the same movable surface. In this talk, I will describe and analyze a model to help understand the conditions that lead to anti-phase synchronization vs. the conditions that lead to in-phase synchronization.

Convergence of the viscosity solutions in vanishing contact structure problem

Series
Dynamical Systems Working Seminar
Time
Friday, January 11, 2019 - 15:00 for 1 hour (actually 50 minutes)
Location
Skiles 246
Speaker
Qinbo ChenAMSS & GT Math
In this talk, I will discuss the vanishing contact structure problem, which focuses on the asymptotic behavior of the viscosity solutions uε of Hamilton-Jacobi equation H (x, Du(x), ε u(x)) =c, as the factor ε tends to zero. This is a natural generalization of the vanishing discount problem. I will explain how to characterize the limit solution in terms of Peierls barrier functions and Mather measures from a dynamical viewpoint. This is a joint work with Hitoshi Ishii, Wei Cheng, and Kai Zhao.

A numerical analysis approach to convex optimization

Series
ACO Student Seminar
Time
Friday, January 11, 2019 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Richard PengCS, Georgia Tech
In current convex optimization literature, there are significant gaps between algorithms that produce high accuracy (1+1/poly(n))-approximate solutions vs. algorithms that produce O(1)-approximate solutions for symmetrized special cases. This gap is reflected in the differences between interior point methods vs. (accelerated) gradient descent for regression problems, and between exact vs. approximate undirected max-flow. In this talk, I will discuss generalizations of a fundamental building block in numerical analysis, preconditioned iterative methods, to convex functions that include p-norms. This leads to algorithms that converge to high accuracy solutions by crudely solving a sequence of symmetric residual problems. I will then briefly describe several recent and ongoing projects, including p-norm regression using m^{1/3} linear system solves, p-norm flow in undirected unweighted graphs in almost-linear time, and further improvements to the dependence on p in the runtime.

Network data: Modeling and Statistical Analysis

Series
Job Candidate Talk
Time
Thursday, January 10, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Subhabrata SenMIT
Network data arises frequently in modern scientific applications. These networks often have specific characteristics such as edge sparsity, heavy-tailed degree distribution etc. Some broad challenges arising in the analysis of such datasets include (i) developing flexible, interpretable models for network datasets, (ii) testing for goodness of fit, (iii) provably recovering latent structure from such data.In this talk, we will discuss recent progress in addressing very specific instantiations of these challenges. In particular, we will1. Interpret the Caron-Fox model using notions of graph sub-sampling, 2. Study model misspecification due to rare, highly “influential” nodes, 3. Discuss recovery of community structure, given additional covariates.

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