- You are here:
- GT Home
- Home
- News & Events

Series: Other Talks

This is a brief (15 minute) presentation of an undergraduate project that took place in the 2017 Fall semester.

Series: Combinatorics Seminar

Suppose we want to find the largest independent set or maximal cut in a sparse Erdos-Renyi graph, where the average degree is constant. Many algorithms proceed by way of local decision rules, for instance, the "nibbling" procedure. I will explain a form of local algorithms that captures many of these. I will then explain how these fail to find optimal independent sets or cuts once the average degree of the graph gets large. There are some nice connections to entropy and spin glasses.

Series: GT-MAP Seminars

Please go to http://gtmap.gatech.edu or http://gtmap.gatech.edu/events/workshop-mathematics-dynamical-systems for schedule, title and abstract.

Series: Stochastics Seminar

Cars are placed with density p on the lattice. The remaining vertices are parking spots that can fit one car. Cars then drive around at random until finding a parking spot. We study the effect of p on the availability of parking spots and observe some intriguing behavior at criticality. Joint work with Michael Damron, Janko Gravner, Hanbeck Lyu, and David Sivakoff. arXiv id: 1710.10529.

Series: Graph Theory Seminar

Let G be a graph containing 5 different vertices a0, a1, a2, b1 and b2.
We say that (G, a0, a1, a2, b1, b2) is feasible if G contains disjoint
connected subgraphs G1, G2, such that {a0, a1, a2}⊆V(G1) and {b1,
b2}⊆V(G2). In
this talk, we will complete a sketch of our arguments for characterizing when (G, a0, a1, a2, b1, b2) is feasible. Joint work with Changong Li, Robin Thomas, and Xingxing Yu.

Series: Job Candidate Talk

Random effects models are commonly used to measure genetic
variance-covariance matrices of quantitative phenotypic traits. The
population eigenvalues of these matrices describe the evolutionary
response to selection. However, they may be difficult to estimate from
limited samples when the number of traits is large. In this talk, I will
present several results describing the eigenvalues of classical MANOVA
estimators of these matrices, including dispersion of the bulk
eigenvalue distribution, bias and aliasing of large "spike" eigenvalues,
and distributional limits of eigenvalues at the spectral edges. I will
then discuss a new procedure that uses these results to obtain better
estimates of the large population eigenvalues when there are many
traits, and a Tracy-Widom test for detecting true principal components
in these models. The theoretical results extend proof techniques in
random matrix theory and free probability, which I will also briefly
describe.This is joint work with Iain Johnstone, Yi Sun, Mark Blows, and Emma Hine.

Series: Analysis Seminar

In
this talk, I will discuss some polynomials that are best approximants
(in some sense!) to reciprocals of functions in some analytic function
spaces of the unit disk. I will examine the extremal
problem of finding a zero of minimal modulus, and will show how that
extremal problem is related to the spectrum of a certain Jacobi matrix
and real orthogonal polynomials on the real line.

Wednesday, November 29, 2017 - 13:55 ,
Location: Skiles 006 ,
Anubhav Mukherjee ,
Georgia Tech ,
Organizer: Jennifer Hom

I'll try to describe some known facts about 3 manifolds. And in the end I want to give some idea about Geometrization Conjecture/theorem.

Series: Research Horizons Seminar

In
this talk, we consider the structure of a real $n \times n$ matrix in
the form of $A=JL$, where $J$ is anti-symmetric and $L$ is symmetric.
Such a matrix comes from a linear Hamiltonian ODE system with $J$ from
the symplectic structure and the Hamiltonian
energy given by the quadratic form $\frac 12\langle Lx, x\rangle$. We
will discuss the distribution of the eigenvalues of $A$, the
relationship between the canonical form of $A$ and the structure of the
quadratic form $L$, Pontryagin invariant subspace theorem,
etc. Finally, some extension to infinite dimensions will be mentioned.

Series: PDE Seminar

Geometric tangential analysis refers to a constructive systematic approach based on the concept that a problem which enjoys greater regularity can be “tangentially" accessed by certain classes of PDEs. By means of iterative arguments, the method then imports regularity, properly corrected through the path used to access the tangential equation, to the original class. The roots of this idea likely go back to the foundation of De Giorgi’s geometric measure theory of minimal surfaces, and accordingly, it is present in the development of the contemporary theory of free boundary problems. This set of ideas also plays a decisive role in Caffarelli’s work on fully non-linear elliptic PDEs, and subsequently in his studies on Monge-Ampere equations from the 1990’s. In recent years, however, geometric tangential methods have been significantly enhanced, amplifying their range of applications and providing a more user-friendly platform for advancing these endeavors. In this talk, I will discuss some fundamental ideas supporting (modern) geometric tangential methods and will exemplify their power through select examples.