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

Series: Algebra Seminar

The structure of non-archimedean curves X and their tame covers f:Y-->X is well understoodand can be adequately described in terms of a (simultaneous) semistable model. In particular, asindicated by the lifting theorem of Amini-Baker-Brugalle-Rabinoff, it encodes all combinatorialand residual algebra-geometric information about f. My talk will be mainly concerned with the morecomplicated case of wild covers, where new discrete invariants appear, with the different function being the most basic one. I will recall its basic properties following my joint work with Cohen and Trushin,and will then pass to the latest results proved jointly with U. Brezner: the different functioncan be refined to an invariant of a residual type, which is a (sort of) meromorphic differential form on the reduction, so that a lifting theorem in the style of ABBR holds for simplest wild covers.

Series: Geometry Topology Seminar

Series: CDSNS Colloquium

The derivative nonlinear Schrödinger equation (DNLS) is an integrable, mass-critical PDE. The integrals of motion may be written as an infinite sequence of functionals on Sobolev spaces of increasing regularity. I will show how to associate to them a family of invariant Gibbs measures, if the L^2 norm of the solution is sufficiently small (mass-criticality). A joint work with R. Lucà (Basel) and D. Valeri (Beijing).

Series: Dissertation Defense

Constrained low rank approximation is a general framework for data analysis, which usually has the advantage of being simple, fast, scalable and domain general. One of the most known constrained low rank approximation method is nonnegative matrix factorization (NMF). This research studies the design and implementation of several variants of NMF for text, graph and hybrid data analytics. It will address challenges including solving new data analytics problems and improving the scalability of existing NMF algorithms. There are two major types of matrix representation of data: feature-data matrix and similarity matrix. Previous work showed successful application of standard NMF for feature-data matrix to areas such as text mining and image analysis, and Symmetric NMF (SymNMF) for similarity matrix to areas such as graph clustering and community detection. In this work, a divide-and-conquer strategy is applied to both methods to improve their time complexity from cubic growth with respect to the reduced low rank to linear growth, resulting in DC-NMF and HierSymNMF2 method. Extensive experiments on large scale real world data shows improved performance of these two methods.Furthermore, in this work NMF and SymNMF are combined into one formulation called JointNMF, to analyze hybrid data that contains both text content and connection structure information. Typical hybrid data where JointNMF can be applied includes paper/patent data where there are citation connections among content and email data where the sender/receipts relation is represented by a hypergraph and the email content is associated with hypergraph edges. An additional capability of the JointNMF is prediction of unknown network information which is illustrated using several real world problems such as citation recommendations of papers and activity/leader detection in organizations.The dissertation also includes brief discussions of relationship among different variants of NMF.

Series: Other Talks

This is an Atlanta Science Festival performance in which mathematicians team up with dancers to give an artistic interpretation to the public of some mathematicians and some mathematical concepts. This year's show will have an emphasis on graph theory. There will be two performances at Drew Charter School in East Atlanta. For tickets go to https://www.freshtix.com/events/mathematics-in-motion---4pm-showing or https://www.freshtix.com/events/mathematics-in-motion---7pm-showing .

Series: GT-MAP Seminars

In this talk I will present an information theoretic approach to
stochastic optimal control and inference that has advantages over
classical methodologies and theories for decision making under
uncertainty. The main idea is that there are certain connections
between optimality principles in control and information theoretic
inequalities in statistical physics that allow us to solve
hard decision making problems in robotics, autonomous systems and
beyond. There are essentially two different points of view of the same
"thing" and these two different points of view overlap for a fairly
general class of dynamical systems that undergo stochastic effects. I
will also present a holistic view of autonomy that collapses planning,
perception and control into one computational engine, and ask questions
such as how organization and structure relates to computation and
performance. The last part of my talk
includes computational frameworks for uncertainty representation and
suggests ways to incorporate these representations within decision
making and control.

Friday, March 9, 2018 - 14:00 ,
Location: Skiles 006 ,
Jen Hom ,
Georgia Tech ,
Organizer: Jennifer Hom

In this series of talks, we will study the relationship between the Alexander module and the bordered Floer homology of the Seifert surface complement. In particular, we will show that bordered Floer categorifies Donaldson's TQFT description of the Alexander module. This seminar will be an hour long to allow for the GT-MAP seminar at 3 pm.

Friday, March 9, 2018 - 10:00 ,
Location: Skiles 006 ,
Kisun Lee ,
Georgia Tech ,
klee669@gatech.edu ,
Organizer: Kisun Lee

This is an intoductory talk for the currently using methods for certifying roots for system of equations. First we discuss about alpha-theory which was constructed by Smale and Shub, and explain how this theory could be modified in order to apply in actual problems. In this step, we point out that alpha theory is still restricted only into polynomial systems and polynomial-exponential systems. After that as a remedy for this problem, we will introduce an interval arithmetic, and the Krawczyk method. We will end the talk with a discussion about how these current methods could be used in more general setting.

Series: Stochastics Seminar

We obtain an extension of
the Ito-Nisio theorem to certain non separable Banach spaces and apply
it to the continuity of the Ito map and Levy processes. The Ito map
assigns a rough path input of an ODE to its solution (output).
Continuity of this map usually
requires strong, non separable, Banach space norms on the path space.
We consider as an input to this map a series expansion a Levy process
and study the mode of convergence of the corresponding series of
outputs. The key to this approach is the validity of
Ito-Nisio theorem in non separable Wiener spaces of certain functions
of bounded p-variation.
This talk is based on a joint work with Andreas Basse-O’Connor and Jorgen Hoffmann-Jorgensen.

Series: Graph Theory Seminar

For a graph G, a set of subtrees of G are edge-independent with
root r ∈ V(G) if, for every vertex v ∈ V(G), the paths between v and r
in each tree are edge-disjoint. A set of k such trees represent a set of
redundant
broadcasts from r which can withstand k-1 edge failures. It is easy to
see that k-edge-connectivity is a necessary condition for the existence
of a set of k edge-independent spanning trees for all possible roots.
Itai and Rodeh have conjectured that this condition
is also sufficient. This had previously been proven for k=2, 3. We
prove the case k=4 using a decomposition of the graph similar to an ear
decomposition. Joint work with Robin Thomas.