## Seminars and Colloquia by Series

Monday, September 22, 2014 - 14:00 , Location: Skiles 005 , Dr. Chunmei Wang , Georgia Tech Mathematics , Organizer: Martin Short
Weak Galerkin finite element method is a new and efficient numerical method for solving PDEs which was first proposed by Junping Wang and Xiu Ye in 2011. The main idea of WG method is to introduce weak differential operators and apply them to the corresponding variational formulations to solve PDEs. In this talk, I will focus on the WG methods for biharmonic equations, maxwell equations and div-curl equations.
Monday, September 8, 2014 - 14:00 , Location: Skiles 005 , Dr. Marta Canadell , Georgia Tech Mathematics , Organizer: Martin Short
We explain a method for the computation of normally hyperbolic invariant manifolds (NHIM) in discrete dynamical systems.The method is based in finding a parameterization for the manifold formulating a functional equation. We solve the invariance equation using a Newton-like method taking advantage of the dynamics and the geometry of the invariant manifold and its invariant bundles.  The method allows us to compute a NHIM and its internal dynamics, which is a-priori unknown.We implement this method to continue the invariant manifold with respect to parameters, and to explore different mechanisms of breakdown.  This is a joint work with Alex Haro.
Monday, April 28, 2014 - 14:00 , Location: Skiles 005 , , Claremont McKenna College , Organizer: Martin Short
In this talk we will discuss results for robust signal reconstruction from random observations via synthesis and analysis methods in compressive signal processing (CSP). CSP is a new and exciting field which arose as an efficient alternative to traditional signal acquisition techniques. Using a (usually random) projection, signals are measured directly in compressed form, and methods are then needed to recover the signal from those measurements. Synthesis methods attempt to identify the low-dimensional representation of the signal directly, whereas analysis type methods reconstruct in signal space. We also discuss special cases including provable near-optimal reconstruction guarantees for total-variation minimization and new techniques in super-resolution.
Monday, April 14, 2014 - 14:00 , Location: Skiles 005 , Professor Ke Chen , The University of Liverpool, UK , Organizer: Haomin Zhou
Mathematical imaging is not only a multidisciplinary research area but also a major cross-disciplinesubject within mathematical sciences as image analysis techniques involve analysis, optimization, differential geometry and nonlinear partial differential equations, computational algorithms and numerical analysis.In this talk I first review various models and techniques in the variational frameworkthat are used for restoration of images. Then I discuss more recent work on i) choice of optimal coupling parameters for the TV model,ii) the blind deconvolution and iii) high order regularization models.This talk covers joint work with various collaborators in imaging  including J. P. Zhang, T.F. Chan, R. H. Chan, B. Yu,  L. Sun, F. L. Yang (China), C. Brito (Mexico), N. Chumchob (Thailand),  M. Hintermuller (Germany), Y. Q. Dong (Denmark), X. C. Tai (Norway) etc.
Monday, April 7, 2014 - 14:00 , Location: Skiles 005 , Ming-Jun Lai , University of Georgia , Organizer: Martin Short
I mainly discuss the following problem: given a set of scattered locations and nonnegative values, how can one construct a smooth interpolatory or fitting surface of the given data?  This problem arises from the visualization of scattered data and the design of surfaces with shape control.  I shall start explaining scattered data interpolation/fitting based on bivariate spline functions over triangulation without nonnegativity constraint.  Then I will explain the difficulty of the problem of finding nonnegativity perserving interpolation and fitting surfaces and recast the problem into a minimization problem with the constraint. I shall use the  Uzawa algorithm to solve the constrained minimization problem. The convergence of the algorithm in the bivariate spline setting will be shown.  Several numerical examples will be demonstrated and finally a real life example for fitting oxygen anomalies over the Gulf of Mexico will be explained.
Monday, March 31, 2014 - 14:00 , Location: Skiles 005 , Benjamin Seibold , Temple University , Organizer: Martin Short
Initially homogeneous vehicular traffic flow can become inhomogeneous even in the absence of obstacles. Such phantom traffic jams'' can be explained as instabilities of a wide class of second-order'' macroscopic traffic models. In this unstable regime, small perturbations amplify and grow into nonlinear traveling waves. These traffic waves, called jamitons'', are observed in reality and have been reproduced experimentally. We show that jamitons are analogs of detonation waves in reacting gas dynamics, thus creating an interesting link between traffic flow, combustion, water roll waves, and black holes. This analogy enables us to employ the Zel'dovich-von Neumann-Doering theory to predict the shape and travel velocity of the jamitons. We furthermore demonstrate that the existence of jamiton solutions can serve as an explanation for multi-valued parts that fundamental diagrams of traffic flow are observed to exhibit.
Monday, March 24, 2014 - 14:00 , Location: Skiles 005 , Seth Marvel , University of Michigan , Organizer: Martin Short
In this talk, I will present work on two very different problems, with the only common theme being a substantial departure from standard approaches.  In the first part, I will discuss how the spread of many common contagions may be more accurately modeled with nonlocal approaches than with the current standard of local approaches, and I will provide a minimal mathematical foundation showing how this can be done.  In the second part, I will present a new computational method for ranking items given only a set of pairwise preferences between them.  (This is known as the minimum feedback arc set problem in computer science.)  For a broad range of cases, this method appears to beat the current "world record" in both run time and quality of solution.
Monday, March 10, 2014 - 14:00 , Location: Skiles 005 , , Texas State, San Marcos , Organizer: John McCuan
The symmetric configurations for the equilibrium shape of a fluid interfaceare given by the geometric differential equation mean curvature isproportional to height.  The equations are explored numerically tohighlight the differences in classically treated capillary tubes andsessile drops, and what has recently emerged as annular capillary surfaces. Asymptotic results are presented.
Monday, March 3, 2014 - 14:00 , Location: Skiles 005 , , GT Math , Organizer: Sung Ha Kang
In this talk, the two approaches for computing the long time behavior of highly oscillatory dynamical systems will be introduced.  Firstly, a generalization of the backward-forward HMM (BF HMM) will be discussed. It is intended to deal with the multiple time scale (>2) behavior of certain nonlinear systems where the non-linearity is introduced as a perturbation to a primarily linear problem. Focusing on the Fermi-Pasta-Ulam problem, I propose a three-scale version of the BF HMM.  Secondly, I will consider a multiscale method using a signal processingidea. The dynamics on the slow time scale can be approximated by an averaged system gained by fltering out the fast oscillations. An Adaptive Local Iterative Filtering (ALIF) algorithm is used to do such averaging with respect to fast oscillations.
Monday, February 24, 2014 - 14:00 , Location: Skiles 005 , Le Song , Georgia Tech CSE , Organizer: Martin Short
Dynamical processes, such as information diffusion in social networks, gene regulation in biological systems and functional collaborations between brain regions, generate a large volume of high dimensional “asynchronous” and “interdependent” time-stamped event data. This type of timing information is rather different from traditional iid. data and discrete-time temporal data, which calls for new models and scalable algorithms for learning, analyzing and utilizing them. In this talk, I will present methods based on multivariate point processes, high dimensional sparse recovery, and randomized algorithms for addressing a sequence of problems arising from this context. As a concrete example, I will also present experimental results on learning and optimizing information cascades in web logs, including estimating hidden diffusion networks and influence maximization with the learned networks. With both careful model and algorithm design, the framework is able to handle millions of events and millions of networked entities.