Monday, March 4, 2013 - 14:00 , Location: Skiles 005 , Xiaojing Ye , Georgia Tech, School of Math , firstname.lastname@example.org , Organizer: Sung Ha Kang
We consider the modeling and computations of random dynamical processes of viral signals propagating over time in social networks. The viral signals of interests can be popular tweets on trendy topics in social media, or computer malware on the Internet, or infectious diseases spreading between human or animal hosts. The viral signal propagations can be modeled as diffusion processes with various dynamical properties on graphs or networks, which are essentially different from the classical diffusions carried out in continuous spaces. We address a critical computational problem in predicting influences of such signal propagations, and develop a discrete Fokker-Planck equation method to solve this problem in an efficient and effective manner. We show that the solution can be integrated to search for the optimal source node set that maximizes the influences in any prescribed time period. This is a joint work with Profs. Shui-Nee Chow (GT-MATH), Hongyuan Zha (GT-CSE), and Haomin Zhou (GT-MATH).
Monday, February 4, 2013 - 14:05 , Location: Skiles 005 , Robert Lipton , LSU , Organizer: Guillermo Goldsztein
Metamaterials are a new form of structured materials used to control electromagnetic waves through localized resonances. In this talk we introduce a rigorous mathematical framework for controlling localized resonances and predicting exotic behavior inside optical metamaterials. The theory is multiscale in nature and provides a rational basis for designing microstructure using multiphase nonmagnetic materials to create backward wave behavior across prescribed frequency ranges.
Friday, January 25, 2013 - 14:00 , Location: Skiles 005 , Sangwoon Yun , Sung Kyun Kwan Univ. (Korea) , email@example.com , Organizer: Sung Ha Kang
In this talk, we introduce coordinate gradient descent methods for nonsmooth separable minimization whose objective function is the sum of a smooth function and a convex separable function and for linearly constrained smooth minimization. We also introduce incremental gradient methods for nonsmooth minimization whose objective function is the sum of smooth functions and a convex function.
Friday, January 18, 2013 - 14:00 , Location: Skiles 005 , Michael Klibanov , University of North Carolina, Charlotte , Organizer: Haomin Zhou
Coefficient Inverse Problems (CIPs) are the hardest ones to work with in the field of Inverse Problems. Indeed, they are both nonlinear and ill-posed. Conventional numerical methods for CIPs are based on the least squares minimization. Therefore, these methods suffer from the phenomenon of multiple local minima and ravines. This means in turn that those methods are locally convergent ones. In other words, their convergence is guaranteed only of their starting points of iterations are located in small neighborhoods of true solutions. In the past five years we have developed a new numerical method for CIPs for an important hyperbolic Partial Differential Equation, see, e.g. [1,2] and references cited there. This is a globally convergent method. In other words, there is a rigorous guarantee that this method delivers a good approximation for the exact solution without any advanced knowledge of a small neighborhood of this solution. In simple words, a good first guess is not necessary. This method is verified on many examples of computationally simulated data. In addition, it is verified on experimental data. In this talk we will outline this method and present many numerical examples with the focus on experimental data.REFERENCES  L. Beilina and M.V. Klibanov, Approximate Global Convergence and Adaptivity for Coefficient Inverse Problems, Springer, New York, 2012.  A.V. Kuzhuget, L. Beilina and M.V. Klibanov, A. Sullivan, L. Nguyen and M.A. Fiddy, Blind backscattering experimental data collected in the field and an approximately globally convergent inverse algorithm, Inverse Problems, 28, 095007, 2012.
Monday, January 14, 2013 - 14:00 , Location: Skiles 005 , Xue-Cheng Tai , University of Bergen, Department of Mathematics, Norway , Organizer: Sung Ha Kang
In this talk, we present a new global optimization based approach to contour evolution, with or without the novel variational shape constraint that imposes a generic star shape using a continuous max-flow framework. In theory, the proposed continuous max-flow model provides a dual perspective to the reduced continuous min-cut formulation of the contour evolution at each discrete time frame, which proves the global optimality of the discrete time contour propagation. The variational analysis of the flow conservation condition of the continuous max-flow model shows that the proposed approach does provide a fully time implicit solver to the contour convection PDE, which allows a large time-step size to significantly speed up the contour evolution. For the contour evolution with a star shape prior, a novel variational representation of the star shape is integrated to the continuous max-flow based scheme by introducing an additional spatial flow. In numerics, the proposed continuous max-flow formulations lead to efficient duality-based algorithms using modern convex optimization theories. Our approach is implemented in a GPU, which significantly improves computing efficiency. We show the high performance of our approach in terms of speed and reliability to both poor initialization and large evolution step-size, using numerous experiments on synthetic, real-world and 2D/3D medical images.This talk is based in a joint work by: J. Yuan, E. Ukwatta, X.C. Tai, A. Fenster, and C. Schnorr.
Monday, November 26, 2012 - 14:00 , Location: Skiles 005 , Prashant Athavale , Fields Institute, Dep. of Math, University of Toronto, , firstname.lastname@example.org , Organizer: Sung Ha Kang
Images consist of features of varying scales. Thus, multiscale image processing techniques are extremely valuable, especially for medical images. We will discuss multiscale image processing techniques based onvariational methods, specifically (BV, L^2) and (BV, L^1) decompositions. We will discuss the applications to real time denoising, deblurring and image registration.
Monday, November 19, 2012 - 14:00 , Location: Skiles 005 , Hao Gao , Dep of Math and CS/ Dep of Radiology and Imaging Sciences, Emory University , email@example.com , Organizer: Sung Ha Kang
I will talk about (1) a few sparsity models for 4DCBCT; (2) the split Bregman method as an efficient algorithm for solving L1-type minimization problem; (3) an efficient implementation through fast and highly parallelizable algorithms for computing the x-ray transform and its adjoint.
Monday, November 12, 2012 - 14:00 , Location: Skiles 005 , Antonio Cicone , GT Math , firstname.lastname@example.org , Organizer: Sung Ha Kang
Given F, finite set of square matrices of dimension n, it is possible to define the Joint Spectral Radius or simply JSR as a generalization of the well known spectral radius of a matrix. The JSR evaluation proves to be useful for instance in the analysis of the asymptotic behavior of solutions of linear difference equations with variable coefficients, in the construction of compactly supported wavelets of and many others contexts. This quantity proves, however, to be hard to compute in general. Gripenberg in 1996 proposed an algorithm for the computation of lower and upper bounds to the JSR based on a four member inequality and a branch and bound technique. In this talk we describe a generalization of Gripenberg's method based on ellipsoidal norms that achieve a tighter upper bound, speeding up the approximation of the JSR. We show the performance of this new algorithm compared with Gripenberg's one. This talk is based on joint work with V.Y.Protasov.
Monday, October 29, 2012 - 14:00 , Location: Skiles 005 , Hyenkyun Woo , Georiga Tech CSE , email@example.com , Organizer: Sung Ha Kang
The fully developed speckle(multiplicative noise) naturally appears in coherent imaging systems, such as synthetic aperture radar imaging systems. Since the speckle is multiplicative, it makes difficult to interpret observed data. In this talk, we introduce total variation based variational model and convex optimization algorithm(linearized proximal alternating minimization algorithm) to efficiently remove speckle in synthetic aperture radar imaging systems. Numerical results show that our proposed methods outperform the augmented Lagrangian based state-of-the-art algorithms.
Monday, October 22, 2012 - 14:00 , Location: Skiles 005 , Alessio Medda , Aerospace Transportation and Advanced System Laboratory, Georgia Tech Research Institute , Alessio.Medda@gtri.gatech.edu , Organizer: Sung Ha Kang
In this talk, I will present two examples of the application of wavelet analysis to the understanding of mild Traumatic Brain Injury (mTBI). First, the talk will focus on how wavelet-based features can be used to define important characteristics of blast-related acceleration and pressure signatures, and how these can be used to drive a Naïve Bayes classifier using wavelet packets. Later, some recent progress on the use of wavelets for data-driven clustering of brain regions and the characterization of functional network dynamics related to mTBI will be discussed. In particular, because neurological time series such as the ones obtained from an fMRI scan belong to the class of long term memory processes (also referred to as 1/f-like processes), the use of wavelet analysis guarantees optimal theoretical whitening properties and leads to better clusters compared to classical seed-based approaches.