http://www.fellowships.gatech.edu

 GT Fellowships Office staff and a special guest will speak.  If you are interested in learning more about the award and the application process OR if you are already preparing an application, these sessions are for you!

Faculty presenter: Dr. Manu Platt.   Dr. Platt is a Professor in the Coulter Dept. of Biomedical Engineering at Georgia Tech and Emory University. 
 

"Generalized Fast Multipole Method"

Abstract:
I will present a novel FMM technique applicable to both oscillatory and non-oscillatory
kernels. The method is very flexible and accurate. It is more general than existing FMMs and can often compete with nearly optimal schemes for specific kernels like 1/r. I will present the progress of the research with some preliminary numerical results.

Bio:

 Visit Dr. Darve's website.

You are cordially invited to attend a reception preceding the seminar to chat informally with faculty and students. Reception will take place outside Klaus 1324 between 1:30 - 2:00.

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"Model-Based Data Mining for Functional Data Under Spatial Interdependence" 

Abstract:

In this seminar, I will present data mining methods for discovering and summarizing patterns in functional data observed under spatial interdependence. The field of functional data analysis has already provided a series of competitive data mining approaches, but they are generally limited to the assumption of independence between the random functions. This assumption is rather restrictive in many research applications.

In the first part of this seminar, I will introduce a model-based method for clustering random functions which are spatially interdependent. The time functions are decomposed into spatial global and time-dependent cluster effects using a semi-parametric model. We assume that the clustering membership is a realization from a Markov random field. In the case study presented in this paper, we focus on obtaining temporal cluster trends for racial-ethnic diversity for five southeast states in the US.

In the second part of this seminar, I will introduce a computational efficient and theoretically-founded cross-correlation analysis. Under the proposed semi-parametric model, we show that the cross-correlation estimators are asymptotically unbiased under the conditions that the sample size is large and the intrinsic dimensionality of the functional processes is much smaller than the sample size. We illustrate this correlation analysis within a demographic study, in which we analyze the association between per capita income and racial-ethnic diversity.

This is joint work with Huijing Jiang, PhD student in ISyE, Georgia Institute of Technology

Bio:

Nicoleta Serban is an assistant professor in ISyE. She received her B.S. in Mathematics and an M.S. in Theoretical Statistics and Stochastic Processes from the University of Bucharest. She went on to earn her Ph.D. in Statistics at Carnegie Mellon University. Before joining Georgia Tech, Dr. Serban's research focused on nonparametric statistical methods motivated by recent applications from proteomics and genomics. Dr Serban's current research focusses on multiple functional estimation and clustering with applications to industrial performance, service site location, socio-economics and NMR biomolecular studies. Visit Dr. Serban's website.

Please join us for a reception preceding the seminar outside Klaus 1324, beginning at 1:30pm.

To receive future announcements, please sign up to the cse-seminar email list: https://mailman.cc.gatech.edu/mailman/listinfo/cse-seminar

Professor, Mathematics and Computer Science Department, Emory University 

"Efficient Iterative Methods for Large Scale Inverse Problems"

Abstract:

Ill-posed problems arise in many image processing applications, including microscopy, medicine and astronomy.  Iterative methods are typically recommended for these large scale problems, but they can be difficult to use in practice.  In this talk we describe a hybrid approach that combines the Golub-Kahan bidiagonalization algorithm with Tikhonov regularization and a weighted generalized cross validation scheme.  We also show how this method can be used as an effective scheme for choosing regularization parameters for certain nonlinear inverse problem.  Applications from image processing illustrate the effectiveness of the resulting numerical schemes.

This is joint work with Julianne Chung, University of Maryland.

Bio:

James Nagy is a Professor of Mathematics and Computer Science at Emory University.  He received his Ph.D. in Applied Mathematics from North Carolina State University in 1991.  Before joining Emory University in 1999 he had postdoctoral research fellowships with the IMA at the University of Minnesota, with the NSF at the University of Maryland, and was on the faculty at Southern Methodist University.  He is on the editorial boards of SIAM Journal on Scientific Computing (SISC), SIAM Journal on Matrix Analysis and Applications (SIMAX), and the SIAM Book Series "Fundamentals of Algorithms".  His research interests include numerical linear algebra, structured matrix computations, and numerical solution of inverse problems in image processing. Among his many publications includes the recent SIAM book, "Image Deblurring: Matrices, Spectra and Filtering", which is co-authored with Per Christian Hansen and Dianne O'Leary. Visit Dr. Nagy's website.

Please join us for a reception preceding the seminar outside Klaus 1324, beginning at 1:30pm.

To receive future announcements, please sign up to the cse-seminar email list: https://mailman.cc.gatech.edu/mailman/listinfo/cse-seminar