Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Yao Xie

 Yao Xie joined Georgia Institute of Technology as an Assistant Professor in the H. Milton Stewart School of Industrial & Systems Engineering in 2013. Prior to that, she worked as a Research Scientist at Duke University in the Department of Electrical and Computer Engineering, after receiving her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2011. She is interested in signal processing,…


Liang Liu

http://faculty.franklin.uga.edu/lliu/


Anindya Roy

We present a reparameterization of vector autoregressive moving average (VARMA) models that allows estimation of parameters under the constraints of causality and invertibility. The parameter constraints associated with a causal invertible VARMA model are highly complex. An m-variate VARMA(p; q) process contains (p+q)m2 + m(m+1)/2 parameters, which must be constrained to a complicated subset of the Euclidean space in order to guarantee causality…


Gary T. Green

http://warnell.forestry.uga.edu/nrrt/green.html


Detecting Nonlinear Relationships via Slicing

I will discuss a few recent results from my group aiming to the detection of non-linear dependence and interactive effects of several random variables. These approaches were all developed by taking a Bayesian viewpoint on the inverse-slicing idea first proposed by Ker-Chau Li. We will also show how these methods are applied to bioinformatics problems such as gene-set enrichment analysis, transcriptional regulation analysis, etc. http://en…


David Banks

The last decade has seen substantial progress in topic modeling, and considerable progress in the study of dynamic networks.  This research combines these threads, so that the network structure informs topic discovery and the identified topics predict network behavior.  The data consist of text and links from all U.S. political blogs curated by Technorati during the calendar year 2012.  A particular advantage of the model used in…


Philippe Barbe

http://philippe.barbe.perso.math.cnrs.fr


A Bayesian Multivariate Functional Dynamic Linear Model

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data--functional, time dependent, and multivariate components--we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time…


Yingnian Wu

We propose a learning algorithm for a class of random field models of natural image patterns, where the energy functions of the random fields are in the form of linear combinations of rectified filter responses from subsets of wavelets selected from a given over-complete dictionary. The algorithm consists of the following two components. (1) We propose to induce the wavelets into the random field model by a generative version of the epsilon…


Bo Li

Understanding the complex dynamics of Earth's climate system is a grand scientific challenge. Projecting climate for 50 or 100 years into the future is, however, complicated by the fact that the behavior of the Earth system over such time scales is not well characterized over the modern instrumental interval, which only stretches back about 100-150 years with global extent. Paleoclimate reconstructions using climate proxies such as tree rings,…
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