The faculty and students in the University of Georgia Statistics Department are involved in exciting research projects, many of which are computationally intensive. The current computing equipment is inadequate to meet the growing needs of the research, which includes functional neuro-imaging analyses, modeling the ecology of the genome, Bayesian empirical likelihood methods, methodology for symbolic data, and high-dimensional data reduction. Both theoretical research and interdisciplinary applications involve computational work. Research into the genome and brain imaging requires large data storage capacities as well as computational speed. Research into new statistical methodologies often requires extensive, computationally intensive simulations. For example, the calculation of posterior distributions for Bayesian Empirical Likelihood applications is quite computationally intensive, and lends itself well to a parallel computing environment. One of the developing methods for high-dimensional data reduction involves sequential quadratic programming, which could be many times faster when programmed in parallel. Each of the enumerated research projects will benefit greatly from the new cluster. Many PhD students are involved in the projects; training in the most modern computing facilities will enhance students' job prospects after graduation, and better computing resources will make the department more attractive for recruiting and retaining top-quality students.
The acquisition of a state-of-the-art Linux cluster is necessary for the researchers at the University of Georgia Statistics Department to realize the full potential of their efforts. Computationally intensive research projects for six faculty members are enumerated in the project description. These projects entail both theoretical and interdisciplinary research, including development of new methodologies for use in brain imaging, modeling of the ecology of the genome, and inference about climatological time series. Theoretical advances and real-world applications form a symbiotic relationship, as each activity nourishes the other. Often research into new statistical methodologies involves computationally intensive simulations and/or very large datasets. For example, to better understand the workings of the human brain, research in functional neuro-imaging involves datasets containing images for many individuals under various stimuli. Methodologies must model variation across individuals and across stimuli, as well as within each brain, to determine relationships between function and activity. Similarly, research into the human genome involves enormous amounts of data and computationally intensive analyses. New directions in theoretical research can be suggested by intensive computer work. The current computing equipment in the department is limiting the scope of the projects currently in hand, and is inadequate for the new directions envisioned by a young and active faculty.
