Scientific Computing Research Environments for the Mathematical Sciences (SCREMS)

The Department of Statistics at Pennsylvania State University proposes the implementation of a computing environment for the statistical sciences, supporting research projects of five faculty.These projects share a common requirement for high-speed computing with applications including astronomy, image analysis, longitudinal data analysis, Markov Chain Monte Carlo (MCMC) algorithms, mixture inference and environmental science. Massive data sets from astronomy, images and ecology and geoscience require high-capacity disk storage and the corresponding analyses benefit greatly from fast, parallelized computing resources.

The work in astronomy will enhance and maintain the web computing environment `VOStat', which will allow astronomers to easily conduct a variety of statistical analyses on massive (terabyte scale) data. Automated annotation of digital pictures is a highly challenging technology with significant applications. Real time algorithms will be developed that use sophisticated statistical classification techniques to automatically classify images. This kind of technology can greatly improve upon image searches provided by major search engines. Longitudinal studies involve data with repeated observations on experimental units over time and require specialized statistical methods. New statistical approaches will be investigated for such studies. Mixture models are very flexible ways to model complex data. A number of theoretical and practical issues will be investigated in the context of mixture modeling, particularly as applied to massive data sets. MCMC algorithms are computational tools for fitting realistic models to complex data. New MCMC algorithms will be developed and studied in the context of models for data that are geographically referenced. These computationally intensive algorithms will also be used in collaborative work with Geography,Ecology and Climatology at Penn State.