University of Iowa will purchase computer equipment (an Atipa
64-bit Dual-Core Opteron Beowulf cluster for intensive computation),
which will be used for the following research projects: Extensions
and applications of the smoothly mixing regression model (John Geweke);
Regularization methods for high-dimensional regression models,
with applications to survival analysis, microarray normalization and
ROC classification (Jian Huang); Parallel statistical computations in R,
and Bayesian analysis of PET imaging data (Luke Tierney); Bayesian models
and parallel and grid-based computing strategies for high-dimensional
spatial and spatiotemporal data (Jun Yan and Mary Kathryn Cowles).
These projects will make substantive contributions in Bayesian econometrics;
statistical genetics; statistical research, practice, and education; brain
imaging; and environmental statistics. For education, the new cluster
will be used for graduate courses on computational statistics and
interdisciplinary seminar courses on environmental informatics, as well
as in thesis research by individual graduate students. The cluster will
be integrated into the University of Iowa's research computational Grid
(HawkGrid), which is a node of the Open Science Grid. Thus, the cluster
will contribute to broader research on Grid computing methodology, and,
when not being used to capacity by researchers in the Statistics and
Actuarial Science Department, it will be a research computing resource for
the greater University of Iowa community and for users of the Open Science
Grid.
The last decade has witnessed an explosion of available data -- from
satellite images and medical images to remote sensing data to databases
of customer information. This phenomenon has been accompanied by an
increase in the complexity of questions that people in government,
business, and the sciences need answered. Five researchers from the
Department of Statistics and Actuarial Science at the University of Iowa
are developing and applying statistical computing methods to use massive,
complex data to answer real-world questions. John Geweke's work addresses
socio-economic issues, such as what factors drive the price of gasoline.
Jian Huang's work involves collaboration with medical researchers in the
Cancer Center at the University of Iowa and will contribute to the
development of more effective methods for using genetic information
in the diagnosis and treatment of cancer. Luke Tierney develops
methods to help people understand data by exploring it visually as well as
statistically. This work will assist educators, researchers, and users of
data in business, government, and any other fields. Tierney also is
involved in the statistical side of brain imaging using PET technology.
Jun Yan and Mary Kathryn Cowles are working to improve statistical
computing strategies for data measured over space and time, and are
using their methods to study changes in the available water supply in
the western United States and levels of radon gas (a risk factor for lung
cancer) in buildings.
