
Numerical simulation of realworld phenomena provides fertile ground for building interdisciplinary relationships. The SCI Institute has a long tradition of building these relationships in a winwin fashion – a win for the theoretical and algorithmic development of numerical modeling and simulation techniques and a win for the disciplinespecific science of interest. Highorder and adaptive methods, uncertainty quantification, complexity analysis, and parallelization are just some of the topics being investigated by SCI faculty. These areas of computing are being applied to a wide variety of engineering applications ranging from fluid mechanics and solid mechanics to bioelectricity.


Scientific visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Scientific visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, and fire simulations. Research involves novel algorithm development to building tools and systems that assist in the comprehension of massive amounts of scientific data. In helping researchers to comprehend spatial and temporal relationships between data, interactive techniques provide better cues than noninteractive techniques; therefore, much of scientific visualization research focuses on better methods for visualization and rendering at interactive rates.


SCI’s imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In lowlevel image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and realtime implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.


Research in the Musculoskeletal Research Laboratories has historically focused on the biomechanics and healing of musculoskeletal soft tissues, in particular the ligaments of the knee. Over the past five years, the research focus has expanded considerably to include hard tissue as well as cardiovascular tissues including the heart, coronary arteries and smaller vessels involved in angiogenesis.


The Information Management group has been working on building new cyberinfrastructure that streamlines the creation, execution and sharing of complex visualizations, data mining and other largescale data analysis applications. We developed VisTrails (www.vistrails.org), a new open source, scientific workflow and provenance management system that was designed to manage rapidly evolving workflows common in exploratory applications. VisTrails provides novel mechanisms for capturing and interacting with provenance that greatly simplify the data exploration process. The system has been downloaded over 8,000 times since its beta release in January, 2007. VisTrails has been adopted as part of the cyberinfrastructure in large scientific projects, as well as a teaching and learning tool in graduate and undergraduate courses, both in the U.S. and abroad.


Graphics research at the SCI Institute is closely tied to our work in scientific visualization and information visualization. This research area focuses on algorithm development where graphics meets large scientific datasets. This area of research also involves the use of new platforms such as the iPad, iPhone, large storage systems such as isilon or the latest generation of graphics processing unit and the creation of tailored algorithms to those platforms. Current projects include the development and refinement of raytracing algorithms, light scatter algorithms, and efficient volume rendering packages such as Tuvok. Graphics research at SCI impacts nearly all of our other research areas.


Research in the area of the GPU is focused on harnessing the power of the GPU for visualization, simulation, image and information processing and analysis, and, of course, for graphics. As the speed and efficiency of graphical processing units (GPUs) grows at rates even faster than those of conventional central processing units (CPUs), there is a growing consensus that the streaming architecture embodied in most modern graphics processors has inherent advantages in scalability. Virtually all the contemporary developers of computer processing units are exploring opportunities for GPU improvement and there is an emerging set of standards and tools that can take advantage of these processors for more general purpose computing than graphics.


In the Genomic Signal Processing Lab at the University of Utah, we develop generalizations of the matrix and tensor computations that underlie theoretical physics, and use them to create models that compare and integrate different types of largescale molecular biological data, such as DNA microarray data, and computationally predict global mechanisms that govern the activity of DNA and RNA. We believe that future discovery and control in biology and medicine will come from the mathematical modeling of such largescale molecular biological data data, just as Kepler discovered the laws of planetary motion by using mathematics to describe trends in astronomical data. We pioneered the use of the matrix singular value decomposition (SVD), the tensor higherorder SVD (HOSVD) and their generalizations in modeling different types of genomic data from different studies of cell division and cancer and from different organisms. Our recent experimental results verify our computational prediction of a mechanism of regulation that correlates DNA replication origin activity with mRNA expression, demonstrating for the first time that mathematical modeling of DNA microarray data, in which the mathematical variables and operations represent biological reality, can be used, beyond classification of genes and cellular samples, to correctly predict previously unknown global biological mechanisms. We now extend our recent computational results, modeling data from the Cancer Genome Atlas, to formulate and implement a protocol for the utilization of recent global profiling biotechnologies in the computational prognosis of cancers. Ultimately, our work will bring physicians a step closer to one day being able to predict and control the progression of cancers as readily as NASA engineers plot the trajectories of spacecraft today.

