The School of Computing at the University of Utah has research efforts in Image Analysis, Scientific Computing and Visualization. Many research activities in Applied Computation overlap those in Computer Graphics.
Research
Image Analysis
The School of Computing's research in image analysis addresses fundamental questions in 2D and 3D images and applications in a variety of fields including energy, defense, biology, and medicine. This research spans a wide range of areas including new methods for low-level image processing, such as filtering, segmentation, and surface reconstruction. Work on image analysis also focuses on statistical methods for analyzing shapes and applications of these methods to problems in medicine and biology. The work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). A particularly important application area is neuroimage analysis, and collaborations with the University of Utah Brain Institute focus on disorders and diseases such as autism and Altzeimers. Examples are the analysis of diffusion-weighted MRI images for quantifying brain connectivity, the analysis of longitudinal pediatric images to understand brain development, and analysis of functional data for understanding brain function and the affects of disease. These applications drive the development of new methods for image processing, shape representation and analysis, and computational statistics.
Scientific Computing
The Scientific Computing faculty within the School of Computing perform cutting edge research in all of the aspects of the scientific computing pipeline: mathematical and geometric modeling; advanced methods in simulation such as high-performance computing and parallelization; numerical algorithm development; scientific visualization; and error quantification and evaluation. The School of Computing has scientific computing research efforts in a wide variety of areas, including adaptive methods, inverse and imaging problems, numerical analysis, uncertainty and error quantification, distributed and parallel computing, problem solving environments, integral methods, Monte Carlo algorithms, computational complexity and computational science applications. Students at both the undergraduate and graduate level working under faculty guidance are able to apply this knowledge to real-world problems in important scientific disciplines, including combustion, mechanics, geophysics, fluid dynamics, biology, and medicine. A collaborative base provides students with tremendous flexibility to seek out science which interests them, and strong mentoring from scientific computing track faculty enables students to mature as scientists.
Visualization
Scientific visualization, sometimes referred to as visual data analysis, is the graphical representation of data as a means of gaining understanding and insight into the data. Scientific visualization research at Utah 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. To comprehend spatial and temporal relationships between data, interactive techniques provide better cues and therefore, much of the scientific visualization research focuses on better methods for visualization and rendering at interactive rates.
Graduate Programs
Computational Engineering and Science Program
