|The SCI Institute|
Over 15 years on the cutting edge
The SCI research group was founded in 1994 by Drs. Chris Johnson and Rob MacLeod along with five graduate students. In 1996, they became the Center for Scientific Computing and Imaging and in 2000, the SCI Institute. The Scientific Computing and Imaging (SCI) Institute is now one of eight permanent research institutes at the University of Utah and home to over 200 faculty, students, and staff. The 16 tenure-track faculty are drawn primarily from the School of Computing, Department of Bioengineering, Department of Mathematics, and Department of Electrical and Computer Engineering, and virtually all faculty have adjunct appointments in other, largely medical, departments. Recent growth in the SCI Institute has come in part from the award in 2007 from the state of Utah of a USTAR (Utah Science and Technology Advanced Research) cluster in Imaging Technology. This allowed the Institute to recruit three new faculty in image analysis: Professors Guido Gerig, Tom Fletcher, Tolga Tasdizen. During this same time period, they were also able to recruit Professor Valerio Pascucci in visualization. In 2011, USTAR funding allowed two more: Orly Alter who specializes in genomic signal processing and Miriah Meyer, who's novel biological visualization tools are revolutionizing the way scientists view and understand their data. In 2012, the SCI Institute recruited Dongbin Xiu as its latest faculty member. Dongbin is one of the most recognized names and highly cited researchers in the area of uncertainty quantification, and will make a wonderful addition to the Institute.
Over the past decade, the SCI Institute has established itself as an internationally recognized leader in visualization, scientific computing, and image analysis applied to a broad range of application domains. The overarching research objective is to conduct application-driven research in the creation of new scientific computing techniques, tools, and systems. An important application focus of the Institute continues to be biomedicine, however, SCI Institute researchers also address challenging computational problems in a variety of application domains such as manufacturing, defense, and energy. SCI Institute research interests generally fall into the areas of: scientific visualization, scientific computing and numerics, image processing and analysis, and scientific software environments. SCI Institute researchers also apply many of the above computational techniques within their own particular scientific and engineering sub-specialties, such as fluid dynamics, biomechanics, electrophysiology, bioelectric fields, parallel computing, inverse problems, and neuroimaging.
The academic programs available for students are outstanding. The School of Computing has collaborated with faculty in the SCI Institute to create a graduate degree in Computing, which offers tracks in Scientific Computing and Graphics (Image Analysis is planned). The physical infrastructure is also outstanding with many large-scale computing facilities at the disposal of students and trainees, perhaps most exciting is the new NVIDIA computing cluster, which, along with a new graduate course in Parallel Programming for GPUs, provides opportunities for developing unique expertise in large-scale streaming architectures. SCI faculty also provide leadership in developing educational and research tracks in biomedical engineering through the Bioengineering Department. There are undergraduate and graduate tracks in computing and imaging, in part created and directed by SCI faculty. There is also a graduate track in cardiac electrophysiology and biophysics, directed by SCI faculty and supported through collaboration between SCI and the Cardiovascular Research and Training Institute (CVRTI).
Perhaps most encouraging is the general atmosphere provided by the SCI Institute and its more than 200 members, all dedicated to some aspect of scientific computing. There is extensive expertise within the SCI Institute that covers all the topics required for simulation, modeling, and visualization including high performance computing, efficient numerical algorithms, large data management and storage, database management, and scientific visualization of all forms of scalar, vector, tensor, and volume data.
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