Our site has moved!Please visit our new site at SISTA
The School of Information: Science, Technology, and Arts has classes and programs that help you gain a deeper understanding of the role of computing and digital information in any major by placing it within the context of The Information Age - giving you that extra edge in today's and tomorrow's job market. SISTA's classes are open to all majors and we offer interdisciplinary BS and BA degrees, a minor, and excellent opportunities for undergraduate research.
Computing is a foundation for research in the sciences, engineering, humanities and arts. Computing means more than building or programming computers: It means solving problems in an algorithmic way, or thinking about problems as if computers will implement the solutions. SISTA’s mission is to provide expertise and promote research in computational methods and thinking across disciplines; and to teach students to understand the computational aspects of any discipline.
SISTA Research Mission: SISTA is a home for interdisciplinary work at the University of Arizona (see our research page for examples). We welcome all students, faculty and others who want to start new projects or participate in current projects.
In Fall 2010 we began the campus-wide SISTA Colloquium Series. Since then we have featured two dozen speakers from 20 departments and programs. Please join us for talks about computation in disciplines from all over campus.
Visit our About page to learn more of what SISTA has done in its first two years.
|Jan 17:||SISTA Art Show|
|Jan 13:||UA News : SISTA Art Show|
|Dec 05:||SISTA Associate Director, Clayton Morrison talks about SISTA in "The Journal"|
|Oct 31:||SISTA features Harold Cohen, Distinguished Speaker|
|Oct 19:||SISTA team of researchers receive $3M grant|
Vision Research: Computer Vision
The SISTA computer vision group studies automatically inferring semantics from image data in a wide range of contexts including fundamental research into scene understanding, inferring human activity from video data, extracting 3D structure from biological images, and improving access to image and video data. We take a statistical modeling approach where we build and/or learn models that explain image data based upon high level representations of objects, scenes, and activities. We then use Bayesian inference for interpreting visual data as evidence for what is in the world with respect to those representations. The vision group has roughly a dozen active projects. Undergraduate researchers contribute to many of these.