National Science Foundation Funds Claremont McKenna College Mathematics Professor to Research Compressive Signal Processing

CLAREMONT, Calif.--()--Claremont McKenna College assistant professor of mathematics Deanna Needell has been awarded a prestigious, five-year National Science Foundation CAREER grant of more than $413,000 for her research on the practical application of compressive signal processing (CSP). The grant, from the NSF’s Faculty Early Career Development Program, supports junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and integration of education and research within the context of the mission of their organizations.

Compressive signal processing (CSP) is a new and growing field of mathematics that attempts to resolve inefficiencies of traditional signal acquisition. Needell’s work provides novel approaches to CSP including but not limited to video surveillance, face recognition, latent semantic indexing, and radar accuracy.

The technology provides a mathematical framework to tackle the problem of capturing and analyzing abundant amounts of data, such as medical images of people’s brains. Its goal is to provide a mathematical theory that shows how large-scale critical data can be acquired and analyzed using far fewer samples, measurements, and/or memory.

“Most people assume a higher pixel count means a higher quality camera,” Needell says. “But what they may not realize is that the majority of the captured pixels are discarded as the camera compresses the image for storage. This research seeks to create a single sampling process that only captures the important information.”

The field of compressive sampling aims to resolve this dilemma through the idea of dimension-reduction—meaning the size of a compressible object could be reduced without sacrificing most of its information.

“One simple example of CSP is to use random projections of images, to allow photographers to use a single-photon detector and take far fewer measurements than traditional digital cameras, without sacrificing an accurate image. This is a remarkable advancement, because in other types of cameras, such as infrared, or those used in medical imaging, the cost of each measurement can be extremely high.”

Needell says an exciting application of compressive sampling has been implemented in a “single-pixel digital camera” at Rice University. The Stanford Medical Center is currently testing the compressive sampling technology in pediatric Medical Resonance Imaging (MRI). CSP can dramatically reduce the scan time of an MRI—from 45 minutes to eight minutes—without sacrificing image quality. This is significant, especially in the pediatric setting, when a young patient may not be able to remain still or tolerate sedation.

“This technology could lead to decreased costs, reduced sedation, less power consumption, and more accurate diagnoses, all through the power of beautiful mathematics,” Needell says.

Needell earned her Ph.D. from the University of California, Davis and joined Claremont McKenna in 2011. Her research interests include numerical analysis, geometric functional analysis, statistics, probability, applications to computer science, scientific computing, and—in particular—the field of compressed sensing.

Earlier this year, she won an Alfred P. Sloan Research Fellowship, given to early-career scientists and scholars of “outstanding promise,” in recognition of distinguished performance and a unique potential to make substantial contributions to their field. In 2013, she was the recipient of a Simons Foundation Collaboration Grant for Mathematicians.

Contacts

Colehour + Cohen
Sian Wu, 1-206-262-0363 x115
swu@colehourcohen.com

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Contacts

Colehour + Cohen
Sian Wu, 1-206-262-0363 x115
swu@colehourcohen.com