The has announced a two-year, $1 million grant from the in support of research aimed at improving the way images and large data sets are collected and analyzed in science, engineering, medicine, and other fields.
The grant will support UCLA's "Leveraging Sparsity" project, which aims to expand real-world applications of "compressive sensing," a method that uses mathematical algorithms to reconstruct complex medical and scientific images and data sets precisely from sparse amounts of information. "Our goal is to leverage mathematical advances to transform the way imaging and related data are acquired, analyzed and understood," said lead principal investigator Paul S. Weiss, who directs the university's and holds the Fred Kavli Chair in Nanosystems Sciences. "The result will be richer, more meaningful data [obtained] through significant changes in how experiments are currently conducted and analyzed."
One specific area of research the group plans to pursue is the detection of epilepsy, where conventional analysis is often inadequate. "Medical data are expensive to acquire and frequently are difficult to interpret," said principal investigator Mark Cohen, director of the . "We believe that understanding the ways in which complete data sets — analogous to complete images — can be formed from sparse samples will help us to extract better and more accurate diagnoses from the limited data that can be acquired from people being evaluated for epilepsy."