Speaker: Dr. Shirin Jalali, Nokia Bell Labs
Title: Compression Codes for Efficient Data Acquisition
Abstract: With more than a century of research and development, data compression is a relatively mature field with impressive practical results on one hand, and a solid information-theoretic background on the other. Commercial image and video compression codes are carefully-designed algorithms that take advantage of intricate structures that exist in natural images or video files to encode them as efficiently as possible. On the other hand, exploiting a signal's structure to design more efficient data acquisition systems, as done in compressed sensing or phase retrieval, is a relatively new research endeavor with its actual impact starting to emerge in few industries. However, comparing signal structures used by typical data compression codes with those used by modern data acquisition systems readily reveals the big gap between the two. This motivates us to ask the following question: can we design a data acquisition algorithm that employs an existing compression code as a mechanism to define and impose structure? An affirmative answer to this question potentially leads to much more efficient data acquisition systems that exploit complex structures, much beyond those already used in such systems. In this talk, we focus on addressing this question and show that not only the answer to this question is theoretically affirmative, but also there are efficient compression based recovery algorithms that can achieve state-of-the-art performance, for instance, in imaging systems.
Bio: Shirin Jalali is a research scientist in the Mathematics and Algorithms Group at Nokia Bell Labs in Murray Hills, NJ. Prior to that she held positions as a research scholar at the department of Electrical Engineering at Princeton University and as a faculty fellow at NYU Tandon School of Engineering. She received her B.Sc. and M.Sc. degrees in Electrical Engineering from Sharif University of Technology. She obtained her M.Sc. in Statistics and Ph.D. in Electrical Engineering from Stanford University. Her research interests are in the areas of information theory, high-dimensional statistics and machine learning.