Professor wins Best Paper Award from IEEE Signal Processing Society

Professor  Namrata Vaswani and her student, Wei Lu won the prestigious Best Paper Award from IEEE Signal Processing Society for their paper “Modified-CS: Modifying Compressive Sensing for Problems with Partially Known Support,  IEEE Trans. Signal Processing, Vol. 58, No. 9, September, 2010.”

Lu was Vaswani’s Ph.D. student when this work was done; he graduated in 2011 and is now a senior algorithms engineer for KLA-Tencor.

The Best Paper Award honors the author(s) of a paper of exceptional merit dealing with a subject related to the Society’s technical scope, and appearing in one of the Society’s solely owned transactions or the Journal of Selected Topics in Signal Processing. Authors are eligible to win within five years. Vaswani and her student published their paper in 2010 and received the Best Paper Award for 2014.

A key application of the ideas proposed in this paper is in recursive recovery of a time sequence of sparse signals which is a problem that Vaswani introduced and first started studying 2008. This problem, which is also referred to as dynamic compressive sensing (CS), occurs in many domains such as medical imaging, astronomy, video analysis, etc. One medical imaging example where dynamic CS can be useful is dynamic magnetic resonance imaging (MRI), which involves non-invasively imaging cross-sections of human organs such as the brain and the heart. By using her “modified-CS” technique, one can potentially reduce the number of measurements needed to create accurate MR images. Since MR data is acquired one Fourier projection at a time, the ability to accurately reconstruct using fewer measurements directly translates into reduced scan times. Shorter scan times along with online and fast reconstruction algorithms, such as modified-CS, can potentially enable real-time imaging of fast changing physiological phenomena, thus making many interventional MRI applications practically feasible.

Vaswani’s research is in the broad area of statistical machine learning and signal processing. In recent years her work has focused on both the theoretical and practical aspects of sequential sparse recovery (compressive sensing), robust principal components’ analysis (PCA) and phase retrieval. Her work has applications in medical imaging, video and other big-data analytics problems and astronomical imaging.

For more information, visit the Signal Process Society’s webpage.