Chandra Murthy -- Role of Sparse Signal Recovery in Big Data Analytics
Channel: Big Data Initiative at CSA (IISc)
Date: 2014-08-31

On June 10, 2014, we had Prof. Chandra Murthy speak about big data from the perspective of signal processing. "Big" data generally tends to be big but sparse, meaning that the true content itself is small but is embedded in an unknown part of the signal. The question is whether we can process the data without explicitly combing through it to find the data. Sparse signal recovery is by now a fast-developing field of research, and Prof. Murthy gave an overview of the various techniques and applications. 


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Abstract: In this talk, we start with providing a brief overview of some of the signal processing challenges that arise in big data analytics. We then discuss the mathematical models that are commonly employed to address these challenges, and argue that sparsity and sparse signal recovery methods naturally arise as promising solutions to a variety of big data problems. We also discuss some of the recent sparse signal recovery algorithms that may be applicable to big data. We present example studies on the use of these techniques in distributed sparse signal recovery, spectrum cartography, and, time permitting, wideband channel estimation in wireless communications. 

Speaker Bio: Chandra R. Murthy is an associate professor in the Dept. of ECE, Indian Institute of Science, Bangalore. His research interests are in the areas of sparse signal recovery, cognitive radio systems, energy-harvesting based communications and multiple antenna systems with channel-state feedback.