Prof. Raj Nadakuditi receives 2012 SPS Young Author Best Paper Award

Prof. Raj Nadakuditi has been selected by the IEEE Signal Processing Society (SPS) to receive the 2012 SPS Young Author Best Paper Award based on his 2008 research paper, “Sample Eigenvalue Based Detection of High-Dimensional Signals in White Noise Using Relatively Few Samples,” co-authored by Dr. Alan Edelman.

Recipients of the SPS Young Author Best Paper Award must be younger than 30 years of age, and have published an “especially meritorious paper” in one of the Society’s Transactions within the past three years preceding the award date.

As stated in the abstract of the paper, “The detection and estimation of signals in noisy, limited data is a problem of interest to many scientific and engineering communities. We present a mathematically justifiable, computationally simple, sample-eigenvalue-based procedure for estimating the number of high-dimensional signals in white noise using relatively few samples.”

The research has applications to biomedical signal processing, wireless communications, geophysical signal processing, array processing, and finance.

Prof. Nadakuditi will receive the award during the 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), held in Kyoto, Japan on March 25-30, 2012.

About Prof. Nadakuditi

Prof. Nadakuditi conducts research at the interface of statistical signal processing and random matrix theory with applications such as sonar, radar, wireless communications and machine learning.

He received his Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), and the MIT-Woods Hole Oceanographic Institution (WHOI) Joint Program in Oceanography and Applied Ocean Science, in 2007. Before coming to Michigan in 2009, he was a post-doctoral research associate at MIT. He is a recipient of the Office of Naval Research Young Investigator Award.

He has taught both undergraduate and graduate courses, namely EECS 451 (Digital Signal Processing and Analysis), EECS 551 (Mathematical Methods for Signal Processing), and the graduate level course Random Matrix Theory and Applications, which he developed.