Research > Information Theory & Signal Processing

Mathematical Signal Processing


Mathematical signal processing focuses on rigorous mathematical methods for representing data and computer algorithms for manipulating these representations. This field grew out of Fourier and wavelet analysis. Modern research directions include sparse representation, low-rank matrix models, diffusion geometry, and more. Applications range from data compression and image processing to machine learning and sensor networks. This field is sometimes known as applied and computational harmonic analysis.

CMS has a distinguished tradition in mathematical signal processing. Our faculty did pioneering work on multirate filter banks. We helped develop the lifting scheme for building wavelets on manifolds. The field of compressed sensing was born here. Our researchers introduced powerful algorithms for sparse and low-rank representation. Recently, we have established machinery for studying phase transitions in convex optimization problems with random data.


Venkat Chandrasekaran, Babak Hassibi, Tom Hou, Pietro Perona, Peter Schröder, Andrew Stuart, Joel Tropp, PP Vaidyanathan

Related research groups & Centers > CMI, CD3, DOLCIT, EE, Hassibi Group

Recent Research Talks

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