Research > Information Theory & 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.
Recent Research Talks
User-Friendly Tools for Random Matrices I - Joel Tropp 9/9/13
Relative Entropy Relaxations for Signomial Optimization - Venkat Chandrasekaran 9/29/14
ISL Colloquium - Babak Hassibi 10/25/12
EE 111. Signal-processing systems and transforms.
EE 112. Introduction to digital signal processing.
ACM/EE 116. Introduction to stochastic processes and modeling.
EE/Ma/CS 126 ab. Information theory.
EE/Ma/CS 127. Error correcting codes.
EE 128 ab. Selected topics in digital signal processing.
CS/EE/Ma 129abc. Information and complexity.
CS/EE 143. Communication networks.
CS/EE 144. The ideas behind out networked world.
CS/EE 145. Projects in networking.
CS/EE 146. Advanced networking.
CS/EE 147. Network performance analysis.
EE/CNS/CS 148ab. Selected topics in computational vision.
CS/CNS/EE 155. Machine learning and data mining.
CS/CNS/EE 156ab. Learning systems.
CS/CNS/EE 159. Projects in machine learning and AI.
EE 160. Communication-system fundamentals.
EE 161. Wireless communications.
EE 163ab. Communication theory.
EE 164. Stochastic and adaptive signal processing.
EE 167. Data compression.
ACM/CS/EE 218. Statistical inference.
EE 226. Advanced information and coding theory.
CS/CNS/EE 253. Special topics in machine learning.