Saligrama elected IEEE Fellow

# Structured Signal Proc. & Learning

The goal of this project involves developing statistical signal processing methods in the context of structured information, a problem arising in many applications. Structure can be in the form of linear/non-linear/Boolean sensing operators, or in the form of signal structures such as sparsity, graph/group structures or low-rank information.

Selected Publications:

Sparse Signal Processing with Linear and Non-Linear Observations: A Unified Shannon Theoretic Approach (http://arxiv.org/pdf/1304.0682.pdf)

Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery (http://arxiv.org/pdf/1508.05565v2.pdf)

Learning Mixed Membership Mallows Models from Pairwise Comparisons (http://arxiv.org/pdf/1504.00757.pdf)

Learning Immune-Defectives Graph through Group Tests (http://arxiv.org/pdf/1503.00555.pdf)

Minimax Optimal Sparse Signal Recovery with Poisson Statistics (http://arxiv.org/pdf/1501.05200.pdf)

Efficient Minimax Signal Detection on Graphs ( http://arxiv.org/pdf/1411.6203.pdf )