Machine Learning

Research projects in ML include methods for learning under resource constraints (BudgetML), anomaly detection, topic modeling & ranking, zero shot learning. Applications of interest include problems arising in video analytics, hardware electronics, medical diagnosis and sensor networks.

Structured Signal Processing

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.

Vision & Learning

The goal of this effort is to develop statistical learning methods in the context of applications arising from visual media.  Fundamental issues such as visual ambiguity and spatial distortion arise in the context of vision that must be accounted for while developing statistical learning methods. We are particularly interested in interactions that arise as a consequence of multiple views. These problems include zero shot learning and person re-identification.