We focus on the analysis of complex, high-dimensional data.

 We combine elements of Bayesian inference, information theory, optimization, and physical sensor models to develop scalable algorithms with theoretical performance guarantees. Application areas include multi-modal data fusion, distributed inference under resource constraints, structural inference, resource management in sensor networks, and analysis of video, seismic volumes, and radar images.