In recent years, we have come under mounting pressure to accommodate massive amounts of increasingly high-dimensional data. For example, note the explosion in the quantity of high-resolution audio, imagery, video, and other sensed data produced by relatively inexpensive mobile devices. Similar scenarios arise in such diverse application areas as medical and scientific imaging, genomic data analysis, and digital communications. Despite extraordinary advances in computational power, such high-dimensional data continue to pose a number of challenges. Fortunately, in many cases, these high-dimensional data have relatively few degrees of freedom. For example, data can often be represented as sparse or compressible in a known basis or dictionary. Exploiting such structure is critical to any effort to extract information from such data. This syposium will provide an overview of recent developments in compressive sensing, a new approach to acquisition of sparse data. Compressive sensing provides a flexible framework for designing hardware and algorithms that can tackle previously intractable problems in applications including cameras and imaging systems, tomography, biological testing, radio receivers, communication systems, and networks. Speakers will highlight a diverse range of applications, while also providing an overview of the underlying mathematical theory.