"Modern communication networks are constantly growing in size, speed and sophistication. Applications such as streaming multimedia and cloud computing consume ever-increasing amounts of bandwidth in wireless networks and the Internet. New kinds of networks are being built for sensing, communication and coordination in applications as diverse as transportation, security, power grids, and infrastructure monitoring. All these applications demand a high degree of reliability and energy efficiency, in addition to having low delay tolerance. To meet these demands in the face of rapidly growing data volume, it is critical to have fast, rate-optimal codes for data transmission and compression.The project aims to develop low-complexity, rate-optimal codes using the framework of high-dimensional sparse regression. Using the sparse regression methodology, we will construct codes whose rates approach the optimal information-theoretic limits with low-complexity coding algorithms for a large class of communication and compression problems. This class includes Gaussian channels and sources, which are important in practice. First, the codes will be designed for the basic problems of point-to-point communication and lossy compression. The codes will then serve as building blocks which can be combined to implement coding schemes for various network settings involving distributed communication and compression. The final goal, therefore, is to develop a library of low-complexity, rate-optimal codes for a variety of network models such as multi-access, broadcast, and interference channels."