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Wireless ad hoc networks (WANETs) allow users to communicate, sharing the same wireless channel, without the need of any infrastructure. The performance of wireless ad-hoc networks (WANET) is mainly limited by its self-interference. The talk will focus on the performance of WANETs applying slotted carrier sense multiple access (CSMA) mechanism and possibly utilizing also directional antennas.

Traditionally, channel estimation has been undertaken only in service of better data communication. However, a number of problem frameworks (sonar, cognitive radio, digital watermarking) require the reconstruction of a transmitted message as well as estimating properties of the channel over which the message was transmitted. We abstract these scenarios to one wherein a source sends a message to the destination and the destination endeavors to both decode the message and estimate the channel to some fidelity.

Rank aggregation is the problem of combining multiple candidate rankings into one list that best reflects the candidates' standing as a whole. Rank aggregation has many applications,in fields as diverse as bioinformatics, coding theory and social sciences.Mathematically, the rank aggregation problem can be formulated as finding a permutation thatrepresents the ``centroid'' of a set of permutations - i.e., a permutation that minimizes the averagedistance from the given set of permutations.

Modern systems must trade off traditional performance goals with energy concerns, e.g., running faster lowers delays but increasespower usage. However, while there are well-developed theories and models for discussing computation, communication, and memory demandsof algorithms/systems, a theory for discussing the energy efficiency of an algorithm/system is only developing. In this talk, I willdescribe some recent work toward this goal, which focuses on dynamic capacity provisioning in data centers.

"If we know more, we can achieve more." This adage also applies to communication networks, where more information about the network state translates into higher sum rates. In this talk, we formalize and investigate this increase of sum-rate with increased knowledge of the network state. In particular, we focus on the case that each source-destination pair has enough information to perform optimally when other pairs do not interfere, however beyond that they only know the connectivity of the network (i.e., not the channel gains).

Reconstructing a high-dimensional sparse vector from a small number of observationsis a well-studied problem in many scientific, economic and engineering disciplines, anda number of tools have been designed to address this problem. It is currently experiencinga resurgence due to new applications, such as data-driven medicine and online advertising,and due to the need for accurate predictions under time and complexity constraints.

Camera-phones have come a long way from when they debuted as gimmicks. Today camera-phone pictures have become usable not only for web consumption but also for high resolution displays and prints. However, image quality challenges remain due to form factor, cost, and market constraints.Despite the constraints, the camera-phone provides unprecedented opportunity through the computational capability available and the programmable platforms that are available to harness that compute power.

Traditional cameras use lenses to form an optical image of the scene and thus obtain spatial correspondences between the scene and the film or sensor array. These cameras do not sample the incident light fast enough to record any transient variations in the light field. This talk introduces diffuse imaging -- a signal processing framework for imaging using only omnidirectional illumination and sensing. We show that it is possible to construct images by computationally processing samples of the response to a time-varying illumination.

The nation's power grid is perhaps the largest and most dynamic man-made network. Thesmart grid envisaged is that many renewable energy sources (such as wind and solar) will beadded to the mega-scale smart grid. Integrating a significant amount of wind energy into thebulk power grid, however, has put forth great challenges for generation planning and systemreliability, because wind generation is highly variable and non-dispatchable.