Past Events

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.

Infectious diseases, human behavior, and health-related information spread via networks of physical, social, and institutional interactions. By understanding and harnessing the structure of these networks, public health agencies can more effectively monitor and control human diseases. Over the last decade, we have developed new network-based mathematical approaches for predicting the spread of infectious diseases and collaborating with public health officials worldwide to apply these methods to address public health challenges.

Abstract: In recent years dynamic spectrum sharing (DSS) has emerged as a key technique for increasing the radio spectrum utilization by accommodating unlicensed devices (UDs) on underutilized licensed (primary) frequencies. The first authorised application of DSS is specified in recent regulations issued by the Federal Communications Commission (FCC) for operation of UDs on DTV channels. The FCC regulations implement interference control from co-channel UDs by a UD exclusive region (protection region) around the primary system.

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As signal acquisition systems become increasingly interconnected, complex and diverse, new methods of data analysis and processing have become crucial. Often, the development of such methods benefits from modeling the inherent structure present in data.

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