Past Events

AbstractThe performance of complex systems depends on joint decision-making by a large number of components. As the system size increases, designs that require global coordination do not scale well, while designs based on just local knowledge perform poorly. Using examples from networking and medical imaging, I will show that global objectives can be achieved via simple local rules that exchange the right amount of information.The first application concerns data measurement in networks. Networks collect detailed statistics on the data (bytes, packets, etc) sent by each flow.

Abstract We are ushering into the era of ubiquitous large-scale complex systems that will offer high-speed data communication, reliable low-power data storage and efficient data inference -- all occurring on a massive scale. An indispensable step towards a systematic innovation in such high-performance systems is the identification of the appropriate performance metric, and the subsequent development of a methodology for efficient system evaluation based on this metric.

AbstractThe emergence of pervasive sensing, high bandwidth communications and inexpensive data storage and computation systems makes it possible to drastically change how we design, monitor and regulate very large-scale physical and human networks. Performance gains in the way we operate these networks translate into large savings. There are many critical challenges to create functional monitoring systems, such as data reliability, computational efficiency and proper system design, including choices of sensors, communication protocols, and analysis approaches.

Abstract:Cognitive radio is a promising technology to mitigate spectrum shortage in wireless communications. It enables secondary users (SUs) to opportunistically access low-occupancy primary spectral bands as long as the primary user (PU) access is protected. PU protection is vital to the success of cognitive radio system because no PU would be inclined to accommodate secondary cognitive networks without such assurance.

AbstractAs networks proliferate in diverse fields of modern endeavor, so grows the need to control them on a large scale (e.g. in wireless or sensor networks), and interpret the high-dimensional data they generate (e.g. in social or biological networks). In this talk I present a broad-based approach to both challenges, based on a popular, but currently unrelated, formalism for multivariate statistical inference: Markov random fields (MRFs).The popularity of MRFs derives from the empirical success of graph-based heuristics, like Belief Propagation (BP), in performing basic statistical tasks.