Past Events

In high speed network measurement, such as in core Internet routers, there may only be a few nanoseconds available per packet for measurement purposes.

This work studies the problem of sequentially recovering a sparse vector x_t and a vector from a low-dimensional subspace l_t from knowledge of their sum m_t=x_t+l_t. If the primary goal is to recover the low-dimensional subspace where the l_t's lie, then the problem is one of online or recursive robust principal components analysis (PCA). An example of where such a problem might arise is in separating a sparse foreground and a slowly changing dense background in a surveillance video.

Topic to be determined.

Several general-purpose deterministic global optimization algorithms have been developed for mixed-integer nonlinear optimization problems over the past two decades. Central to the efficiency of such methods is their ability to construct sharp convex relaxations. Current global solvers rely on factorable programming techniques to iteratively decompose nonconvex factorable functions, until each intermediate expression can be outer-approximated by a convex feasible set.

Traditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands.

Online social networks like Facebook, Twitter, Quora, etc have greatly influenced our social lives. This effect extends to economic and political realms, where social networks have become the best media for targeted campaigns of products and political ideas. From these perspectives as well as from the perspective of social sciences understanding the evolution of opinions and its effect on societies is of great importance. In the first part of this proposal we study opinion evolution in a social network.

A basic problem in the design of privacy-preserving algorithms is the private maximization problem: the goal is to pick an item from a universe that (approximately) maximizes a data-dependent function, all under the constraint of differential privacy. This problem is central to many privacy-preserving algorithms for statistics and machine learning.Previous algorithms for this problem are either range-dependent---i.e., their utility diminishes with the size of the universe---or only apply to very restricted function classes.  Prof.

Millimeter wave communication is coming to a wireless network near you. Because of the small antenna size and the need for array gain, array processing is important in millimeter wave communication systems.  This presentation provides an overview of millimeter wave communication systems. Particular attention is paid to the ways that MIMO communication has played a role in the past and how it may play a role in the future.

This year, Professor Alexandros Dimakis with the WNCG will give the Plenary Talk at the Université de Bordeaux's Algebra, Codes and Networks Conference. For more information, visit the ACN 2014 website.

ACM Sigmetrics is the flagship conference of the ACM special interest group for the computer systems performance evaluation community. SIGMETRICS is one of the premier conferences addressing the development and application of state-of-the-art, broadly applicable analytic, simulation and measurement-based performance evaluation techniques. This year, Dr. Sujay Sanghavi and Dr. Sanjay Shakkottai of the WNCG will serve as ACM SIGMETRICS General Chairs.