Past Events

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. 

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.

Many websites rely on user-generated content to provide value to consumers. These websites often incentivize participation by awarding users badges based on their contributions. In this paper, we consider the design of badgemechanisms that maximize total contributions. Users exert costly effort to make contributions and, in return, are awarded with badges. Abadge is only valued to the extent that it signals social status and thus badge valuations are determined endogenously by the number of users who earn eachbadge. 

After a lengthy job search, WNCG student Zak Kassas, who is co-advised by Profs. Todd Humphreys and Ari Araposthathis, was offered tenure-track positions at universities across the world. He ultimately accepted an a position with University of California at Riverside. Kassas' search process was fairly exhaustive and he compiled many valuable lessons. Join Kassas as he shares these lessons in a seminar targeted towards UT Cockrell School graduate students with similar aspirations.

This workshop is part of a month-long program on kinetics, non standard diffusions and stochastics that involves senior and junior researchers, postdoctoral associates and graduate students at The University of Texas at Austin. The program is organized by WNCG Professor François Bacelli and is open to the public.

Revenue approximation results for auctions have been limited to settings in which analytically solving for equilibrium is feasible: in particular, truthful auctions or symmetric settings of non-truthful auctions. We use a price of anarchy style approach similar to the smooth mechanisms framework of Syrkganis and Tardos [2013] to derive revenue approximation bounds in Bayes-Nash equilibrium without needing

Inter-cell interference coordination (ICIC) and intra-cell diversity (ICD) play important roles in improving cellular downlink coverage. ICIC ensures that users in nearby cells are assigned with orthogonal resource blocks (RBs). ICD transmit the same information to the users using multiple RBs and allow the user to combine different versions of the packet. While the idea behind ICIC and ICD are quite different, they share the important property of improving the user experience by assigning additional resources and thus are comparable. ​

Join WNCG student Chao Jia for a PhD defense. Video data has increased dramatically in recent years due to the prevalence of handheld cameras. Such videos, however, are usually shakier compared to videos shot by tripod-mounted cameras or cameras with mechanical stabilizers. In addition, most handheld cameras use CMOS sensors. In a CMOS sensor camera, different rows in a frame are read/reset sequentially from top to bottom. When there is fast relative motion between the scene and the video camera, a frame can be distorted because each row was captured under a different 3D-to-2D projection.

CMOS technology scaling has fueled tremendous progress in electronics and has brought about system-on-chip (SoC) products with a broad impact on our society and economy. Technology scaling is very beneficial to increase the performance and density for digital signal processing, computation and memory. Analog and radio-frequency (RF) circuits remain the critical interfaces to connect the digital cores of SoCs to the physical world and need to satisfy increasing performance demands.

We consider generic optimization problems that can be formulated as minimizing the cost of a feasible solution w.x over a combinatorial feasible set F ⊂ {0, 1}^n. For these problems we describe a framework of risk-averse stochastic problems where the cost vector W has independent random components, unknown at the time of solution. A natural and important objective that incorporates risk in this stochastic setting is to look for a feasible solution whose stochastic cost has a small tail or a small convex combination of mean and standard deviation.