With a vast number of items, web-pages, and news to choose from, online services and the customers both benefit tremendously from personalized recommender systems. Such sys- tems however provide great opportunities for targeted adver- tisements, by displaying ads alongside genuine recommendations. We consider a biased recommendation system where such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to a single user. We ask whether it is possible for a small subset of collaborating users to detect such a bias.
Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute tasks and edges indicate data-flows between these compute tasks. Jobs (graphs) arrive randomly over time, and upon completion, leave the system. When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations.
Every few years, WNCG welcomes a new Director and Associate Director from among its faculty ranks. With an academic culture that encourages openness and research collaborations among equals, the rotation of Directors provides each faculty member with the opportunity to lead WNCG.
Just like Edison turned on the light bulb, the Department of Electrical and Computer Engineering (ECE) and the Wireless Networking and Communications Group (WNCG) at UT Austin are switching kids on to the field of engineering. Part of a STEM program geared towards middle school and high school students, the Edison Lecture Series celebrates fun over fundamentals and enables kids to have fun with science.