CS Department Colloquium Series

Recent research in program synthesis has made it possible to effectively synthesize small programs in a variety of domains. In this talk, I will describe two useful applications of this technology that have the potential to influence daily lives of billions of people.

Online applications are vulnerable to theft of sensitive information because adversaries can exploit software bugs to gain access to private data, and because curious or malicious administrators may capture and leak data.

We explore nonparametric Bayesian statistical models for image partitions which coherently model uncertainty in the size, shape, and structure of human image interpretations.

Major advances in Question Answering technology were needed for Watson to play Jeopardy! at championship level -- the show requires rapid-fire answers to challenging natural language questions, broad general knowledge, high precision, and accurate confidence estimates. In addition, Jeopardy!

Architects and circuit designers have made enormous strides in managing the energy efficiency and peak power demands of processors and other silicon systems. Sophisticated power management features and modes are now myriad across system components, from DRAM to processors to disks.

The Echo Nest is a music intelligence platform that has collected and analyzed data about over two million artists and 35 million songs.

Graphical models (GMs) offer a powerful language to elegantly define expressive distributions, and a generic computational framework to support reasoning under uncertainty in a wide range of problems.

Most software is inherently nonrobust--change the operating conditions of a typical program slightly, and you may obtain very different results.

GelSight is a novel sensor capable of measuring surface texture and shape at high resolutions. The sensor itself is a slab of clear elastomer covered with a reflective skin. When an object presses into the sensor, the reflective skin conforms to the shape of the object's surface.

A path-sensitive static analysis considers each possible execution path through a program separately, potentially yielding much more precise results than an analysis that makes fewer distinctions among paths.