CS Department Colloquium Series

Established practices for data privacy focus on simplistic transformations such as the removal of “personally identifiable information.” On the other hand, academia has produced a long line of work on privacy-preserving computation that has yet to be translated into practice.

The past decade has seen the emergence of datasets of an unprecedented scale, with both large sample sizes and dimensionality.

Recent progress in automated deductive reasoning techniques has triggered a small scientific revolution in automated software testing, verification, and security.

Internet and personal photo collections now add up to over a trillion photos, with people being in most of them. The availability of so many photos presents a unique opportunity to model virtually the whole human population.

Over the past decade, machine learning has emerged as a major and highly influential discipline of computer science and engineering. As the scope and variety of its applications increase, it faces novel and increasingly challenging settings, which go beyond classical learning frameworks.

Second-generation DNA sequencers are improving rapidly and are now capable of sequencing hundreds of billions of nucleotides of data in about a week for a few thousand dollars. Consequently, sequencing has become a common tool in many fields of life science.

Crowd-powered systems combine computation with human intelligence, drawn from large groups of people connecting and coordinating online. These hybrid systems enable applications and experiences that neither crowds nor computation could support alone.

Theoretical computer science has brought new ideas and techniques to game and economic theory.

Visionaries in Computer Science have long seen the computer as a tool to augment our intellect.

We know how to write bad code: Litter our programs with casts, macros, pointers, naked new and deletes, and complicated control structures.