CS Department Colloquium Series

The Internet is the underlying fabric for nearly all of our day-to-day communication; it is an undeniable success. More than a billion people around the world now have Internet access, and that number is projected to at least double in the next 10 years.

In the past few years, we have seen a tremendous growth in public human communication and self-expression, through blogs, microblogs, and social networks.

In recent years, the emergence of massive computing tasks that arise in context of web applications and networks has made the need for efficient graph algorithms more pressing than ever.

Many important computational problems are affected by various computational barriers that make them intractable in the worst case. In spite of this, it is often possible to get around these barriers and produce useful, if not optimal solutions for most instances of the problem.

The lack of accountability makes the Internet vulnerable to numerous attacks, including prefix hijacking, route forgery, source address spoofing, and DoS flooding attacks.

Our work sits at the intersection of Human-Computer Interaction (HCI) and Medicine. That means that 50% of what we do is applying HCI methodology to problems in medicine, so we can spend the other 50% of our time "borrowing" technologies from medicine and applying them to HCI.

The personalization of search results has long been seen as an important strategic direction for search engines. However, it was not until the past few years where personalized search was possible at scale.

In this talk I will demonstrate instances where natural CS problems lead to classical problems in mathematics. In particular, to basic problems in geometry involving lines and points in vector spaces. In some cases these are well known open problems and in others new variants of old theorems.

This talk describes a set of distributed services developed at Microsoft Research Silicon Valley to enable efficient parallel programming on very large datasets. Parallel programs arise naturally within scientific, data mining, and business applications.

In astronomy and cosmology, dark matter is hypothetical matter that is undetectable by its emitted radiation, but whose presence can be inferred from gravitational effects on visible matter. [Wikipedia]