CS Colloquium Series

The evolution of HIV within individual patients is associated with disease progression and failure of antiretroviral drug therapy.

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In the popular imagination, quantum computers would be almost magical devices, able to "solve impossible problems in an instant" by trying exponentially many solutions in parallel.

Studying the behavior of gene regulatory networks by learning from high-throughput genomic data has become one of the central problems in computational systems biology. Most work in this area has focused on learning structure from data -- e.g.

There is a great need for programs that can describe what people are doing from video.

We study the problem of finding point-to-point shortest paths in a setting where preprocessing is allowed. We are interested in exact algorithms only, and our focus is on road networks. Our algorithms use several different strategies to speed up the queries.

This talk has two parts: the first part is on new ways to model and analyze biological sequences; the second part describes methods for constructing and comparing protein interaction networks, which are emerging as canonical data sets of the post-genomic era.

Algorithms for biological sequence

When linear models are too simple then the following "kernel trick" is commonly used: Expand the instances into a high-dimensional feature space and use any algorithm whose linear weight vector in feature space is a linear combination of the expanded instances.

The notion of a ``market'' has undergone a paradigm shift with the Internet -- totally new and highly successful markets have been defined and launched by companies such as Google, Yahoo!, Amazon, MSN and Ebay.

In the past few years, overlay networks have received much attention but there has been little study on the "interaction" of multiple, co-existing overlays on top of a physical network.

In addition to previously introduced concept of overlay routing strategy such as the selfish routing, we intro