Presidential Lectures

This Biotech Symposium will focus on the visualization and representation of analytic results from complex data sets.

Andrew Gelman will illustrate this concept with various examples from his recent research and discuss more generally how statistical methods can help or hinder the scientific process.

Although most people regularly tune up their cars, you probably haven’t needed to bring your brain in for a tune-up, despite the fact that the human brain is far more complex than the internal combustion engine. What’s more, unlike most machines, your brain is constantly changing in order to store memories and adapt to a fluid environment. Our brains are faced with a fundamental challenge: They must preserve the integrity of the neural circuits that subserve behaviors over the lifetime of an organism, while at the same time allowing plastic mechanisms to shape and fine-tune their function.

Probabilistic topic models provide a suite of tools for analyzing large collections of electronic documents. A traditional topic model analyzes a collection of documents to discover its hidden themes.  These themes can be used to organize, visualize, summarize and navigate the collection. Many collections are associated with corresponding reader behavior data, which is useful both for making predictions about readers (such as which articles they will like) and in understanding patterns in how they read.