Title: New Techniques for Analyzing Large Collections of Evolutionary Trees
Prof. Tiffani L. Williams


Bayesian analysis is one of the most common approaches for reconstructing an evolutionary history (or phylogeny) of a set of organisms (or taxa). Such analysis can easily produce tens of thousands of evolutionary trees that later have to be summarized in some way. Currently, scientists use consensus trees to summarize the results from these multitudes of trees into a single tree. Yet, much information is lost by summarizing the evolutionary relationships between the trees into a single consensus tree.

In this talk, I explore an alternative approach, which compliments consensus trees, to help scientists better understand the results of their phylogenetic analysis.


Tiffani L. Williams is an Assistant Professor in the Department of Computer Science at Texas A&M University. During the 2004-2005 academic year, she was the Edward, Frances, and Shirley B. Daniels Fellow at the Radcliffe Institute of Advanced Study at Harvard University. She earned her B.S. in computer science from Marquette University and Ph.D. in computer science from the University of Central Florida. Afterward, she was a postdoctoral fellow at the University of New Mexico. Her honors include a Radcliffe Institute Fellowship, an Alfred P. Sloan Foundation Postdoctoral Fellowship, and a McKnight Doctoral Fellowship. Her research interests are in the areas of bioinformatics and high-performance computing.