Tandy Warnow
Professor of Computer Sciences at the University of Texas at Austin

Tandy Warnow is Professor of Computer Sciences at the University of Texas at Austin, where she is a member of five graduate groups (Computer Sciences, Mathematics, Computational and Applied Mathematics, Molecular Biology, and Ecology, Evolution, and Behavior). Her research combines mathematics, computer science, and statistics to develop improved models and algorithms for reconstructing complex and large-scale evolutionary histories in both biology and historical linguistics. Tandy serves on the National Academy of Science's Committee for Interdisciplinarity, and is a member of the board of directors of the International Society for Computational Biology. Tandy received her PhD in Mathematics at UC Berkeley under the direction of Gene Lawler, and did postdoctoral training with Simon Tavare and Michael Waterman at USC. She received the National Science Foundation Young Investigator Award in 1994, and the David and Lucile Packard Foundation Award in Science and Engineering in 1996.

Talk title:
Computational Problems in Inferring the Tree of Life

Abstract:
The Tree of Life initiative - to reconstruct the evolutionary history of all organisms - is the computational grand challenge of evolutionary biology. Current methods are limited to problems several orders of magnitude smaller and also fail to provide sufficient accuracy at the high end of their range. The Cyberinfrastructure for Phylogenetic Research (CIPRes) project, recently funded by a $11.6M Information Technology Grant from the NSF, funds 33 investigators (including biologists, computer scientists, and mathematicians) from 13 institutions, to help develop the computational infrastructure for evolutionary biologists so that they can analyze large datasets.

In this talk, I will describe the activity in the CIPRES project, and show the progress the group is making towards enabling highly accurate phylogenetic analyses of large datasets. In particular, I will describe our work on developing better techniques for the major NP-hard optimization problems, Maximum Parsimony and Maximum Likelihood.