Inferring the "tree of life" from genetic data is an important problem in evolutionary biology. It has been customary to think about this problem as one of learning the evolutionary history of a single gene. However, individual genes might have evolutionary histories that are (topologically) distinct from each other and, of course, from the underlying tree of life. In this talk, I will discuss a probabilistic model of evolution that takes this into account. I will then outline our recent theoretical explorations into questions such as reliable algorithms for learning the tree of life from several genes and the amount of data required for such algorithms to succeed. It will be my goal to convince the audience that the combinatorial and statistical structures that arise from evolutionary biology is a fascinating source of problems for people with an electrical engineering, computer science, or statistics background. The talk will assume no background in biology. (Based on joint work with Robert Nowak and Sebastien Roch)
Gautam Dasarathy received the BTech degree in Electronics and Communication Engineering from VIT University, India in 2008, and the MS and PhD degrees in Electrical and Computer Engineering from the University of Wisconsin-Madison in 2010 and 2014 respectively, where he was advised by Prof. Robert Nowak and Prof. Stark Draper. He is currently a Postdoctoral Research Fellow in the Machine Learning Department at Carnegie Mellon University, Pittsburgh. His research interests include topics in signal processing, statistics, learning theory, and information theory.