Title: Phylogenomics: “Inverting” Random Trees Speaker: Sebastien Roch, UW-Madison, Department of Mathematics
Abstract: The estimation of species phylogenies from genome-scale data is an important step in modern evolutionary studies. This estimation is complicated by the fact that genes evolve under biological processes that produce discordant trees. Such processes include horizontal gene transfer, incomplete lineage sorting, and gene duplication and loss, all of which can be modeled using certain random tree distributions. I will discuss recent results on the identifiability, or “invertibility”, of these probabilistic models. I will also consider the large-sample properties of species tree estimation methods in this context. Based partly on joint works with Max Bacharach, Brandon Legried, Erin Molloy, Elchanan Mossel, Allan Sly, Tandy Warnow, Shuqi Yu.
Bio: Sebastien Roch is a Professor in the Department of Mathematics at University of Wisconsin-Madison, where he is also affiliated with the Department of Statistics and the Theory of Computing. He earned his Ph.D. in Statistics from the University of California, Berkeley under the guidance of Elchanan Mossel. From 2007-2009, he was a Postdoctoral Researcher at Microsoft Research. From 2009-2012, he was a tenure-track Assistant Professor in the Department of Mathematics at the University of California-Los Angeles. He is the recipient of an NSF CAREER Award and of an Alfred P. Sloan Fellowship. He was a Kavli Fellow of the National Academy of Sciences in 2014 and 2017, and was a 2018 Simons Fellow. He also received the Best Paper Award at RECOMB 2018. His research interests lie at the interface of applied probability, statistics, and theoretical computer science with an emphasis on biological applications.