Distance-based summaries and modeling of evolutionary trees

Abstract:  Phylogenetic trees are mathematical objects of great importance used to model hierarchical data and evolutionary relationships with applications in many fields including evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explore the posterior distribution of trees via Markov Chain Monte Carlo methods, however assessing uncertainty and summarizing distributions remains challenging for these types of structures. In this talk I will first introduce a distance metric on the space of unlabeled ranked tree shapes and genealogies. I will then use it to define several summary statistics such as the Fréchet mean, variance, and interquartile sets. I will then provide an efficient combinatorial optimization algorithm for computation and show the applicability of our summaries for studying popular tree distributions and for comparing the SARS-CoV-2 evolutionary trees across different locations during the COVID-19 epidemic in 2020.

Bio:  Dr. Julia A. Palacios is an Assistant Professor in the departments of Statistics, Biomedical Data Science and by courtesy in Biology at Stanford University. Professor Palacios completed her PhD in Statistics at the University of Washington in 2013. She did a joint postdoc at Harvard University and Brown University before joining Stanford. In her research, Professor Palacios seeks to provide statistically rigorous answers to concrete, data-driven questions in population genetics, epidemiology, and comparative genomics, often involving probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems.