Title: Maximum Likelihood Degree of Brownian motion tree models

Abstract: A Brownian motion tree model is a Gaussian model whose associated set of covariance matrices is linearly constrained according to common ancestry in a phylogenetic tree. In joint work with Jane Coons, Aida Maraj, and Ikenna Nometa, we study the complexity of inferring the maximum likelihood (ML) estimator for a Brownian motion tree model by computing its ML-degree. Our main result is that the ML-degree of the BMT model on a star tree with n + 1 leaves is 2^(n+1) – 2n – 3, which was previously conjectured by Amendola and Zwiernik. We also prove that the ML-degree of a Brownian motion tree model is independent of the choice of root and we find a combinatorial formula for the likelihood function. In this talk, I will introduce Brownian motion tree models and the tools from computational algebraic geometry that we use to compute the ML-degree.