Mathematics and Statisitcs Colloquium (Dec 7)
Speaker: Elizabeth Gross (University of Hawai`i at Mānoa)
Time and location: Thursday, December 7th, 4:00-5:00pm, IES 110
Title: The Algebra and Geometry of Evolutionary Biology
Abstract: One of the main goals of evolutionary biology is to understand the evolutionary history of a set of species. These histories can aid in conservation efforts and are represented by directed graphs where the leaves represent living species and the interior nodes represent extinct species. While it is common to assume evolutionary histories are trees, when events such as hybridization are present, networks are more realistic. However, allowing for networks complicates the process of inference, and ways to overcome this complication are needed. One recent approach to phylogenetic network inference is rooted in algebra and geometry. In this talk, we discuss the role algebra and geometry has played in the statistical problems related to network inference and show how these tools combined with statistical learning can aid in network reconstruction.
About the speaker: I am an Associate Professor at the University of Hawai’i at Mānoa in the Department of Mathematics. I study the geometric and algebraic structure of statistical models in biology and leverage this structure to answer questions relating to parameter estimation, model selection, and hypothesis testing. My work is interdisciplinary and lies within the fields of applied algebraic geometry, algebraic statistics, and algebraic biology. I am particularly interested in applications to evolutionary biology, ecology, and systems biology.
Speaker: Elizabeth Gross (University of Hawai`i at Mānoa)
Time and location: Thursday, December 7th, 4:00-5:00pm, IES 110
Title: The Algebra and Geometry of Evolutionary Biology
Abstract: One of the main goals of evolutionary biology is to understand the evolutionary history of a set of species. These histories can aid in conservation efforts and are represented by directed graphs where the leaves represent living species and the interior nodes represent extinct species. While it is common to assume evolutionary histories are trees, when events such as hybridization are present, networks are more realistic. However, allowing for networks complicates the process of inference, and ways to overcome this complication are needed. One recent approach to phylogenetic network inference is rooted in algebra and geometry. In this talk, we discuss the role algebra and geometry has played in the statistical problems related to network inference and show how these tools combined with statistical learning can aid in network reconstruction.
About the speaker: I am an Associate Professor at the University of Hawai’i at Mānoa in the Department of Mathematics. I study the geometric and algebraic structure of statistical models in biology and leverage this structure to answer questions relating to parameter estimation, model selection, and hypothesis testing. My work is interdisciplinary and lies within the fields of applied algebraic geometry, algebraic statistics, and algebraic biology. I am particularly interested in applications to evolutionary biology, ecology, and systems biology.