PhD defence by Roberta Hunt

Title

Deep Learning Derived Traits for Phylogenetic Inference

Abstract

Phylogenetics is a vast and important topic in biology, with far-reaching applications. Yet the state of the art phylogenetic inference process is currently time consuming and requires expert knowledge of the clades being analyzed. In this thesis we explore methods of applying deep learning to the problem of phylogenetic inference. The application of deep learning to phylogenetics is a broad topic, but our focus is on the automatic extraction of traits from images of insects and how they could be used in existing phylogenetic inference methods.

Supervisors

Principal Supervisor Kim Steenstrup Pedersen
Co-Supervisor Francois Bernard Lauze

Assessment Committee

Professor Stefan Sommer, Department of Computer Science (Chair)
Associate Professor Emily A. Hartop, University Museum, Department of Natural History, Norwegian University of Science and Technology, Norway
Associate Professor Sergei Tarasov, University of Helsinki, Finnish Museum of Natural History, Finland

For an electronic copy of the thesis, please visit the PhD Programme page.