Next generation phylogenetic modelling using machine learning

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The goal of phylogenetics is to draw inferences about the past from the diversity of the present. Although phylogenetics is best known from its application to the reconstruction of biological histories (from biodiversity), phylogenetic also has ramifications for the problem of reconstructing linguistic histories (from the world's linguistic diversity). The program I propose involves both biological and linguistic reconstructions. I hypothesize that the two problems have sufficient similarities to justify a joint study, but also have sufficient differences to foster innovation. In both biology and linguistics, the current state of phylogenetic research is stimulating. Increasingly, researchers have at their disposal datasets of new types, that is, new sources of data such as linguistic records (typological or phonological), geographic locations, results from SNPs studies, and combinations of these. Increasingly, researcher also have access to datasets of new scales, that is, datasets containing large numbers of species/populations/languages (collectively called taxa), or large amounts of data for each taxon (genome-wide and vocabulary-wide studies), or both. Efficiently using phylogenetic datasets of new types and scales is still largely an open problem. It is an important problem, since it has the potential to advance our understanding in fundamental areas of science and also to impact biotechnology. The aim of this program is to unlock the potential of phylogenetic datasets of new types and scales. The approach will be to apply and build on recent developments in machine learning. Since phylogenetic inference has challenging characteristics that are only found at the cutting edge of machine learning, this is likely to result not only in innovations in phylogenetics, but also in machine learning.