Working Thesis Title
Mathematical and computational phylogenetics and online algorithms
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Phylogenetic treespaces represent the vast landscape of possible evolutionary relationships among species. Within these spaces, each point corresponds to a unique phylogenetic tree, depicting the hypothesized evolutionary history of species.
The exploration of phylogenetic treespaces is central to phylogenetics, where researchers employ computational algorithms such as maximum likelihood or MCMC to navigate this complex terrain. Understanding the structure and properties of treespaces is crucial for accurately inferring evolutionary relationships and quantifying uncertainty in phylogenetic reconstructions.
The challenges in statistical analysis of treespaces are two-fold: the exponential growth in size as taxa increase, and the non-Euclidean nature of these spaces.As the number of taxa increases, exhaustive exploration of treespaces becomes computationally prohibitive. Meanwhile, the non-Euclidean geometry complicates distance calculations and statistical inference, requiring specialized methodologies to navigate effectively.
My thesis focuses on developing basic statistics, such as mean and variance, within the RNNI treespace and providing practitioners with tools to use these.
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Supervisors:
Primary Supervisor:听Alex Gavryushkin
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Research Interests
Molecular phylogenetics and evolution, Phylogenetic inference, Computational Biology, Non-Euclidean Statistics.
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Academic History
2014-2021 BSc and MSc Biomathematics, University of Greifswald Germany
