PhD in on Evolutive Genetics and Molecular Biology – Federal University of Sao Carlos (UFSCar)-Brazil
Computational and machine learning-based methods in phylogenetics.
Deep Learning frameworks have increasingly been applied to phylogenetics, phylodynamics, and macroevolution due to their flexible and data hungry nature. Recent DL implementations have shown encouraging performance, with higher speed and accuracy than similar methods, when used with phylogenetic information to compare Birth-Death diversification models and estimate parameters for epidemiological data. Here, we propose a new DL framework that allows the incorporation of distinct strategies for simulating and representing phylogenetic information.