Rising Stars 2020:

Uyen Mai

PhD Candidate

University of California, San Diego

Areas of Interest

  • Biosystems and Computational Biology


Log Transformation Improves Dating of Phylogenies


Phylogenetic trees inferred from sequence data often have branch lengths measured in the expected number of substitutions and therefore, do not have divergence times estimated. These trees give an incomplete view of evolutionary histories since many applications of phylogenies require time trees. Many methods have been developed to convert the inferred branch lengths from substitution unit to time unit using calibration points, but none is universally accepted as they are challenged in both scalability and accuracy under complex models. Here, we introduce a new method that formulates dating as a non-convex optimization problem where the variance of log-transformed rate multipliers are minimized across the tree. On simulated and real data, we show that our method, wLogDate, is often more accurate than alternatives and is more robust to various model assumptions.


Uyen Mai is a PhD candidate in the Computer Science and Engineering Department at the University of California, San Diego (UCSD). She works with Prof. Siavash Mirarab on computational biology, with the focus on evolutionary biology. She develops scalable methods for large biological datasets. She is also interested in data analysis of phylogenomics data, especially microbiomes and viruses. Uyen Mai is a contributor (co-first author) to the WoL: Reference Phylogeny for Microbes, which includes 10,575 bacterial and archaeal genomes on 381 global marker genes, in collaboration with Rob Knight's lab. She was a recipient of the Microbial Sciences Graduate Research Fellowship in 2017 and 2018. Uyen obtained her MS degree at UCSD in 2019 and BS degree at Portland State University in 2016.

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