Julia A. Palacios

This paper introduces antiGen, a machine learning model that forecasts viral mutations from phylogenetic trees, achieving state-of-the-art results for SARS-CoV-2 and other viruses.

The paper shows CDV persists in Arctic foxes for over a decade and spreads across species and regions, connecting North America to Eurasia.

The paper introduces inPhynite, an efficient Bayesian phylogenetics algorithm under the infinite sites model, achieving 225x speedup without accuracy loss on large datasets.

The paper integrates multi-omic data to identify genetic and microbiome factors associated with severe COVID-19, independent of ancestry and social factors.

This paper introduces a Bayesian model for inferring dependent population dynamics using Markov random fields and applies it to study SARS-CoV-2 variants.

This paper develops a high-throughput method to generate host and pathogen genomic data from single nasopharyngeal swabs of COVID-19 patients.

This paper introduces a Bayesian method called BESTT for inferring population size changes using Tajima’s coalescent, showing it is computationally efficient and accurate for both simulated and human data.