معرفی
Dr. Julia Palacios is an Associate Professor of Statistics and Biomedical Data Science at Stanford University. She earned her PhD in Statistics from the University of Washington in 2013. Her research program aims to develop statistically rigorous methods for evolutionary genetics and public health challenges, leveraging probabilistic modeling of evolutionary forces with computationally efficient approaches applicable to large-scale datasets.
Her methodological work integrates stochastic processes, Bayesian nonparametrics, machine learning, and statistical theory for big data analysis. Current research foci include coalescent modeling for population dynamics, phylodynamic tracking of pathogens like SARS-CoV-2, and multi-omic integration for disease severity analysis.
Palacios' recent publications demonstrate advancements in Bayesian inference for heterochronous genetic data, coalescent model optimization, and adaptive sampling techniques. Her applied work contributes significantly to understanding COVID-19 transmission dynamics and evolutionary mechanisms.
