Julia A. Palacios completed joint postdoctoral research at Harvard University and Brown University. She completed her PhD in Statistics at the University of Washington in 2013. Her dissertation research focused on methodological development of Bayesian nonparametric inference of evolutionary parameters from DNA sequence data from a single nonrecombining locus; this research resulted in manuscripts published in Biometrics, Biological Journal of the Linnean Society and Proceedings from the 28th Conference on Uncertainty in Artificial Intelligence.
Julia’s postdoctoral work involved developing formal probabilistic and statistical methods to infer evolutionary parameters from whole genomes; her most recent published work develops a Bayesian nonparametric method that infers population size trajectories from a sequence of local genealogies partially linked. Currently she is extending the methodology to infer population size trajectories from whole genomic sequences directly.
In her research, Professor Palacios seeks to provide statistically rigorous answers to concrete, data-driven questions in population genetics, epidemiology, and comparative genomics, often involving probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes and recent developments in machine learning and statistical theory for big data; future research plans are aimed at incorporating the effects of selection and population structure in Bayesian inference of evolutionary parameters such as effective population size and recombination rates, and development of more realistic and computationally efficient methods for phylodynamic methods of infectious diseases.