Yucai Shao

The paper analyzes the spread of H5N1 B3.13 in U.S. dairy cattle using genomic data and movement networks, finding that cattle movement, not birds, drives the outbreak.

The paper introduces parallel algorithms to improve the computational efficiency of structured coalescent models for phylogeographic analysis, enabling faster and larger-scale studies of pathogen spread.

This study tracks the spread and evolution of H5N1 in the sub-Antarctic and Antarctica, revealing long-distance dispersal and mammalian-adaptive mutations.

This paper introduces a gradient-based Hamiltonian Monte Carlo sampler for efficient inference in episodic birth-death-sampling models, showing significant computational improvements over Metropolis-Hastings methods.