Herui Liao

Herui Liao’s main research direction is developing sophisticated computational algorithms to achieve high-resolution composition analysis in metagenomic data. During his PhD studies, he developed and published three tools to address computational challenges in microbial studies: one identifies viral strains from next-generation sequencing data; a second provides high-resolution bacterial strain-level composition analysis for next-generation sequencing data; and a third classifies host disease status and identifies disease-related microbial biomarkers based on human gut microbiome data. As an MIT-Novo Nordisk Artificial Intelligence Postdoctoral Fellow, he will apply machine learning to update high-resolution microbial genomics pipelines and make them more tractable for a wide audience. One of these pipelines aims to construct highly accurate phylogenetic trees between closely related bacterial isolates, which is a task highly sensitive to false positive and false negative mutation calling. By applying machine learning to identify dataset-specific thresholds that distinguish real and fake maturations, Herui’s work has the potential to increase automation of this pipeline, making it accessible to less experienced users, such as microbiologists with minimal coding experience, microbiome researchers using this novel data type, as well as epidemiologists detecting outbreaks in hospital settings.