Zhengqing "Vera" Liu's Dissertation Defense

Apr

2022

1:00 pm – 2:00 pm

Virtual (view details)

Featured Speaker(s): Vera Liu

Cost: Free

Description

The 2022 Dissertation Defenses continues on Wednesday April 20, from 1:00 p.m. to 2:00 p.m. with Zhengqing "Vera" Liu.

Title: Interpretable Random Structure for Non-standard Data -- with Applications to Biomedical and Social Science Studies

Advisor: Peter Mueller

Abstract: This work introduces three examples of non-standard data that occur in statistical inference for biomedical and social science research. Each example represents a problem that is of substantial interest to researchers in the relevant field and requires some novel statistical approaches. As a common theme, all three settings involve complex data structure that is not easily interpretable with more traditional methods.

The first project proposes a hypothesis testing procedure applied to phylogenetic trees that represent evolutionary paths of seasonal influenza strains. The method quantifies the change of genetic composition of seasonal influenza over years and serves as a crucial step to inform vaccine selection. The second project uses information from atmospheric studies that describe the movement and dispersion of pollutants emitted from coal powered factories. The goal is to make valid causal statements on how intervention applied at factories may affect the health outcome of the surrounding community, which is an important step to make policy recommendations. The third project proposes a joint model to analyze tweet contents posted by UK general election candidates. The proposed method effectively borrows information from external sources, such as concurrent newspaper. The joint model describes the types of issues candidates tend to focus on, given their demographic information and the election constituency they represent.