I’m a 3rd year PhD student studying statistics at Duke university under the supervision of Amy H. Herring and David Dunson. I have interests in statistical applications in the environmental and human health sciences. I develop Bayesian methods that provide better interpretation in relation to the reality along with uncertainty quantification. My current work focuses on chemical toxicity, pollutant effects on diseases, and spatial prevalence of hazardous chemicals.
PhD in Statistical Science, 2022 (expected)
Master's in Statistics, 2017
Bachelor's in Statistics, 2015
Build a Bayesian model to predict effects of PM2.5 with and without wildfires.
Include information about dynamics of wildfires and their progression into a Gaussian process (GP) using multiple directed acyclic graphs (DAG).
Investigate the short-term effect of PM2.5 on respiratory diseases including COVID-19.
Develop a predictive spatial model for per- and polyfluoroalkyl substances (PFAS) in public water system (PWS) along with uncertainty quantification.
Identify upstream and downstream information of PWSs in relation to the nearest stream network and construct a corresponding DAG.
Fit a multivariate GP using the DAG, incorporating external factors such as known sources or known relationship between PFAS compounds.
Adapted Bayesian heteroscedastic nonparametric regression to a multiple hypothesis testing framework.
Imposed a generalized latent factor model to form a non-exchangeable prior for testing.
Developed a matrix completion method for a latent matrix.
Tackled sparsity of the ToxCast data using hierarchical framework.
Enabled prediction for non-tested chemical’s activity.
Broadened the definition of activity including heteroscedasticity.