My methods work primarily focuses on multi-pollutant modeling, non-linear interaction selection in the presence of many environmental contaminants, and statistical inference when exposure data are subject to multiple detection limits. I have a number of applied collaborations in environmental statistics ranging from amyotrophic lateral sclerosis, racial disparities in telomere length, and gestational duration in pregnant women.
My main research interest is developing methods for determining noteworthy associations from genetic studies. Accurate assessment of potential signals from the exploratory stage is important for minimizing false-positive findings for follow-up analysis.
I'm currently working on modeling and optimization for variable selection with missing data, and median analysis in high-dimentional data. My current project is to identify environmental pollutants associated with amyotrophic lateral sclerosis disease.
I am passionate in utilizing data integration techniques to improve the precision and efficiency of disease risk prediction. As my PhD thesis, I am developing efficient statistical tools to incorporate external information from big health science data into internal studies.
Currently, I am working on quantifying gene-environment interactions on the prevalence of cancer. I would like to see which measures of risk associations are the most informative on providing the most accurate quantification of the relationship between gene-environment interaction and the prevalence of disease.
Currently, I am interested in causal inference and survival analysis methods. I work on methods to statistically validate biomarkers as surrogate endpoints for clinical trials. I began my research in the department on the NIH Training Grant for Cancer Research currently a National Science Foundation Graduate Research Fellow.
I am interested in causal inference methods and their applications to cancer epidemiology. My work on analysis of subpopulation causal effects helps bridge the gap between traditional population causal inference and individualized inference of cancer treatment.
My research centers on clinical quality measure development for public reporting, latent variable methods, conditional graphical models, and joint estimation for high-dimensional and mixed-type data. In the Kidney Epidemiology and Cost Center, I work on methods related to the Dialysis Facility Compare (DFC) Clinical Quality of Care Star Rating. I am also passionate about data for good initiatives at the University of Michigan such as Statistics in the Community (STATCOM).
I currently work on developing high-dimensional Bayesian mediation analysis methods with application to omics data. I am also interested in developing analytical approaches to extract information from large-scale data, with the hope to advance our understanding of the molecular mechanism of human diseases.
My current research focuses on causal inference for censored data. I'm working on a number of projects including evaluating the effect of treatments for metastatic prostate cancer using insurance claims data.
I am interested in the environmental risk score and already published one paper titled "Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES" in Environmental Health. Currently, I am working on the joint model of latent class analysis.