I am a Research Associate Professor (with tenure) in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor campus. I received my Ph.D on Statistical Science in 2012 from Duke University. Before joining the University of Michigan in 2017, I was an assistant professor jointly in the Department of Biostatistics & Medical Informatics and the Department of Population Health Sciences at the University of Wisconsin-Madison and a Postdoctoral Research Scholar in the Department of Statistics at Columbia University.

My research focuses on cutting-edge statistical methodology linking design- and model-based approaches for survey inference, missing data analysis, confidentiality protection involving the creation and analysis of synthetic datasets, and causal inference with observational data. I have established my research agenda on advancing survey inference with Bayesian modeling techniques and adjusting for selection/nonresponse bias in complex data modeling with various types of data (e.g., survey and big data) and across broad substantive disciplines.

My methodological focus has been on multilevel regression and poststratification (MRP) and multiple imputation (MI) via Bayesian latent variable models. My research work has generalized MRP to disentangle analytic challenges in data integration and inferences with large-scale, massive data. I have maintained a strong research focus on MI methods for missing data that have complexities arising from design, nonresponse, and measurement error; confidentiality concerns; or unbalanced contents after combining multiple data sources. I also work on synthetic data generation for confidentiality protection and causal effect evaluations with observed electronic health records and claims data.

In addition to methodological research, I have been actively collaborating with interdisciplinary researchers to improve the application of statistics in many different substantive fields.

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