PhD in Statistics
Program Faculty
Professor Wu's research interest lies in applying the power of statistical science to promote biomedical researches. In statistical genetics, he is developing novel statistical theory and methodology to analyze genome-wide association (GWA) data and deep (re)sequencing data to hunt new genetic factors for complex human diseases. In epigenetics, he is studying gene expression regulation mechanisms through chromatin interaction, and RNA silencing pathways in the developmental stages of germ-line cells.
Professor Zou's research focuses on financial time series and spatial statistics with applications to epidemiology, public health and climate change. His most recent research on statistical theory and methodology addressed a wide range of challenges including high dimensionality, complex dependencies, and space and time variations. His research in high-frequency financial data tackled problems with high dimensionality, which is currently a hot topic in statistics.
Teaching is an art. It is something you develop within yourself. It needs hard work, time, creativity and dedication. Giving clear and understandable lectures, facilitating group activities, and interacting with students are all important parts of my teaching. My objectives as a statistics educator are: (1) to teach students about statistical tools and how to use them correctly, (2) to expose students to the structure of statistical analysis, and (3) to teach students how to communicate statistical results and concepts to a variety of audiences.
My research centers on quantitative analysis of dependent data, and my current interests include time series analysis, spatial statistics, spatial econometrics, and financial econometrics. My work involves both statistical and econometric methodology development and empirical studies related to financial market and remote sensing data. Although anchored in statistics, my research projects are interdisciplinary and lie at the intersection of statistics, applied probability, data science, finance, and economics.
My research in applied statistics focuses on methods for causal inference using large, administrative datasets, primarily with applications in learning sciences and social sciences. I have developed and worked on methods combining machine learning with design-based analysis of randomized trials and matched observational studies, principal stratification and mediation analysis using log data from intelligent tutoring systems, and regression discontinuity designs.
Getting Involved
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