Faculty in the Department of Statistics engage in fundamental and multidisciplinary research to expand the scope of statistical methodology and its implementation in data intensive fields. Fundamental research in statistics is advancing methodology and its theoretical basis to accommodate new data structures and to generalize the scope of application. Data science research is expanding the range of statistical methods, data structures and algorithms, and software packages to address the massive amounts of data that can be collected and the challenges of drawing valid conclusions from it. Research in biostatistics includes methodology development and evaluation, design and analysis of health studies, and advanced applications in genomic research. Research in statistical methods for the social sciences is providing the framework for measurement, policy analysis and risk analysis.
Key drivers of modern research in statistics and data science are large scale high-dimensional data, network data, spatially and temporally correlated data, and large complex data bases. The links below provide further information about the major research themes of our faculty including links to individual faculty members associated with each theme.
Research Areas
Fundamental Research in Statistics
Our faculty conduct fundamental research in statistics, focusing on developing new theories, models, and methodologies to address complex data challenges. Areas of expertise include nonparametric and Bayesian methods, machine learning, high-dimensional data analysis, statistical computing, and modeling of dependent, time series, and spatial-temporal data.
Data Science and Big Data Analytics
Our faculty engage in data science research focused on addressing the challenges of analyzing and managing large-scale, high-dimensional data. Their work spans areas such as statistical inference, machine learning, computational statistics, parallel computing, functional image analysis, climate modeling, and network analysis.
Biostatistics and Quantitative Biology
Our faculty conduct biostatistics research to address the unique challenges of biological, medical, and health data, such as censored observations, longitudinal structures, and large-scale multiple testing. Their work includes advancements in survival and longitudinal analysis, functional image analysis, genomics data integration, clinical trials, and ecological modeling.
Statistics and Data Science Education
Our faculty engage in Statistics and Data Science Education research, exploring how learning, curricular, and instructional theories intersect in statistics and data science classrooms. Their work focuses on areas such as student cognition, social and cultural influences, technology-enhanced learning, TA training, online education, social justice in statistics, and assessment development.
Quantitative Methods in the Social Sciences
Our faculty conduct quantitative research in the social sciences, developing advanced statistical methods to analyze complex surveys, educational assessments, economic data, and policy impacts. Key areas include latent variable modeling, multilevel modeling, educational measurement, econometrics, finance, policy analysis, and risk analysis.