Research in Statistics, Biostatistics, and Data Science

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 Themes

  • Fundamental Research in Statistics
  • Data Science and Big Data Analytics
  • Biostatistics and Quantitative Biology
  • Quantitative Methods in the Social Sciences