Research

Our faculty members are prolific in a variety of statistical and interdisciplinary research collaborations. 

  • Christopher T. Franck
  • Feng Guo
  • Ina Hoeschele
  • Leanna L. House
  • Inyoung Kim
  • Scotland C. Leman
  • Xiaowei Wu
  • Hongxiao Zhu
  • Xinwei Deng
  • Feng Guo
  • Ina Hoeschele
  • Leanna L. House
  • Inyoung Kim
  • Scotland C. Leman
  • Xiaowei Wu
  • Hongxiao Zhu

Statistical Geneticists are trained both in Statistics and in Genetics (molecular, clinical, population). Statistical geneticists apply and develop statistical methods for the identification of genes implicated in the health of humans, companion animals and model organisms and in the economic value of plants and animals, the evolution of genes, genomes and species, and the design and analysis of DNA, transcriptomics, epigenomics, proteomics and metabolomics data/experiments. For the study of complex diseases in humans and model organisms, a systems-level approach is taken, termed Systems Genetics or Genetical Systems Biology. There is strong overlap with the fields of Bioinformatics and Genomics.

  • Christopher T. Franck
  • Ina Hoeschele
  • Leanna L. House
  • Inyoung Kim
  • Xiaowei Wu
  • Pang Du
  • Inyoung Kim
  • Pang Du
  • Christopher T. Franck
  • Feng Guo
  • Ina Hoeschele
  • Inyoung Kim
  • Hongxiao Zhu
  • Leanna L. House
  • Xinwei Deng
  • JP Morgan
  • Anne Ryan Driscoll
  • G. Geoffrey Vining
  • Feng Guo
  • Pang Du
  • Leanna L. House
  • Inyoung Kim
  • Hongxiao Zhu
  • George R. Terrell
  • Pang Du
  • Feng Guo
  • Inyoung Kim
  • G. Geoffrey Vining
  • Hongxiao Zhu
  • Xinwei Deng
  • Pang Du
  • Christopher T. Franck
  • Inyoung Kim
  • Hongxiao Zhu
  • Pang Du
  • Leanna L. House
  • Inyoung Kim
  • Hongxiao Zhu
  • Pang Du
  • Feng Guo
  • Yili Hong
  • G. Geoffrey Vining
  • G. Geoffrey Vining
  • William H. Woodall

As two popular smoothers, splines and kernels provide flexible nonparametric models  for regression, density estimation, hazard estimation, and a variety of other problems. Splines smooth the data via piece-wise polynomial basis functions; kernels smooth the data via a kernel function weighing the data locally in an appropriate fashion. Both methods can deal with data of multiple dimensions.

  • Pang Du
  • Inyoung Kim

Statisticians recognize that there is room for improvement in their communication and collaboration activities. This research area targets how to systematically identify opportunities for improvement and to capitalize on them using video technology and pedagogical techniques. A researcher in this area designs experiments or studies, gathers data, analyzes data, interprets the results, and uses the results to identify best practices in statistical communication and collaboration. In other words, a researcher in this area uses statistical thinking to systematically improve the process of applying statistics to solve real-world problems.

  • Anne Ryan Driscoll
  • G. Geoffrey Vining
  • William H. Woodall

Statistics Education is a field of study that focuses on the teaching and learning of statistical concepts and ideas.  Statistics educators research how students understand, process, and interpret these concepts, and emphasize statistical thinking and literacy skills as key characteristics of effective statistics students.

  • Leanna L. House
  • Jane Robertson Evia
  • Anne Ryan Driscoll
  • Pang Du
  • Feng Guo