Major Research Themes and Projects in the Department of Statistics
Fundamental Research in Statistics and Biostatistics
- Nonparametric and semiparametric statistics, including quantile regression, robust statistics, and dimension reduction (He, Portnoy, Simpson, Zhong, funded by NSF)
- Inferential methods for dependent and longitudinal data (Qu, Shao, funded by NSF and NIH)
- High dimensional data analysis and model selection (Liang, He, Ma, funded by NSF and NIH)
- Monte Carlo methods in statistical computing (Chen, funded by NSF)
- Statistical modeling and analysis of rank data (Marden)
Statistical Modeling and Analysis in Complex Systems
- Remote sensing analysis of the inner earth (Ma, funded by NSF)
- Tomographic analysis of the solar atmosphere (Chen, funded by NSF)
- Educational assessment and cognitive diagnosis (Douglas, Stout, funded by NSF)
- Statistical methods for assessing the pixel-level fidelity of radio-interferometric images in astronomical sciences (Martinsek, funded by NSF)
Large-Scale Data Analysis
- Calibration and pattern recognition for quantitative ultrasound (Simpson, funded by NIH)
- Image analysis in neurosciences (Wang, Liang, funded by NIH)
- Downscaling of climate change projections (He and Shao, funded by NSF)
- Better detection of odorants using the colorimetric response from a library of immobilized vapor-sensing dyes (Zhong, funded by NIH)
Biological Statistics and Computational Biology
- Statistical approaches to integration of mass spectral and genomics data of yeast histone modifications (Ma and He, funded by NSF)
- Low-rank representation of genomics data (He, funded by NIH)
- Statistical modeling of gene regulatory networks (Zhong)
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