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Xiaotian Dai

Assistant Professor
  • About
  • Research

Current Courses

350.001Applied Probability Models

400.001Independent Study

Research Interests & Areas

My research interests are in developing new statistical methods for high-throughput and complex genomic, transcriptomic, and epidemiologic data. I have been broadly interested in Bayesian statistical methods, high-dimensional data variable selection, functional data analysis, and other statistics/biostatistics areas.

Journal Article

Dai, X., Acosta, N., Lu, X., et al (2024). A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater. Statistics in Medicine, 43(6): 1153-1169.
Dai, X., Lu, X., & Chekouo, T. (2023). A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease.
Statistical Methods in Medical Research, 32(8): 1616-1629.
Reese, R., Fu, G., Zhao, G., Dai, X., Li, X., & Chiu, K. (2022). Epistasis detection via the joint cumulant. Statistics in Biosciences, 14(3): 514-532.
Dai, X., Fu, G., & Reese, R. (2020). Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening. BMC Bioinformatics, 21, 1-15.