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Dr. Hui Yi is the Assistant Research Scientist for APRIES. Hui Yi conducts research on statistical analysis for complex, high volume and high-dimensional data. The statistical methods she closely works with include regression, multivariate methods, Bayesian models, data mining, machine learning, amongst others. She has focused on applying frequentist and Bayesian variable selection methods for high-dimensional GWAS data. She also investigated Bayesian shrinkage methods and Bayesian mixed prior models with false discovery rate control criterion for Health ABC data. She was Principal Investigator of several projects on statistical modeling and data mining for highway maintenance and rehabilitation decision-making. Hui Yi’s research interest lies in developing statistical model/data mining/machine learning approaches to understanding big data. Current research topics include generalized variational neural inference of causal effects, spatial-temporal optimization models, hidden Markov model for travel time reliability analysis and multiple hypothesis tests for semi-parametric models.
developing statistical model; data mining; machine learning approaches to understand big data