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Dr. Ye Liang

Department of Statistics

Dr. Ye Liang

With the recent rise of data science, more scientific disciplines are deeply involved in the revolution of big data and machine learning. Although nowadays information is abundant, big data are not immune to sampling bias, measurement errors, noises or random shocks, spurious correlation and other classical data issues. Statistical science remains a foundational role in the era of data science as per a statement by the American Statistical Association.


As an applied statistician, I work on developing statistical models, parameter estimation methods and computational algorithms for modern applications that arise from health data analysis, ecological modelling and econometrics. In particular, my research methodology belongs to a subfield called Bayesian statistics, which has its own interpretation of randomness, as often compared to frequentist statistics. I am specialized in Bayesian hierarchical modelling and Bayesian computations. I like to use my statistical tools to address challenging models where uncertainty exists and dependence is complex.


One of my focused areas is analyzing data from electronic health records (EHR). The modern EHR is typical big data, with massive numbers of patients, encounters, lab results, medications and other records. The data structure is complex and there are uncertainty and heterogeneity at all levels. With the recent NIH R01 grant, my research team could tackle the data quality issue of EHR and build better predictive analytic tools for an eye disease called diabetic retinopathy. Our long term goal is to establish machine learning tools for disease screening, detection and prevention by using the massive EHR lab results.


I teach several courses in the Department of Statistics, including Bayesian analysis, time series analysis, nonparametric statistics and multivariate analysis. I enjoy the variety of topics that I engage, which helps me to be versatile for real applied problems. I earned my Ph.D. in Statistics from the University of Missouri in 2012, and joined Oklahoma State University in 2012.

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