Our Team
Dr. Rob Deardon
Research interests: Epidemic models, disease surveillance, Bayesian and computational statistics, experimental design, spatial statistics, machine learning & data science.
Dr. Jason de Koning
Dr. Chel Hee Lee
Chel is a statistician with a background in statistical modeling, Bayesian data analysis, and statistical computation. He also has data visualization, data reduction, feature engineering, and machine learning skills for inference, prediction, and classification. His interest is to communicate statistical ideas effectively and to problem-solve from a statistical perspective in applications. I am highly interested in statistical and analytical advances in clinical research, health services, and medical education.
Dr. Quan Long
Research interests: Bioinformatics, Biostatistics, Multi-scale omics, Machine learning, Genetic basis of complex diseases, Population genetics.
Dr. Cristian Rios
I am Associate Professor at the Department of Mathematics and Statistics. My research specialty is Analysis of Differential Equations. I primarily worked with diffusion equations, nonlinear models, inverse problems, and degenerate systems. I am interested in applied (real-world) problems which require an expertise both in the theory of differential equations and on mathematical modeling, including machine learning algorithms.
Dr. Hua Shen
Dr. Hua Shen's research focus is on the methodology development and statistical analysis of data arising from public health and medical research. They include the analysis of survival data, recurrent events data, longitudinal data, incomplete data, and other complex data involving multiple outcomes, measurement error or misclassification, hierarchical structures, and high dimension. Dr. Shen is interested in statistical consultation and collaboration to provide support on research design and statistical analysis for grant applications and manuscript preparation.
Dr. Jingjing Wu
My research interests are mainly in the area of statistical inferences for much challenging semiparametric models and related applications in biostatistics. To achieve efficiency and robustness simultaneously, minimum distance methods are investigated under both semiparametric model of general form and particularly mixture models, regression models and case-control studies. I have keen interests in not only the asymptotic efficiency and robustness properties of procedures based on minimum distance technique, but also their applications in survival analysis and genetic studies. Recently I also get interested in the area of variable selection and classification for big data with either strong or rare and weak signals.
Dr. Qingrun Zhang
Research interests: Machine learning, Biostatistics, Model stability, High-dimensional-small-sample problems, RNA-seq data analysis (bulk & single-cell).