Shuyu holds a Master’s degree in Statistics, with rigorous training in mathematics and probability theory. With over a decade of experience working with real-world data, Shuyu has a proven track record of applying a broad range of statistical models—including Generalized Linear Mixed Models, Mixture Models, Random Forests, Time Series analysis, and Meta-Analysis—to solve complex scientific and business problems.

Shuyu specializes in robust statistical methods for outlier detection and handling. With over 15 years of programming experience in R and SAS, she is also proficient with Shiny, SAS PROCs, and ODS. Additionally, she has working knowledge of Python and experience with TensorFlow. Shuyu is familiar with CDISC standards and industry best practices, and is actively expanding her expertise in advanced machine learning techniques, including Convolutional Neural Networks (CNNs) and Bayesian ensembles.