Guohua Yan is a Professor in the Department of Mathematics at the University of New Brunswick. His research interests lie primarily in cluster analysis, where he investigates methods to discover groups of similar objects based on vector measurements in a multidimensional space. His work includes linear clustering and hyperplane clustering, extending traditional clustering techniques to identify linear structures within datasets. For instance, his research delves into allometry studies that examine the relationship between body weight and brain weight across different animal species. Furthermore, he focuses on the development of efficient and robust algorithms for general linear clustering. Another key area of his research is the inference on clustered count data, which is often encountered in medical, health, ecological, and biological studies. He addresses issues such as excessive zeros in clustered count data through modeling approaches that incorporate cluster-level random effects, accounting for heterogeneity among subjects in medical studies. His collaborative work contributes to advancing the modeling strategies for complex clustered count data, particularly in longitudinal and spatial contexts.