Kai Wang

Kai Wang is a Professor of Pathology and Laboratory Medicine, specializing in bioinformatics methods to understand the genetic basis of human diseases. His work integrates electronic health records and genomic information to advance large-scale genomic medicine.

His research spans statistical genetics, clinical trials, and health informatics, with recent publications focusing on cluster-randomized trials, covariate adjustment, and brain imaging analysis. Notable work includes methodologies for robust inference in stepped-wedge designs and applications in Alzheimer's disease studies.

Kai Wang's publications demonstrate expertise in bridging statistical theory with biomedical challenges, particularly in handling incomplete data and optimizing clinical trial precision. While no specific awards or students are listed, his Google Scholar profile highlights contributions to genomic medicine and causal inference.