Research

The Ye Lab’s research focuses on developing and applying methods in biostatistics and bioinformatics to biomedical research, with particular interests in causal inference and real-world evidence, clinical trial design, and statistical methods for omics data analysis. The lab’s work emphasizes rigorous design and inference for complex clinical, translational, and molecular studies, with applications primarily in cancer and other chronic diseases.

Current research includes methodological work in target trial emulation and comparative effectiveness research using observational data, including methods for addressing treatment changes, imperfectly measured clinical variables, and the credibility of causal conclusions. The lab also develops novel and robust clinical trial designs, particularly for early-phase and Phase II cancer studies, including adaptive designs and approaches that account for uncertainty in historical benchmarks. Its omics research includes statistical methods for characterizing gene-set co-expression and other molecular relationships across phenotypes, tissues, and cell types, including in single-cell RNA-seq data.

Across these areas, the Ye Lab aims to develop broadly applicable statistical methodology and bioinformatics tools that improve the design, analysis, and interpretation of biomedical studies.