Joseph Yu

Joseph (Yang-en) Yu is a Ph.D. candidate in Computer Science at Baylor University, where he previously earned his B.S. in Bioinformatics (Magna Cum Laude) and M.S. in Computer Science. An active scholar and student leader, he has served as Research Chair on the Baylor Graduate Student Association and is a student member of both the American Society of Human Genetics and the Royal Statistical Society. His dissertation research centers on Transcriptome-wide association studies (TWAS), a computational approach used to identify correlations between genetic factors and phenotypes, such as candidate disease genes and biological processes. Although TWAS is increasingly utilized across large biobanks as an efficient alternative to laboratory screening and single-variant genome-wide association studies (GWAS), its performance across varying gene expression and regulatory architectures is not yet well characterized, and rigorous benchmarks incorporating deep learning models remain limited. To address these limitations, Yu's work aims to benchmark the TWAS framework across complex human phenotypes and gene expression patterns, develop and integrate deep learning models into TWAS pipelines, and utilize controlled simulations for model evaluation before applying the pipeline to large biobanks like AllOfUs.