Tinyi Chu develops computational frameworks spanning Bayesian statistics, deep learning, and machine learning to study cellular communication, tissue organization, and gene regulation from single-cell and spatial transcriptomics data. His lab builds methods such as Bayesian deconvolution models and continuous-time models for gene regulatory dynamics, and analyzes cell-free RNA signatures, with applications in tumor microenvironment analysis, transplant rejection monitoring, and early cancer detection.