Our research areas are in single-cell computational biology and childhood cancer genomics. We apply our developed computational methods to characterize cancer cell heterogeneity by integrating large-scale bulk and single-cell multi-omics and spatial transcriptomics analyses. We work on data science by applying machine learning techniques and statistical analysis to uncover patterns and extract knowledge underlying disease mechanisms. We work on multiple single-cell approaches to study medulloblastoma and other solid tumors in children. These include developing methodologies/platforms for analyzing individual genomes and epigenomes. We build cell-cell interaction networks to understand how cancer cells communicate, particularly with their microenvironment and immune cells. We focus more on integrating genes and alternative splicing information to explore new potential targets.  We also work on bulk DNA methylation to develop novel methods for the deconvolution and classification of samples. Our analysis methods and tools developed along the way of our research will help identify molecular signatures and provide a decision support system for diagnosis, prognosis, and biomarker discovery or design of novel targeted therapies for personalized treatment.