Gloria Song is a Research Computational Analyst at the University of Texas MD Anderson Cancer Center. With a background in data science, she develops scalable and reproducible workflows for analyzing high-dimensional biological data, with a particular focus on single-cell sequencing. Her research centers on integrating multi-omics data—including scRNA-seq, ATAC-seq, and CUT&Tag—to investigate cellular heterogeneity, gene regulation, and disease progression. She applies advanced statistical modeling, machine learning, and data visualization methods to translate complex datasets into meaningful biological insights.

Her recent projects include dissecting T cell subpopulations and exploring chromatin accessibility dynamics in CAR-T therapy and other immunological contexts. By working closely with experimental scientists, she bridges computational analysis and biological interpretation, contributing to a deeper understanding of cancer biology and the development of potential therapeutic strategies.