Natalie Stanley

Natalie Stanley, PhD is an Assistant Professor in the Computational Medicine Program at the University of North Carolina at Chapel Hill School of Medicine, with joint appointments in the Department of Computer Science and UNC Lineberger Comprehensive Cancer Center. Her research bridges computational science and immunology through advanced bioinformatics approaches.

Dr. Stanley's research focuses on developing computational methods for high-throughput single-cell immune profiling data, particularly flow cytometry, mass cytometry, and imaging mass cytometry. Her work addresses critical challenges in linking single-cell data to clinical outcomes across diverse contexts including aging, neurodegeneration, women's health, pregnancy, and trauma. Key methodological contributions include metaclustering approaches for automated cell population discovery, set-based modeling of cytometry data, and techniques for extracting biologically meaningful features from complex immune datasets.

Her publication record demonstrates consistent productivity with 41 PubMed-indexed articles spanning 2015-2025, showing accelerating output in recent years with 7 publications in 2023 and 7 in 2024. The research trajectory reveals progressive specialization in single-cell bioinformatics, with early work on network compression evolving into sophisticated immune profiling methodologies. Publications appear in high-impact journals including Immunity, Nature Communications, Science Translational Medicine, and Aging Cell, reflecting interdisciplinary impact.

Dr. Stanley leads the CompCy Lab at UNC, which specializes in developing scalable computational methods that can accommodate hundreds of samples with millions of cells. The lab's work emphasizes automated cell population discovery, feature extraction, and comprehensive visualization of single-cell data to uncover cell-types and signaling pathways implicated in disease phenotypes. Current research priorities include understanding neuroprotective immune cell states and developing mathematical abstractions of immune system data for clinical prediction tasks.