Research Associate
Seattle, Wa, Us
Built high-performance, high-fidelity computational pipelines for integrating and clustering scores of batches of (moderately) high-dimensional cytometry data (e.g., ~20 dimensions in flow cytometry or ~ 50 dimensions in CyTOF data). The cells belong to participants in large-cohort, longitudinal clinical studies of disease, or vaccination, and the cell snapshots provide estimates of the respective amounts of 20+ different surface protein complexes on every cell. Using respective cluster frequencies over time, per participant and per blood (or per tissue) sample, we quantify phenotypic and functional immune cell responses to infection, vaccination and/or disease, in relation to clinical information on the individuals participating in a given study.At Fred Hutchinson Cancer Center, in the Evan Newell Lab, I've applied statistical learning, computational science, and mathematics to arrive at dimensionality reduction and clustering routines effective for integrating batches of cytometry data for a growing list of biological contexts (e.g., COVID, acute leukemia, CAR T immunotherapy). Cluster validation and verification is paramount, and there remains room for advancing aspects of quantitative systems immunology in this respect.