Current immunological research is driven by the advances in both cytometer capabilities and breadth of reagent availability that have led to the expansion of multicolor flow cytometry panels; however, despite this newly found ease in the generation of high-parameter cytometry data, the interpretation of resulting datasets is currently bottlenecked due the limitations of available analysis tools as well as slow adaptation of algorithmic methods within the field. Computational analysis is imperative for the proper investigation of high-dimensional flow and mass cytometry datasets. In this seminar, I will discuss our lab approaches that enable more efficient and comprehensive computational analysis of large flow cytometry datasets including full spectrum cytometry data. To demonstrate the potency of various newer and adapted computational approaches, I will focus on research scenarios where our assembly of methods was used to characterize the phenotypic landscape of various immune subsets. I will highlight our studies that reveal specific inhibitory receptor phenotypes to be investigated as potential biomarker readouts in HIV and aging and our work on profiling hematopoietic stem cells in the human fetal liver.