Speaker
Abstract: The immune system is a complex, adaptive network spanning molecules, cells, tissues, and organs. With the advent of high-dimensional single-cell and spatial omics, immunology is entering a data-rich era and that urgently demands integrative and interpretable computational frameworks. In this talk, I will discuss how artificial intelligence (AI) can transform immunological discovery and translation. I will highlight our efforts to build virtual immunity, an AI-driven framework that models the immune system at cellular, tissue, and systemic levels. This includes deep-learning methods for large-scale single-cell integration (DISCO), spatial organization (GraphST, SpatialGlue, STAMP), and digital twin modeling (ImmunoTwin) that predicts immune aging and responses across contexts. Together, these approaches aim to construct a human immune atlas and an interpretable, data-driven "virtual immune system" to accelerate precision immunology and therapeutic innovation. The immune system is a complex, adaptive network spanning molecules, cells, tissues, and organs. With the advent of high-dimensional single-cell and spatial omics, immunology is entering a data-rich era and that urgently demands integrative and interpretable computational frameworks.
In this talk, I will discuss how artificial intelligence (AI) can transform immunological discovery and translation. I will highlight our efforts to build virtual immunity, an AI-driven framework that models the immune system at cellular, tissue, and systemic levels. This includes deep-learning methods for large-scale single-cell integration (DISCO), spatial organization (GraphST, SpatialGlue, STAMP), and digital twin modeling (ImmunoTwin) that predicts immune aging and responses across contexts. Together, these approaches aim to construct a human immune atlas and an interpretable, data-driven "virtual immune system" to accelerate precision immunology and therapeutic innovation.
Zoom: https://duke.zoom.us/j/93703663288?pwd=9zRBNe7RMcxCGAKGUfTZJx0MXuMaAh.1
Meeting Id: 937 0366 3288
Passcode: 103794
Event Series