Machine Learning for Modeling Dynamics of Immune Cell States

About this Class

*** This seminar is open to the public, but registration is required. Recent genomic and imaging technologies that measure features at the resolution of single cells present exciting opportunities to characterize diverse immune cell states in various disease contexts and elucidate their circuitry and role in driving response to therapies. However, analyzing and integrating single-cell data across patients, time points, and data modalities involves significant statistical and computational challenges. Dr. Azizi will present a set of machine learning methods developed to address problems such as handling sparsity and noise, distinguishing technical variation from biological heterogeneity, inferring underlying circuitry, and inferring temporal dynamics of immune states in clinical cohorts. Dr. Azizi will also present novel biological insights obtained from applying these methods to cancer systems. These results include continuous phenotypic expansion of immune cells when interfacing with breast tumors and detecting key exhausted T cell subsets with divergent temporal dynamics that define response to immunotherapy in leukemia. Speaker: Elham Azizi, Ph.D. Herbert & Florence Irving Assistant Professor of Cancer Data Research at the Irving Institute for Cancer Dynamics Assistant Professor of Biomedical Engineering Columbia University