Machine Learning Dynamics in the Tumor Microenvironment
Are you a cancer researcher trying to tackle the statistical and computational challenges of analyzing and integrating data types in the tumor microenvironment (TME)? Recent genomic technologies that measure cell features present exciting opportunities to study the heterogeneity of cells and characterize complex interactions in the TME.
Join Columbia University’s Dr. Elham Azizi as she presents a set of statistical machine learning methods for inferring the temporal and spatial dynamics of cells in the TME. She will show their application in the characterization of spatial dynamics in aggressive metaplastic breast cancer, revealing how metabolic reprogramming is shaping immunosuppressive niches. Additionally, she will present a systematic dissection of coordinated immune cell networks in an established adoptive cellular therapy, donor lymphocyte infusion in relapsed leukemia.
To view upcoming speakers or recordings of past presentations, visit the CBIIT Data Science Seminar Series webpage.
The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.
Dr. Azizi is an assistant professor of biomedical engineering at Columbia University. She was a postdoctoral fellow in the Dana Pe’er Lab at Columbia University and Memorial Sloan Kettering Cancer Center. Her multidisciplinary research utilizes novel machine learning techniques and single-cell genomic and imaging technologies to study the dynamics and circuitry of interacting cells in the tumor microenvironment.