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In this episode we're joined by Nina Miolane, researcher and lecturer at Stanford University.
Nina and I recently spoke about her work in the field of geometric statistics in machine learning. Specifically, we discuss the application of Riemannian geometry, which is the study of curved surfaces, to ML. Riemannian geometry can be helpful in building machine learning models in a number of situations including in computational anatomy and medicine where it helps Nina create models of organs like the brain and heart. In our discussion we review the differences between Riemannian and Euclidean geometry in theory and practice, and discuss several examples from Nina's research. We also discuss her new Geomstats project, which is a python package that simplifies computations and statistics on manifolds with geometric structures.
