Geometric Statistics, Geometric Deep Learning, Topological Deep Learning, Equivariant Deep Learning, Shape Analysis, Computational Medicine, Theoretical Neuroscience, Computational Neuroscience
Nina Miolane received her M.S. in Mathematics from Ecole Polytechnique (France) & Imperial College (UK), and her Ph.D. in Computer Science from INRIA (France) in collaboration with Stanford University. She was an instructor in the French Army and was decorated for succeeding in a commando training program. After her studies, Nina was a postdoctoral fellow at Stanford University, and worked as a deep learning software engineer in the Silicon Valley.
At UCSB, Nina directs the Geometric Intelligence Lab, whose goal is to reveal the geometric signatures of natural and artificial intelligence and to build next–generation intelligent systems: Geometric AI. Her lab developstools from geometry, topology, computer vision, machine learning and deep learning which are then incorporated into the open-source packages: Geomstats and TopoX with the PyT-team.