Nina Miolane

Research Interests

Geometric Statistics, Geometric Deep Learning, Topological Deep Learning, Equivariant Deep Learning, Shape Analysis, Computational Medicine, Theoretical Neuroscience, Computational Neuroscience

Bio

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 develops tools 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.

Research fundings include an NSF Career Award, an NIH R01 grant, a NSF SCALE MoDL grant, Google Season of Code and Noyce Initiative UC Partnerships grant for the University of California Women's Brain Initiative. Nina was the recipient of the L'Oréal-Unesco for Women in Science Award, the Hellman Fellow award and is involved in outreach initiatives in France and in the U.S. At UCSB, Nina's work was recognized by an Academic Senate Faculty Grant and a UC Regent's Junior Faculty Award. In her free time, Nina adventures in the golden state's outdoors, hiking in the mountains, riding motorcycles along the coast, or piloting single-engine airplanes in the Californian skies.