Computational Medicine, Computational Biology, Shape Data Analysis, Biomedical Imaging, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Geometry, Geometric Deep Learning
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 for a year and was decorated for succeeding in a commando training program. After her studies, Nina spent two years at Stanford University in Statistics as a postdoctoral fellow, and worked as a deep learning software engineer in the Silicon Valley.
At UCSB, Nina directs the BioShape Lab, whose goal is to explore the "geometries of life". Her research investigates how the shapes of proteins, cells, and organs relate to their biological functions, how abnormal shape changes correlate with pathologies, and how these findings can help design new automatic diagnosis tools. Her team also co-develops the open-source Geomstats library, a software that provides methods at the intersection of geometry and machine learning, to compute with geometric data such as biological shape data.