Miolane's BioShape Lab URL to be posted soon! Until then here's the link to her personal website.
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), her Ph.D. in Computer Science from INRIA (France) in collaboration with Stanford, and spent two additional years at Stanford in Statistics during her postdoc. She also worked as a deep learning software engineer in Silicon Valley for several years. 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 is also co-developing the open-source Geomstats library, a package that provides methods at the intersection of geometry and machine learning, to compute with geometric data such as biological shape data.
Nina has extensively published in the field, is a co-author on the reference book "Riemannian Geometric Statistics For Medical Imaging", a co-inventor on several patents, and serves on the scientific committees of several international conferences. Research fundings include a NIH R01 grant on Biological and Mathematical Science and the NSF SCALE MoDL grant on Mathematical and Scientific Foundations of Deep Learning. Nina was the recipient of the L'Oréal-Unesco for Women in Science Award, is involved in several outreach initiatives in France and in the U.S, and is a private pilot enjoying the proximity of SBA airport to UCSB's campus.