Tuesday, January 25, 2022 - 3:45pm
Event Date Details:
This will be an Zoom streamed event.
- Physics Department Colloquium
Nina Miolane, UCSB
Riemannian Geometry: From General Relativity to Biomedical Imaging
Riemannian geometry is a branch of mathematics known to provide the theoretical foundations of General Relativity. Yet today, Riemannian geometry is being rediscovered with a completely new purpose. In computational medicine for example, researchers couple it with machine learning to analyze the statistical properties of algorithms related to the automatic diagnosis of neurodegenerative diseases.
This talk introduces Geometric Statistics, a statistical framework on Riemannian manifolds with associated open-source effort Geomstats. We illustrate the use of Geometric Statistics in the context of biological shape analysis in computational medicine.
Nina Miolane received her M.S. in Mathematical Physics from Ecole Polytechnique (France) & Imperial College (UK), and her Ph.D. in Computer Science from INRIA (France) in collaboration with Stanford University. 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.
December 28, 2021 - 9:59am