In this episode we’re joined by Nina Miolane, researcher and lecturer at Stanford University.
Subscribe: iTunes / Google Play / Spotify / RSS
Nina and I recently spoke about her work in the field of geometric statistics in machine learning. Specifically, we discuss the application of Riemannian geometry, which is the study of curved surfaces, to ML. Riemannian geometry can be helpful in building machine learning models in a number of situations including in computational anatomy and medicine where it helps Nina create models of organs like the brain and heart. In our discussion we review the differences between Riemannian and Euclidean geometry in theory and practice, and discuss several examples from Nina’s research. We also discuss her new Geomstats project, which is a python package that simplifies computations and statistics on manifolds with geometric structures.
Thanks to our Sponsor!
I’d like to send a huge thanks to our friends at IBM for their sponsorship of this episode. Are you interested in exploring code patterns leveraging multiple technologies, including ML and AI? Then check out IBM Developer. With more than 100 open source programs, a library of knowledge resources, developer advocates ready to help, and a global community of developers, what in the world will you create? Dive in at ibm.biz/mlaipodcast, and be sure to let them know that TWiML sent you!
About Nina
Mentioned in the Interview
- geomstats
- An Introduction to Riemennian Geometry
- TWiML Presents: Series page
- TWiML Events Page
- TWiML Meetup
- TWiML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0
Joe Dinius
I think your `geomstats` link is a copy-paste error. It points to `StockTwits`. Great podcast. Thanks much!