Nina Miolane

Nina Miolane is currently an advisor for machine learning at Atmo. Prior to this, they worked as a software engineer for computational medicine at Bay Labs, Inc. from March 2017 to March 2018. Nina then took a position as a postdoc in statistics for neuroimaging and cognitive neuroscience at Stanford University from April 2018 to July 2020. Miolane's previous experience also includes working as a PhD candidate in geometric statistics for medical imaging at INRIA & Stanford University from November 2013 to December 2016. In this role, they conducted theoretical research on topics such as submanifold learning through prior on geometry, geometric analysis of statistical estimators, non parametric statistics on stratified spaces, and neurogeometry: sub-riemannian geometry inspired by the visual cortex and applied to computer vision. Additionally, their applied research interests include computational anatomy, image registration and segmentation, template computation, and in-painting (3D images).

Nina Miolane graduated from Lycée Sainte-Geneviève with a degree in mathematics. Nina then attended École Polytechnique, where they earned a Bachelor of Science in Mathematics, Computer Science. Next, they completed a Master's degree in Theoretical Physics at École Polytechnique. After that, they pursued a Master's degree in Theoretical and Mathematical Physics at Imperial College London. Finally, they completed a postgraduate degree in Mathematics and Statistics at Stanford University.