Welcome to my home page! My name is Nina, I am a postdoctoral researcher and lecturer in the Statistics Department of Stanford University, USA. I work with Susan Holmes, Russell Poldrack and Xavier Pennec.
My research aims to create computational representations of the human body, at different scales. At the macroscopic scale, I am interested in learning quantitative descriptions of organ shapes and functions, together with their normal and pathological variations in the population; for example, learning the manifold of the brain anatomy or the manifold of the brain activity during certain functional tasks. At the microscopic scale, I am interested in studying cells shapes and functions, and at the nanoscopic scale, molecular shapes and functions. My goal is to leverage these heterogeneous multi-scale representations to implement new computer-assisted diagnosis methods and develop innovative treatments for diseases.
My methodological interests lie at the intersection of Geometry, Statistics and Computer Science.
Geometric Statistics: Statistical theory for data belonging to non-linear spaces, like metric spaces and Riemannian manifolds. Shape data, or weighted graph data naturally belong to such spaces, while high-dimensional data can be naturally projected to such spaces.
Dimensionality reduction in non-linear spaces: Fréchet mean, Submanifold learning in metric spaces and Riemannian manifolds, with a special interest for manifolds with additional properties, like Lie groups and Quotient spaces.
Fast implementation of the above techniques: Variational inference and variational autoencoders for submanifold learning.
Statistical properties of the above techniques: Asymptotic studies and bias-correction methods.
My application domains are medical imaging and biological imaging, with a special interest in brain data:
Brain shapes, as observed in brain MRIs (macroscopic scale),
Brain BOLD (blood oxygen level dependent) activation, as observed in brain functional MRIs (macroscopic scale),
Brain structural and functional connectomes (macroscopic scale),
Neuronal electric signals, either from EEG or from deep brain implants (macroscopic or microscopic scale),
Molecular imaging using cryo electron microscopy (nanoscopic scale).
Beside my research interests, I am a lecturer for the classes “Introduction to Statistical Methods: Precalculus“ (2019) and “Statistical Methods for Engineering and the Physical Sciences“ (2018, 2019) at Stanford University. I am a reviewer for the scientific journals Journal of Mathematical Imaging and Vision JMLR (2015, 2017), Biometrika (2018) and the Journal of Mathematical Neuroscience (2019), as well as a reviewer for the conferences NeurIPS (2016, 2018, 2019), International Conference of Machine Learning ICML (2019), Geometric Science of Information GSI (2017, 2019). I am a member of the scientific committee of the Conference of Geometric Science of Information (GSI) (2017, 2019) and I was one of the two lead organizers of the workshop on Brain Computing at the Berkeley-Inria-Stanford annual meeting (2017).