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).

Feel free to contact me at nmiolane at stanford.edu. You can follow me on: Github, LinkedIn, Twitter: @ninamiolane.