Welcome to my home page!

My name is Nina, I am an Assistant Professor of Electrical and Computer Engineering at UC Santa Barbara. My research group aims to create computational representations of the human body at different scales - thus contributing to the development of Computational Medicine. Before joining UC Santa Barbara, I performed my postdoctoral work in Statistics at Stanford under the supervision of Susan Holmes, and my doctoral work in Computer Science at Inria in the Asclepios-Epione team under the supervision of Xavier Pennec.

To Students: M.Sc. and PhD positions are available! If interested, contact me at ninamiolane at ucsb.edu.

To Postdocs: Open position in geometry and deep learning to reveal the structure of membrane proteins, see details here.

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 multiscale 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).

You can follow me on: Github, LinkedIn, Twitter: @ninamiolane.