Applied Geometry

Shape Analysis

Shapes appear in various forms in medical image analysis and biology, e.g. as 2D surfaces of the human hippocampi or curves representing the outline of a butterfly wing. Analysing the progression of shapes during diseases such as Alzheimer’s disease allows organ shapes to be used as biomarkers for diagnosis and prognosis. Shape analysis also has import application in morphometrics, the study of animal shape changes in biology. More generally, shapes can be considered any object that can be deformed, thus also including complex images such as diffusion weighted MR images in the category of shapes. Mathematically, shape deformations happen by the action of diffeomorphism groups, and geometry of the diffeomorphism groups and its subgroups therefore play important roles in shape analysis. Shape spaces are generally infinite dimensional nonlinear Riemannian manifolds, and statistical shape analysis must thus handle both nonlinearity and infinite dimensionality simultaneously. Focus areas of the work performed in the group is stochastic models for shape evolution and subsequent statistical inference of the parameters governing the stochastically changing shapes. This includes phylogenetic inference using shape data.

Geometric Statistics

Geometric statistics concerns the statistical analysis of nonlinear data, particularly data residing in geometric spaces such as manifolds. Examples include shapes and shape spaces, tensors as observed in diffusion imaging, protein angles, and directional data. Even the most classical constructions in statistics, e.g. the mean value, are difficult to define in the geometric setting (for the mean, the fact that shapes cannot be added or multiplied by scalars immediately rules out the classic mean estimator of summing observations divided by the number of observations). We focus on probabilistic interpretations of mean values and covariance, and asymptotics of manifold means (central limit theorems of diffusion means). This includes stochastic processes on Riemannian manifolds, and bridge sampling on manifolds and shape spaces.

The software packages Jax Geometry and Theano Geometry showcases many of the geometric statistics constructions we work with.

sub-Riemannian geometry

We apply sub-Riemannian constructions for analysis of geometric data, particularly exploring how extremal paths for sub-Riemannian metrics in the frame bundle encode most likely transitions between mean and data points in the presence of non-trivial covariance; how PCA-like dimensionality reduction on manifolds can results in non-integrable distributions and hence allow modelling of higher dimensional data; and reduction theorems on Lie groups and homogeneous spaces for sub-Riemannian metrics.

Geometric Deep Learning

Convolutional neural networks classically take image on 2D domains as input. Geometric deep learning aims at extending the class of input data to allow curved domains or more general data with geometric structure. We tackle both foundational questions in geometric deep learning such as the definition of convolution operators on geometric domains and application of geometric deep learning methods in specific contexts such as for diffusion weighed images.

• Math on Long Island, July 2022
• Workshop on Stochastic Morphometrics, June 8-10 2022