Little is known about how exposure to limited socioeconomic resources (SER) in childhood
gets “under the skin” to shape brain development, especially using rigorous whole-brain …

In network analysis, within-community members are more likely to be connected than
between-community members, which is reflected in that the edges within a community are …

This paper investigates the computational and statistical limits in clustering matrix-valued
observations. We propose a low-rank mixture model (LrMM), adapted from the classical …

Graph embeddings, a class of dimensionality reduction techniques designed for relational
data, have proven useful in exploring and modeling network structure. Most dimensionality …

Community structure in networks is observed in many different domains, and unsupervised
community detection has received a lot of attention in the literature. Increasingly the focus of …

Structured sparsity is an important part of the modern statistical toolkit. We say a set of model
parameters has block diagonal sparsity up to permutations if its elements can be viewed as …

Many statistical settings call for estimating a population parameter, most typically the
population mean, based on a sample of matrices. The most natural estimate of the …

In the past decade, there has been a surge of interest in processing and analyzing mixture
matrix-valued models arising in many scientific fields such as genomics, neuroimaging, and …