Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular
responses to perturbations such as therapeutic interventions and vaccines. Gene relevance …

Many modern spatial models express the stochastic variation component as a basis
expansion with random coefficients. Low rank models, approximate spectral …

Gaussian graphical models are widely used to represent correlations among entities but
remain vulnerable to data corruption. In this work, we introduce a modified trimmed-inner …

We propose a new modeling framework for highly multivariate spatial processes that
synthesizes ideas from recent multiscale and spectral approaches with graphical models …

In this paper, we study the problem of inferring spatially-varying Gaussian Markov random
fields (SV-GMRF) where the goal is to learn a network of sparse, context-specific GMRFs …

We study the problem of inferring sparse time-varying Markov random fields (MRFs) with
different discrete and temporal regularizations on the parameters. Due to the intractability of …

Gaussian graphical models have been well studied as a way to represent the relationships
between various entities, and numerous algorithms have been proposed to learn the …

How the brain uses feedback information during speech to maintain fluency is a complex
and unresolved process. Additionally, how alterations to feedback can be utilized for speech …

We introduce new global and local inexact oracle concepts for a wide class of convex
functions in composite convex minimization. Such inexact oracles naturally arise in many …

In this thesis, we present algorithms to allow agents to estimate certain properties in a
robust, online, and distributed manner. Each agent receives a sequence of observations …