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Geometric algorithms for interpretable manifold learning
This thesis proposes several algorithms in the area of interpretable unsupervised learning.Chapters 3 and 4 introduce a sparse convex regression approach for identifying local diffeomor phisms from a dictionary of ... 
Improving Uncertainty Quantification and Visualization for Spatiotemporal Earthquake Rate Models for the Pacific Northwest
The Pacific Northwest (PNW) has substantial earthquake risk, both due to the offshore Cascadia megathrust fault but also other fault systems that produce earthquakes under the region's population centers. Forecasts of ... 
Statistical Modeling of Long Memory and Uncontrolled Effects in Neural Recordings
Scientific analyses of time series data are often formalized as statistical investigations targeting one or more aspects of a complex underlying dependence structure. In the multivariate time series setting, there are three ... 
Statistical analysis of lowfrequency earthquake catalogs
Lowfrequency earthquakes (LFEs) are small magnitude (less than 2) earthquakes, with reduced amplitudes at frequencies greater than 10 Hz relative to ordinary small earthquakes. They are usually grouped into families of ... 
Causality, Fairness, and Information in Peer Review
In this dissertation, I study peer reviewthe process by which scientists evaluate one another's work for publication or fundingthrough three distinct but related lenses. I focus on multistep grant proposal peer ... 
Subnational Estimation of Period Child Mortality in a Low and Middle Income Countries Context
Child mortality is an important metric used in quantifying and monitoring the health of a population's children. Moreover, child mortality can be a key indicator of the overall health of a population, and is often used to ... 
DistributionFree Consistent Tests of Independence via Marginal and Multivariate Ranks
Testing independence is a fundamental statistical problem that has received much attention in literature. In this dissertation, we consider testing independence under two different settings. The first is testing mutual ... 
Bias Modeling for Integrating Digital Data and Conventional Surveys for Migration Estimation
Obtaining reliable and timely estimates of migration flows is critical for advancing migration theory and guiding policy decisions, but it remains a challenge. Digital data provide granular information on time and space ... 
Likelihood Analysis of Causal Models
We analyze several problems in causal inference from the perspective of maximum likelihood. Two archetypal likelihoods are primarily concerned: Gaussian likelihood for continuous data and multinomial likelihood for discrete ... 
Progress in nonparametric minimax estimation and high dimensional hypothesis testing
This dissertation is divided into two parts. In the first part, we study minimax estimation of functions and functionals in nonparametric regression models. The investigation of statistical limits in such models deepens ... 
Bayesian Models in Population Projections and Climate Change Forecast
The goal of this dissertation is to develop new methods for probabilistic projections in demography and climate science.In the first project, I propose new models for improving estimates and projections of total fertility ... 
Statistical Methods for Adaptive Immune Receptor Repertoire Analysis and Comparison
B and T cell receptors, also known as adaptive immune receptors, perform key roles in adaptive immunity. These proteins identify and deal with foreign invaders like viruses or bacteria, allowing for robust and longlasting ... 
Statistical Methods for Geospatial Modeling with Stratified Cluster Survey Data
The production of finescale, pixel level maps have become increasingly common in the current era of precision public health. This has led to the use of cluster level spatial models by major organizations such as WorldPop ... 
Metric Learning for Hermitian Manifolds
In recent years, manifold learning has emerged as one of the most promising approaches for performing nonparametric dimension reduction. While numerous manifold learning algorithms of varying degrees of complexity have ... 
Representation Learning for Partitioning Problems
This dissertation addresses representation learning for partitioning problems. Clustering a set of data points and segmenting a time series of data points are two classical partitioning problems. Nonparametric methods such ... 
Estimation and Inference in Changepoint Models
This thesis is motivated by statistical challenges that arise in the analysis of calcium imaging data, a new technology in neuroscience that makes it possible to record from huge numbers of neurons at singleneuron resolution. ... 
SpaceTime Contour Models for Sea Ice Forecasting
This dissertation develops statistical methods for modeling contours. Particular emphasis is placed on forecasting the sea ice edge contour, or the boundary around ocean areas that are icecovered. Current sea ice forecasts ... 
NonGaussian Graphical Models: Estimation with Score Matching and Causal Discovery under ZeroInflation
Graphical models specify conditional independence relations between variables. These include undirected graphical models and directed graphical models, the latter of which also capture causal relationships. This dissertation ... 
Scalable Learning in Latent State Sequence Models
In this dissertation, we develop scalable learning methods for sequential data models with latent (hidden) states. State space models (SSMs) and recurrent neural networks (RNNs) are popular models for sequential data using ... 
Bayesian Hierarchical Models and Moment Bounds for HighDimensional Time Series
In this dissertation, I explore two statistical tasks involving highdimensional time series.The first task is to forecast highdimensional time series using Bayesian hierarchical models (BHM). The data under modeling is ...