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Community Mourning Practices in Historic Building Demolition
(2026-09-16) Wright, Harriet S; Chalana, Manish
As the National Historic Preservation Act approaches its 60th anniversary, the field of historic preservation faces a critical gap: regulatory tools have been developed to protect buildings yet no framework exists for responding to their loss once demolition becomes unavoidable. This qualitative study documents community-based mourning practices surrounding historic building demolition in Seattle, Washington. The work argues that acts of mourning are a legitimate and undertheorized form of preservation practice. Drawing on a repository of eleven case studies and five semi-structured interviews with artists, architects, community organizers, and preservation professionals, this research demonstrates that communities actively develop remembrance practices to reminisce and let go when formal preservation pathways are unavailable. Recent Washington State legislation reducing community standing in the landmark designation process makes these findings especially timely. This study argues that preservation practice must expand beyond saving structures and work to support communities through building demolition. This work is currently carried out by individuals and small organizations that exist without institutional recognition or support. By folding this work into historic preservation practice, we are able to better meet the foundational goals of the National Historic Preservation Act.
How to Afford Community Land Trusts: A Discussion Amongst Comparative Case Studies
(2026-09-16) Song, David; Campbell, Christopher; Chaudhry, Raheem
Community Land Trusts (CLTs) are nonprofit organizations that obtain and separate land from property and lease ownership to a household through a ground lease structure that provides permanently affordable housing to low-income and moderate-income households. Despite their growing role in U.S affordable housing policy, little empirical research examines how CLTs sustain themselves financially, the environmental context, and the organizational mechanisms that enable them to meet their mission. This study addresses the gap by examining “How do successful CLTs manage financial health and what are the differences among them?” Using a mixed-methods approach, this study interviews executive and financial directors of CLT organizations and supports their findings with five years of audited financial statement analysis from 2020 to 2024. Organizations are selected based on Wang’s (2025) financial indicators of location, portfolio type, and age. The study uses Myser’s (2016) theoretical framework of financial health and the practical methods of Kioko and Marlow (2023) of financial analysis ratios and benchmarks. Qualitative data were analyzed using a thematic coding approach that intersects with Myser’s temporal framework. Findings show that all four organizations maintained positive budget surpluses and showed asset growth across the study period. However, liquidity metrics are more constrained among traditional CLTs than among their hybrid counterparts. Qualitative findings identified four themes: revenue constraints in the ground lease model; portfolio type and mission scope; external factors and relationships; and long-term risks of changing landscape. The study supports Wang’s (2025) finding that CLTs’ traditional model has become less self-sufficient and more reliant on external funding, but this may imply that financial health should somehow factor into the relationship with the local community. The study contributes to the literature by providing the qualitative context that quantitative studies of CLT financial health cannot understand the “why”. The findings align with Prentice's (2016) finding that external context plays a factor in accounting ratios as predictors of nonprofit financial health. Due to limitations, conclusions should be used with caution, and solutions may vary depending on the organization and its environmental context.
Variational Problems in Feature Selection and Data Compression
(2026-09-16) Kokot, Alex; Meila, Marina; Luedtke, Alex
Feature selection and data compression are fundamental problems in dimension reduction and representation learning. This dissertation develops a common variational framework for these tasks, posing them as quadratic optimization over spaces of probability distributions. The first substantive chapter introduces the local expected gradient outer product (local EGOP) motivated by high-dimensional nonparametric regression. It is utilized in a recursive feature-learning algorithm which yields an intrinsic learning rate for signals parameterized by low-dimensional manifolds. The second substantive chapter develops CO2 for selecting convexly weighted coresets to approximate measures with respect to smooth divergences. Second-order Hadamard differentiability turns a divergence's Hessian into a loss-adapted kernel, reducing local compression to maximum mean discrepancy minimization and kernel quadrature. For the Sinkhorn divergence, this geometry is equivalent to a scaled Gaussian reproducing kernel Hilbert space and supports coresets with poly-logarithmically many observations. The final substantive chapter analyzes entropic self-transport as the regularization parameter $\varepsilon$ tends to zero. A second-order expansion of the symmetric Schr\"odinger potential makes explicit its relationship to Gaussian-kernel estimators of density, score, and diffusion.
Leveraging Graph Structure for Optimal Design, Estimation, and Inference under Interference
(2026-09-16) Thiyageswaran, Vydhourie; Fazel, Maryam; McCormick, Tyler
Many systems of scientific interest are composed of interacting and interdependent units.In such settings, observations cannot generally be treated as independent, and in experiments, one unit's treatment assignment may affect the outcomes of other units through interference. This dissertation studies inference, testing, estimation, and experimental design in these dependent-data settings, with a particular focus on leveraging network structure. First, I develop general covariance-based sufficient conditions for multivariate central limit theorems withdependent triangular arrays. The framework allows nonzero dependence between all observations and I show how it encompasses several commonly studied dependence structures. Second, I study conditional randomization tests for detecting network interference by conditioning on focal units, deriving power characterizations that motivate optimization-based focal-unit selection. Third, I consider treatment-effect estimation when the correct exposure threshold is unknown. I propose a data-adaptive procedure that estimates the bias--variance trade-off across candidate exposure thresholds and selects the threshold minimizing estimated mean squared error. Finally, I study optimal experimental design under network interference, homophily, and heterogeneous variation. I derive worst-case mean-squared-error bounds that lead to an optimization over the covariance matrix of the treatment assignment, and develop design procedures based on semidefinite programming with Gaussian rounding and vector balancing via the Gram--Schmidt Walk. Together, these results study how dependence structure can be used at different stages ofstatistical analysis: to justify inference, improve detection of interference, adapt treatment-effect estimators, and construct optimal experimental designs.
Bayesian Small Area Estimation Methods for Sparse Complex Survey Data
(2026-09-16) McGovern, Alana; Wakefield, Jon
Small area estimation (SAE) of health and demographic outcomes in low- and middle-income countries (LMICs) is central to policy evaluation and resource allocation, yet it is complicated by the sparsity and complex design of the survey data on which such estimates typically rely. Household surveys like the Demographic and Health Surveys (DHS) are generally powered at the first administrative (admin1) level, so design-based estimates at lower levels are often imprecise or unavailable, particularly for rare outcomes. Model-based approaches can address this sparsity by borrowing strength across areas through spatial random effects, but this borrowing can induce extreme shrinkage when data are limited, affecting both point and variance estimates. This dissertation develops a set of Bayesian hierarchical spatial models which address instability and excessive shrinkage in SAE, motivated throughout by subnational estimation of child health outcomes in LMICs. The first methodological contribution is the direct-assisted Bayesian unit-level (DABUL) model, designed for estimating rare event prevalence at an administrative level too sparse to support standard area-level models. The DABUL model incorporates higher-level design-based estimates from the same underlying data as benchmarks via either a hard constraint or a soft constraint which accounts for uncertainty of the benchmarks. By applying this method to simulated data and neonatal mortality rate (NMR) estimation in Zambia using the 2013 DHS, we show that the soft DABUL model reduces aggregation discrepancy and both variants of the DABUL model yield more conservative intervals than those under a standard unit-level model, which may be particularly preferable for extreme areas. The second contribution addresses a different source of instability. The design-based variance estimates used as input in Fay-Herriot (FH) models are themselves estimated with error which is frequently ignored despite their affect on interval estimation, particularly when sample size is low. Two candidate sampling distributions for the design variance estimator are proposed and incorporated into a variance-smoothing FH framework. One sampling distribution incorporates complex survey design assumptions, while the other, simpler sampling distribution relies on stronger design assumptions. A simulation study and application to height-for-age z-scores in Kenya using the 2022 DHS show that both variance-smoothing approaches outperform a standard FH model without variance smoothing under proper scoring rules, with the simpler sampling distribution recommended for its more favorable shrinkage behavior and ease of implementation. The third contribution extends multivariate areal spatial modeling frameworks from the disease mapping literature to the FH setting and proposes a class of multivariate latent models using a BYM2 spatial structure that includes independent, correlated, and shared-component variants. Theoretical results characterize how the conditional posterior mean and variance of a primary outcome depend on the relationship between sampling and latent covariance structures. A simulation study and application to NMR estimation in Kenya using the 2022 DHS show that joint modeling with correlated outcomes can substantially improve estimation of a noisy primary outcome relative to a univariate FH model, particularly when latent correlation exceeds sampling correlation or auxiliary outcomes carry lower uncertainty than the primary outcome. Together, these three contributions demonstrate that explicitly modeling design-based sampling error and structured dependence across administrative levels, areas, or outcomes, offers a coherent strategy for improving SAE for LMICs in the data-sparse settings characteristic of complex surveys.
Geometry-Aware Statistical Learning and Causal Inference for Complex Observational Data
(2026-09-16) Zhang, Yikun; Chen, Yen-Chi
Motivated by cosmic web detection and related problems in astronomy, this dissertation develops statistically principled and computationally efficient methods for analyzing complex observational data. Three major challenges arise from such data: (i) nonlinear geometric structure in the observational domain, (ii) high dimensionality and missingness in measured features, and (iii) confounding and other causal assumption violations when interpreting scientific relationships from observational studies. The first part of the dissertation addresses the geometric challenge in (i). In Chapter 2, we introduce directional density ridges as statistical surrogates for representing low-dimensional high-density structures on the sphere. These surrogates provide geometry-aware models for cosmic nodes and filaments while respecting the spherical geometry of sky observations. We establish stability and statistical consistency guarantees for directional density ridge estimation based on kernel smoothing. In Chapter 3, we derive practical algorithms for recovering directional density ridges from data and study their linear convergence properties. In Chapter 4, we apply the statistical and computational framework of directional density ridges to galaxy and quasar observations from the Sloan Digital Sky Survey IV, producing a tomographic cosmic web catalog. The second part of the dissertation develops statistical inference methods for the high-dimensional, incomplete, and confounded observational settings described in (ii) and (iii). In Chapter 5, we study high-dimensional linear regression with missing outcomes and propose an efficient debiased inference procedure for low-dimensional functionals of the regression function. When the missingness probabilities are consistently estimated at the observed data points, our proposed estimator is asymptotically normal and semiparametrically efficient among all asymptotically linear estimators. We apply this method to study associations between galactic stellar mass and nearby cosmic web structures. In Chapter 6, we turn from association analysis to causal inference for continuous treatments in observational studies, focusing on settings where the usual positivity condition may fail. We propose novel identification and estimation strategies for the dose-response curve and its derivative under an additive structural model for the counterfactual outcome. Using tools from nonparametric set estimation, we construct inverse probability weighted and doubly robust estimators that remain unbiased under certain positivity violations. We further introduce an extension of the classical dose-response curve based on the nearest feasible treatment policy regime, which adapts to the geometry of the conditional treatment support and accounts for treatment-level heterogeneity. Across these causal settings, we establish asymptotic normality at standard nonparametric rates of convergence, enabling valid statistical inference. We illustrate the proposed causal framework with an application to the IllustrisTNG simulation data, studying the effect of local environment on galactic star formation rate and providing a new causal perspective on the "Nature versus Nurture'' debate in galaxy evolution.
A Modified “Be Clear” Treatment for Ataxic Dysarthria: Targeting Cognitive Load and Generalization
(2026-09-16) Costello, Isabelle Nora Grace; Spencer, Kristie
Ataxic dysarthria is a motor speech disorder that often leads to reduced speech intelligibilityand naturalness, which can negatively impact communication participation and quality of life. The present study is a partial replication of the Olegario and Spencer (2025a,b) treatment approach based on previous Clear Speech-based interventions, which emphasized overarticulation and slowed rate of speech (see Lowit et al., 2023; Park et al., 2016; Whelan et al., 2022). Modifications to the original protocol were made to intentionally target cognitive load, generalization, and communicative participation (Gartner-Schmidt et al., 2016; Rosenbek, 2017; Yorkston et al., 1996). The eight-week remote treatment program consisted of individual and joint group sessions for two speakers with mild-moderate ataxic dysarthria. Blinded perceptual ratings of articulatory precision and speech naturalness were collected from ten listeners across three time points (pre-treatment, post-treatment, one-month maintenance). Additional outcome measures included transcription intelligibility and measures of communication participation and psychosocial impact. Findings were partially consistent with those reported by Olegario and Spencer (2025a,b) and suggested improvements in perceptual ratings of precision but not naturalness. Improvements in transcription intelligibility were present but less pronounced, while improvements in participant-reported communicative participation and psychosocial impact were more consistent and greater in magnitude. Further research is necessary to assess generalizability of the findings in the broader population of individuals with ataxic dysarthria.
The Acoustic Characteristics of Infant-Directed versus Adult-Directed Speech and Song
(2026-09-16) Hippe, Lindsay; Zhao, Christina
Infant-directed (ID) speech is characterized by increased pitch, greater pitch variability, slower rate, and greater vowel space compared to adult-directed (AD) speech. Less is known about ID song, but research suggests that it exhibits increased mean pitch and longer vowel durations when compared to ID speech. The present study investigates the acoustic characteristics of the corner vowels (/i/, /u/, and /ɑ/) in ID and AD song and speech. ID vocalizations were characterized by significantly higher fundamental frequency (f0), duration, vowel space, global vowel dispersion, and within-category vowel dispersion compared to AD vocalizations. Similarly, song was associated with significantly higher measures of f0 and length than speech but was uniquely characterized by reduced f0 variability and increased duration variability when compared to speech. The increase in f0 and f0 variability associated with infant-directedness was much smaller in song than in speech, suggesting that acoustic modifications associated with ID vocalizations are constrained in song.
Characterizing the Variability of Second Formant Frequencies in Vowels of Dysarthric Speech Classified by Diphthong and Monophthong
(2026-09-16) Kobata, Caitlyn Kiyomi; Ingvalson, Erin
This study investigated the effects of variability in the second formant (F2) of monophthongs and diphthongs on intelligibility in patients with dysarthria. The research also examined the relationship between lexical stress and intelligibility. Data from 4 speakers and 30 listeners were collected from previous studies and retrospectively analyzed. Monophthongs were measured at onset, midpoint, and offset according to the fixed-point method while diphthongs were measured at the vowel onset and offset. The variability of these measurements were correlated with intelligibility via a linear-regression model. No significant effect was found for stress. Variability of monophthongs at midpoint and offset had a direct relationship with intelligibility, while variability of diphthongs at onset demonstrated an inverse relationship with intelligibility.
Rethinking the Quiet Revolution: Noncompliance, Mortality, and Public Discourse in the Era of Hospital Desegregation
(2026-09-16) Allen, Courtney; Curran, Sara R
This dissertation investigates hospital desegregation in Mississippi, with special attention to hospital holdouts—institutions that remained segregated despite federal pressure and financial incentives. The Civil Rights Act of 1964 prohibited discrimination in federally funded institutions, and with the implementation of Medicare in 1966, nearly all 3,000 previously segregated hospitals were integrated within a few months to receive federal funding from the Medicare program. This became known as the “quiet revolution” of hospital desegregation. Compared with other areas of desegregation, hospitals have garnered far less empirical investigation and social science interest. This work interrogates the “quiet revolution” by examining compliance, mortality outcomes, and local newspaper discourse in Mississippi during the hospital desegregation years of 1966 to 1970. I describe the onset of hospital desegregation, highlighting the uneven and slow pace of compliance across several Mississippi counties. These findings also reveal that Black mortality increased in counties that were fully desegregated as early as 1966, and counties with hospital holdouts had the highest mortality rates throughout the period. Local newspaper discourse illustrates county-level narratives and strategies of resistance that reorganized practices into new implicit forms of de facto segregation. These patterns show that hospital desegregation was not a quiet revolution, but rather a contested process that may have preserved inequality within hospitals. These results provide evidence that hospitals, as inequality-generating machines, were important sites of contestation for equality during the Civil Rights Era.