University of Illinois at Chicago
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Essays in Applied Macroeconomics: Evidence on the Effects of Remittances and Dollarization in El Salvador
No full textThis dissertation examines key macroeconomic dynamics in El Salvador, focusing on the impacts of remittances and dollarization.
The first chapter investigates the potential Dutch Disease effect in El
Salvador, where remittances constitute a significant share of GDP. Using
Structural Vector Autoregression (SVAR) and Local Projection (LP) methods,
the study analyzes how remittance inflows influence the tradable sector.
The findings indicate that the Tradable-to-Non-Tradable (TNT) and
Agricultural-to-Non-Tradable (ANT) ratios remain largely unchanged, while
the Manufacturing-to-Non-Tradable (MNT) ratio increases, pointing to a
Dutch Disease effect in manufacturing. These results offer new insights into
how remittances reshape sectoral composition in small, open economies.
The second chapter explores the macroeconomic consequences of El Salvador’s
full dollarization in 2001, particularly its effects on inflation, real GDP
growth, and business cycle synchronization with the United States. Employing
Autoregressive Moving Average (ARMA) models and impulse response
functions, the study finds a notable reduction in inflation post-dollarization.
However, contrary to theoretical expectations, business cycle synchronization
with the U.S. appears to have weakened. These findings highlight the tradeoffs
of dollarization, providing an applied perspective on its implications for
macroeconomic stability in El Salvador.
This research contributes to the broader literature on remittances, Dutch
Disease, and dollarization, providing empirical evidence on how these economic forces shape the structure and stability of a highly remittance-dependent, dollarized economy
Multi-Fidelity Scale Bridging Between Electronic, Atomistic and Mesoscale Using Reinforcement Learning
No full textThis thesis presents a comprehensive study on multi-fidelity scale bridging between electronic, atomistic, and mesoscale modeling for silica, utilizing reinforcement learning (RL) to address longstanding challenges in accurately predicting the structural, energetic, alongside other properties of this ubiquitous material. Silica, with its rich polymorphism and diverse applications in fields such as catalysis, water purification, glass production, and energy storage, has proven difficult to model due to the diversity of its Si-O bonding and the wide range of crystal and amorphous phases it can form. Existing models range from computationally efficient empirical potentials, such as the Beest-Kramer-van Santen (BKS) potential, to more recent machine-learned potentials (e.g., GAP, NNPScan), which offer greater accuracy but at a significantly higher computational cost. Despite decades of research, developing a model that balances accuracy with computational efficiency across the full range of silica polymorphs remains an open challenge. In this work, we introduce a novel approach that leverages RL to optimize empirical and machine-learned interatomic potentials for silica, significantly enhancing their accuracy and efficiency. Using multi-reward RL, we systematically explore the high-dimensional parameter spaces of established models, such as the BKS and Soules potentials, to derive new parameterizations that improve the prediction of key properties across various silica phases. For example, our re-parameterized BKS model (ML-BKS) captures not only the equilibrium properties of quartz but also the structure and properties of over 20 metastable silica polymorphs, with a focus on improving the model's flexibility in representing both crystalline and amorphous phases. Additionally, we introduce a new machine-learned potential (ML-Soules), optimized for computational efficiency, that performs on par with state-of-the-art models while requiring fewer computational resources. This thesis also explores three-body interactions, such as those incorporated in the Tersoff potential, to improve the descriptive power of silica models. These interactions allow for better representation of angular dependencies, which are crucial for accurately capturing the mechanical properties and phase transitions of silica polymorphs. The inclusion of three-body terms in the ML-BKS and ML-Soules models enhances their ability to capture the intricate behavior of silica, particularly in non-equilibrium states and high-pressure phases. We demonstrate that such enhancements are critical for improving the predictive accuracy of models across a wide range of polymorphs, including zeolites and amorphous structures. The thesis also introduces a machine learning force-field for silica using MACE framework which is a significant advancement in materials modeling. Unlike traditional models that rely on fixed functional forms, MACE incorporates both short-range and long-range interactions without predefined limitations, making it highly flexible. This approach enables MACE to achieve high accuracy in predicting silica’s structural and energetic landscape while maintaining computational efficiency, thus making it suitable for large-scale simulations and real-time applications like molecular dynamics. The MACE models introduced here represent a paradigm shift in silica modeling, moving away from the limitations of empirical potentials and toward a more flexible, data-driven approach that captures the full complexity of silica’s structural and energetic behavior. By integrating both two-body and three-body interactions, and by leveraging machine learning to optimize parameters, these models provide a more accurate and computationally feasible method for studying silica polymorphs under a wide range of conditions. Additionally, this reinforcement learning workflow is utilized to tune a Coarse-Grained (CG) model for silica, a critical step in developing models capable of simulating large length/time scales. This work lays the groundwork for utilizing high-quality data alongside a state-of-the-art optimization strategy to create coarse-grained models for silica. These models employ simple functional forms while incorporating improved angular resolution, marking an important advancement in the models for silica. In conclusion, this thesis demonstrates the power of multi-fidelity modeling combined with machine learning and RL to significantly improve the accuracy and computational efficiency of silica models. Our work not only advances the understanding of silica’s complex structural landscape but also provides a foundation for the design of novel force-fields or improvements to the exisiting functional forms. Through the development of enhanced interatomic potentials that bridge scales from electronic to mesoscale, this research opens new avenues for the accelerated discovery and synthesis of silica-based materials in a variety of applications
Food Insecurity’s Relationship with Children’s BMI and General Anesthesia
No full textAbstract
Purpose: Children from food insecure (FI) households often consume more calorie-dense, cariogenic foods, leading to higher BMI and increased risk for dental caries requiring treatment under general anesthesia (GA). This study hypothesizes that FI children are associated with higher BMI and increased need for GA.
Methods: A cross-sectional study was conducted with patients aged 3–17 receiving dental care at UIC COD. Legal guardians completed a survey during the child’s exam. Data on caries status, BMI, and GA need were collected from electronic records. Descriptive and bivariate analyses were performed (significance set at p<0.05).
Results: The study included 251 children (median age 7.0 years, 55% female, 61% Hispanic/Latino). Of the households, 67% had low and 26% had very low food security. Among participants, 56% were of healthy weight, while 41% were overweight or obese. GA was required for 38% of patients. No significant association was found between FI and BMI (p=0.660) or FI and GA need (p=0.089). However, subgroup analysis suggested a potential association between FI and BMI, particularly among Black participants.
Conclusions: This study found no significant association between FI and BMI or need for GA. Nonetheless, 93% of the population experienced low or very low food security, and over 40% were overweight or obese. These findings underscore the need for broader studies across more diverse populations to better understand these relationships
Logic, Learning, and Explanation: Theoretical and Applied Perspectives on Machine Reasoning
No full textThis dissertation explores the interface between logic and machine learning, focusing on both theoretical foundations and applications to interpretability. It is structured around three main themes: the logical expressivity of Graph Neural Networks (GNNs), interpretability methods for GNNs, and the use of Large Language Models (LLMs) in legal reasoning.
The first part develops a novel Ehrenfeucht-Fraïssé game tailored to counting logic with a bounded number of variables, which characterizes formula size. This provides the first known formula size lower bound in counting logic and extends existing results from three-variable first-order logic to three-variable counting logic. This work informs the theoretical limits of GNN expressivity, since a fragment of counting logic has been shown to characterize the distinguishing power of certain GNN architectures.
The second part addresses the challenge of explaining GNN predictions. One paper introduces COMRECGC, a novel method for producing global counterfactual explanations through common recourse: minimal, interpretable sets of graph modifications that reliably flip predictions across a dataset. A second paper in this section presents LOGIC, a framework that combines GNN embeddings with LLMs to generate natural language explanations grounded in both the graph structure and node attributes.
The third part applies LLMs to the domain of legal reasoning. Using context augmentation and Chain-of-Thought prompting, this work generates structured legal arguments in landlord-tenant scenarios. The generated arguments are evaluated for factual accuracy, legal relevance, and comprehensiveness, offering insights into how LLMs can support legal professionals and expand access to justice
Design of an RF Component for In-Band Full-Duplex Systems
No full textThis work explores the design, optimization, and practical implementation of an advanced
rat-race coupler with enhanced bandwidth and isolation performances for self-interference can-
cellation (SIC) in in-band full-duplex systems. The component is positioned at the antenna
interface to effectively mitigate the self-interference signal from the received signal, exploit-
ing the inherent signal-processing properties of the component. To address these challenges,
the novel approach proposed consists of the implementation of Chebyshev multi-section trans-
formers in the component structure, with an improvement of 30% in operative bandwidth and
doubling the isolation bandwidth. This thesis starts with a comprehensive theoretical analysis
of the 180-hybrid coupler, with particular emphasis on even and odd mode analysis and the
delicate amplitude and phase balance needed for optimal isolation.
The proposed structure is validated both with schematic and EM full-wave simulations using
Keysight Advanced Design System (ADS). The optimized microstrip implementation is finally
realized on a Rogers RO4003C substrate and measured, demonstrating a superior isolation level
and bandwidth with respect to a conventional structure.
This work contributes to advancing the SIC capabilities at the antenna interface by relying
on passive components only. The proposed solution reduces the dependence on complex active
cancellation techniques, supporting the goal of reducing the self-interference signal and enabling
IBFD communication for next-generation wireless technologies
In Vitro Study Comparing Treatments for Incipient Enamel Lesion Remineralization
No full textPurpose: This study compared the remineralization efficacy of Icon™, 3M™ Varnish™ 5% Sodium Fluoride White Varnish (FV), and 38% Silver Diamine Fluoride (SDF; Advantage Arrest™) on enamel surfaces affected by incipient caries lesions.
Methods: Caries-free permanent teeth, extracted for orthodontic or surgical purposes, were obtained from the Pediatric Dentistry Department at UIC College of Dentistry. Enamel samples (4 mm x 5 mm) were prepared, frozen, and subjected to baseline microhardness testing. Samples were randomized into four groups (n=5/group): Icon, FV, SDF, and a control group. Artificial caries lesions were induced and treated per manufacturers’ instructions. Samples were immersed in a saliva-like remineralization solution for seven days, and post-treatment microhardness was assessed at 20 μm intervals to a depth of 200 μm. Statistical analyses included ANOVA with Tukey-Kramer post hoc tests and Generalized Estimating Equation (GEE) modeling.
Results: SDF demonstrated the lowest microhardness values, with a mean of 147.24 (SD: 39.76) at 100 μm, significantly lower than FV (276.54; SD: 33.72), Icon (254.59; SD: 30.18), and the control (253.19; SD: 36.16). FV and Icon showed significantly higher microhardness, indicating superior enamel strengthening compared to SDF. No significant differences were found between FV and Icon.
Conclusions: When managing incipient caries, all three materials are effective options for addressing early caries lesions. However, FV and Icon provided superior remineralization and surface hardening when compared to SDF
Major Donor Philanthropy and the Effects of Social Capital
No full textThere is little extant philanthropy research on wealthy donors in the U.S. This qualitative research study is an in-depth examination of actual charitable giving experiences described by wealthy donors. It is based on interviews with 32 wealthy major donors who gave 5-7 figure donations to large nonprofit organizations in Chicago, Illinois. Areas examined include giving determinants, donor decision-making processes, and benefits derived from giving. The study also evaluates the effects of dimensions of social capital activated during relationships between major donors and people who were influential to their giving experiences. Results were derived using the methodology of interpretative phenomenological analysis. In-depth excerpts of major donor statements are provided as evidence to support the study's results. Results and insights from this study are useful for theory development by philanthropy and social capital scholars. The study also offers practical applications for use by fundraising practitioners, nonprofit executives and wealthy major donors
Essays on Health and Economics of Tobacco Control Policies
No full textThis dissertation explores the impact of tobacco control policies through two essays. The first evaluates state-level Tobacco 21 (T21) laws on maternal smoking and birth outcomes using birth certificate data from the National Center for Health Statistics (2014–2019). Employing difference-in-differences and triple-differences models, I compare pregnant persons aged 18–20 in T21 states to those in non-T21 states and to older peers aged 21–23. Results show T21 laws don't significantly affect smoking initiation during pregnancy but reduce smoking intensity by 6–9%, with robust findings across alternative specifications including the Callaway and Sant'Anna (2021) method. While T21 laws show no significant impacts on birth outcomes in the full population, among smokers they are associated with a 2–3 percentage point increase in premature births and a 2–3 percentage point decline in Cesarean deliveries.
The second essay estimates Indiana's optimal cigarette tax range of 22.12 per pack following Gruber and Koszegi's (2004, 2008) framework that incorporates both external and internal costs. Using the REMI model to simulate economic effects, I find moderate tax increases up to $7 per pack generate positive outcomes for employment, GDP, and personal income over a 20-year horizon, suggesting cigarette tax increases could simultaneously improve public health and strengthen Indiana's economy
Towards Efficient and Scalable Deep Learning on Graph-Structured Data
No full textThe practical deployment of Graph Neural Networks (GNNs), a primary form of deep learning on graphs, is hindered by intertwined challenges of effectiveness and scalability. This thesis, "Towards Effective and Scalable Deep Learning on Graph-Structured Data," proposes novel methodologies to address these limitations across four main research thrusts.
To address scalability in learning node embeddings, one paper introduces CCA-SSG, a self-supervised framework that learns robust node embeddings. It efficiently avoids the computational burden of negative sampling by using a feature decorrelation objective to prevent representational collapse.
To enhance MLP-based models, which are faster but less accurate than GNNs, two papers are presented. OrthoReg tackles an "over-correlation" issue with a soft orthogonality constraint, making MLPs competitive with leading GNNs. The second framework achieves true end-to-end MLP efficiency by offloading graph computations to a one-time pre-processing step, eliminating iterative complexity.
Finally, for training on massive graphs, this thesis proposes Data-Centric Graph Condensation (DCGC). This framework recasts condensation as a distribution matching problem, creating a small, task-agnostic synthetic graph. This approach significantly improves cross-architecture generalization and reduces condensation time compared to traditional gradient-matching techniques. The proposed models are validated on public benchmarks, demonstrating significant improvements in performance and computational efficiency
‘Memento Mori’… Remember You Will Die: Memorializing the Dead in the Digital Age
No full textThe purpose of this study is to investigate contemporary memorialization techniques involving both material and digital objects and chart the ephemera of hauntological perpetuity that drive modern-day memorialization choices and practices. Situating these modern-day techniques within the lineage of historical practices for memorializing the dead, particularly in the context of technological advancement, I make that case that in eras of ‘emergent scientism’ (Lutz, 2011), curiosity over the meaning of life, death, and the ability to ‘see beyond the veil’ is piqued. Often resulting from this curiosity are calls to renegotiate the social order as it relates to our understanding of life and death (see: OpenAI founder Sam Altman’s recent call to restructure the social contract in light of advancements in artificial intelligence). Utilizing data gathered from the Digital Legacy Association’s 2014-2024 periodical Digital Death Surveys, in-depth semi-structured interviews, and a mixed-methods autoethnography, how and why we use material and digital memento mori, or objects of remembrance, in the twenty-first century will be revealed and attitudes towards emerging digital legacy management services and AI-based tools that promise an ‘end of death’ explored. Drawing from multiple canons including metaphysical philosophy, Romantic philosophy, communication, anthropology, and media studies, the macro goal of this research study is to understand how and to what end practices of modern-day memorialization illustrate an evolution in our cultural understanding of the human subject as well as our relationship to the social systems within which we live out the complexity of our social lives. Critically, from a twenty-first century perspective, liminal experiences connected to corporeal memento mori occur outside of networked environments, allowing the subject to renegotiate her relationship with the deceased on her own terms and sans influence from the recommender systems algorithmically leveraged by platforms and other mnemotechnological environments that steer our experiences (O’Connell et al., 2022). Understanding why we may choose one form or memorialization over another (if we do at all), may provide insight into our attitudes about emerging technology, particularly those related to the emergent Digital Afterlife Industry
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