Physical Sciences and Mathematics Commons^™

Zero-Knowledge Proofs For Verifiable Machine Unlearning, Bidur Niroula

Open Access Theses & Dissertations

Machine unlearning aims to remove the influence of deleted data from a trained machine learning model. This is important because privacy regulations such as GDPR, CCPA, and PIPEDA give individuals the right to request the deletion of their personal data. Since unlearning can be expensive, a dishonest server may skip it or return an incorrect model and this motivates verifiable machine unlearning. Existing cryptographic approaches prove that the server executed a specific training or unlearning algorithm on the committed dataset, so the proof is tied to the algorithm's trajectory. Empirical verifiers avoid this cost but are known to be circumventable …

Deep Neural Network-Gaussian Process For Housing Price Analysis, Bismark Nyarko

Open Access Theses & Dissertations

Neural networks have demonstrated remarkable predictive performance in complex, high-dimensional settings. However, their highly parameterized structure and nonlinear architecture often make them difficult to interpret, limiting formal statistical inference and principled uncertainty quantification. In contrast, Gaussian processes (GPs) provide a fully probabilistic framework with a relatively small number of hyperparameters, enabling coherent uncertainty quantification and seamless integration into hierarchical and structured statistical models. Despite their theoretical flexibility as nonparametric function approximators, standard GP formulations frequently exhibit weaker predictive performance than modern neural networks in large-scale applications.

In this work, we employ a Gaussian process covariance function designed to approximate the …

Efficient Solvers For Phase Field Models, Raymond Obeng

Open Access Theses & Dissertations

Phase field models provide a versatile framework for describing phase transitions and pattern formation in materials, enabling the study of complex phenomena such as crystallization, grain growth, and defect dynamics. Among these models, the Phase Field Crystal (PFC) equation has gained significant attention due to its ability to capture atomic-scale structures while evolving on diffusive time scales. However, the numerical solution of the PFC equation presents substantial challenges arising from its high-order nature and the large-scale, coupled linear systems generated by discretization.

In particular, standard formulations of the discretized PFC system lead to non-symmetric and often ill-conditioned linear systems, which …

Contamination-Aware Boosting For Cancer Prediction, Francis Opoku

Open Access Theses & Dissertations

Modern cancer research increasingly relies on machine learning methods to model complex relationships between high-dimensional genomic predictors and clinically relevant outcomes. Gradient boosting methods, particularly XGBoost, have become widely used due to their strong predictive performance and ability to capture nonlinear effects and interactions. However, these methods are sensitive to contamination in the data, especially in the response variable, where even a small fraction of aberrant observations can disproportionately influence model fitting and degrade generalization.

In this thesis, we propose a contamination-aware extension of XGBoost, termed CA-XGBoost, which improves robustness by explicitly modeling residuals through a latent mixture framework. This …

Computational Studies Of Alpha-Lactalbumin Binding To Perfluorodecanoic Acid, Randhal Smith Ramirez Orozco

Open Access Theses & Dissertations

This thesis investigates the structural and thermodynamic interactions between Perfluorodecanoic acid (PFDA) and bovine alpha-lactalbumin (ALAC), a critical milk protein essential for infant nutrition. The primary objectives were to computationally determine the most probable binding sites of PFDA to ALAC and to quantify the strength of these interactions alongside the structural changes they induce. This study addresses a critical gap in understanding how persistent environmental contaminants like PFDA are recruited and transported by nutritional proteins within the milk matrix. The methodology integrated high-throughput molecular docking with long-range, molecular dynamics (MD) simulations to provide a dynamic characterization of the ALAC-PFDA complex. …

Penalty Approach To Constrained Optimization Problems In Water Reservoir And Energy Generation Management, Edwin Horacio Trejo

Open Access Theses & Dissertations

The growing demand for resilient and sustainable energy generation has driven interest in Hybrid Floating Photovoltaic-Hydropower (HFPVH) systems. Operating these systems effectively requires making water release decisions that satisfy physical and regulatory constraints while maximizing energy production. Prior work by Vega (2024) developed a Dynamic Outlier Filter Long Short-Term Memory (DOF-LSTM) architecture for forecasting reservoir release patterns. That predictive work is valid and addresses an important component of the HFPVH decision pipeline. However, the constraint system in that work operates externally to the learning process, and the behavior of penalty-based constraint enforcement had not been studied independently in this context. …

Soil Microbial Responses To Artifical Light At Night, Randall L. Walker

Open Access Theses & Dissertations

Global change, driven by both natural processes and human activities, has significantly transformed ecosystems. Urbanization and artificial light at night (ALAN) are key contributors, disrupting natural light cycles and influencing biological rhythms, biodiversity, and ecosystem functions. Thus far, there remains a gap in knowledge of how soil ecosystems are impacted by ALAN. This study uses a laboratory incubation to investigate the effects of ALAN on soil microbial communities and functions across urban and natural ecosystems. Briefly, soil was collected from eight sites around El Paso, Texas and neighboring New Mexico representing urban and rural light exposure. Soil was then subjected …

Copula-Based Tests For Assessing The Association Between Genetic Variants And Mixed Phenotypes, Martin Amoah

Open Access Theses & Dissertations

High-dimensional omics studies increasingly involve heterogeneous data types and phenotypes, which traditional association methods struggle to model jointly due to incompatible marginal distributions and complex dependence structures. This thesis develops a unified copula-based framework for assessing associations between genetic variants and mixed phenotypes by decoupling flexible marginal models from their joint dependence structure. While previous copula-based approaches in this setting have focused largely on continuous and binary traits, we extend these methods to a broader class of phenotype pairs. Specifically, we introduce new association tests for bivariate outcomes involving ordinal–continuous, nominal–continuous, and survival–continuous combinations. The proposed methodology derives joint density …

Modeling Aerosol Transport During High Particulate Matter Episodes In El Paso-Juarez Region And Mitigating Mining-Related Emissions Of Rare Earth Elements (Rees) To The Air, Suzan Aranda Luna

Open Access Theses & Dissertations

This dissertation examines the use of the HYSPLIT model to develop a methodology for the transport and dispersion of air masses affecting particulate matter (PM) concentrations in the El Paso Region, and bioleaching experiments as an alternative to mitigating rare-earth concentrations in the air. Chapter 2 describes the study methodology, 3 and 4 cover the modeling methodology using two-representative high PM2.5 episodes, occurring on February 28, 2024, and June 19, 2024, respectively. These sections encompass the analysis of backward trajectories using four different meteorological datasets to build trajectory frequency maps, the exploratory model analysis of modeled outputs from lower to …

Mbse For Process Analytical Technology- Bwon Analysis Case Study, Arnaldo Garcia Cervantes

Open Access Theses & Dissertations

Volatile Organic Compound (VOC) emissions from industrial sources, particularly Benzene, present significant environmental and public health challenges. Regulatory frameworks, such as the U.S. Environmental Protection Agency’s Benzene Waste Operations NESHAP (BWON), mandate strict monitoring of control devices, specifically carbon adsorption canisters, to prevent emission breakthrough. However, current industry practices rely heavily on manual Method 21 testing, a labor-intensive process that creates lagging indicators and increases the risk of non-compliance events. This thesis proposes the design and development of an on-line, automated fugitive emissions monitoring system tailored for carbon canisters using Model-Based Systems Engineering (MBSE). Utilizing the Object-Oriented System Engineering Method …

A Combined Multi-Isotope And Machine Learning Approach To Determine Groundwater Salinity And Solute Sources, Lower Valley Area, El Paso County, Texas, Gloria A. Ortiz Gamboa

Open Access Theses & Dissertations

Groundwater salinization is increasingly troublesome in aquifers in and around El Paso, Texas, where concern for available water resources has grown due to frequent droughts and increasing population. Both groundwater and surface water have experienced an increase in total dissolved solids (TDS), a trend that can pose future health and economic problems for residents who primarily rely on groundwater. To address this issue, this research focuses on the Hueco Bolson Aquifer and the Rio Grande Alluvial Aquifer within the Lower Valley area of El Paso, Texas, utilizing a multi-analysis approach to evaluate and determine solute sources and their possible end-members …

Algebraic Approach To Data Processing: Techniques And Applications, Julio Urenda

Open Access Theses & Dissertations

In many areas of human knowledge, symmetries and invariances play an important role. In fundamental physics, starting with Relativity Theory, new physical theories have been formulated in terms of invariances and of the corresponding transformation groups – i.e., in terms what a mathematician would call an algebraic approach. In engineering, devices like wind tunnels, which are based on scale-invariance, enable us to test smaller-scale models of the actual designs. In biological sciences, symmetries and invariances are extremely important in analyzing the shape and functioning of living beings, from mammals to viruses. Invariance and symmetry – in the form of fairness …

Multi-Hop Hybrid Graph Neural Network, James Arthur

Open Access Theses & Dissertations

Graph-structured data appear across diverse domains, such as social networks, citation graphs, biological systems, and knowledge bases. Graph Neural Networks (GNNs) have emerged as a powerful framework for learning on such data, yet existing architectures face significant challenges. Graph Convolutional Networks (GCNs) suffer from over-smoothing as depth increases, Graph Attention Networks (GATs) introduce computational and statistical instabilities, and naïve multi-hop propagation inflates memory and computation while failing to adapt to topology. These limitations motivate the development of a new framework that is both expressive and scalable. This dissertation proposes the Multi-Hop Hybrid Graph Neural Network (MHHGNN), a novel architecture that …

Advancing Effective Connectivity Analysis: Robust And Sparse Group Dynamic Causal Modeling Via Extended Parametric Empirical Bayes, Godfred Arhin

Open Access Theses & Dissertations

Dynamic Causal Modeling (DCM) provides a principled framework for estimating effective connectivity in neuroimaging data, with Parametric Empirical Bayes (PEB) enabling hierarchical inference across sessions and subjects. However, standard PEB assumes Gaussian distributions at all hierarchical levels and employs conventional shrinkage priors that do not enforce true sparsity, limiting robustness to outlying subjects and reducing sensitivity to parsimonious connectivity structures. This thesis introduces a robust, sparsity-inducing hierarchical extension to DCM-PEB that addresses these limitations through two key innovations. First, a Student-t group-level likelihood replaces the conventional Gaussian likelihood, automatically downweighting outlying subject-level parameters using adaptive precision weights while retaining computational …

Microgrid Assessment And Ml-Based Power System Faults Detection Leveraging Real-Time Co-Simulation, Diego Normando Gandara Mendez

Open Access Theses & Dissertations

The rapid growth of distributed energy resources (DERs) and the increasing reliance on data-driven decision making have reshaped the operational challenges of modern electric power systems. As microgrids become more prominent in distribution networks, utilities require methods that unify planning, control, and real-time situational awareness to ensure resilient operation under faulted or uncertain conditions. The goal of this MSEE thesis is to design and validate a latency-aware ML framework for rapid, reliable fault detection in distribution grids. To achieve the goal of the thesis, there are three specific objectives. Objective 1 evaluates optimized microgrid configurations under varying DER levels and …

Lamda: A Longitudinal Android Malware Dataset For Benchmarking Concept Drift Detection And Adaptation, Md Ahsanul Haque

Open Access Theses & Dissertations

Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem, adversarial development of new malware families, and the continuous emergence of both benign and malicious applications. Prior studies have shown that such concept drift—distributional shifts in benign and malicious samples—leads to significant degradation in detection performance over time. Despite the practical importance of this issue, existing datasets are often outdated and limited in temporal scope, diversity of malware families, and sample scale, making them insufficient for …

Using Uranium And Strontium Isotopes To Identify Water Flow Paths And Solute Sources In Agricultural Upper Snake-Rock Watershed In Idaho: Understanding Agrohydrology Processes Of Dryland Critical Zone, Jennifer Herrera

Open Access Theses & Dissertations

Irrigation in agricultural systems alters hydrological cycles by redistributing surface water and groundwater, modifying Critical Zone elemental cycles, and impacting water quality and availability. Here, I focus on understanding agrohydrologic processes in Dryland Critical Zones and the impacts of land-use changes and climate variability in the extensively irrigated agricultural Upper Snake-Rock Watershed in semi-arid south-central Idaho. The Snake River originates in Wyoming and flows across southern and central Idaho. The Snake River supplies irrigation water to the Kimberly and Twin Falls areas of Idaho, the focus of this study, which is characterized by intensive agricultural activity. With the increasing pressure …

Exploiting The In-Distribution Embedding Space With Deep Learning And Gaussian Discriminant Analysis For An Out-Of-Distribution Malware Attach Detection, Tosin Olusola Ige

Open Access Theses & Dissertations

State-of-the-art machine and deep learning models generally perform well on previously seen data, albeit with wrong close world assumption that all real-world data are from previously seen train and validation samples, hence there poor performance when exposed to data which deviates from previously seen training and validation set. This is clearly evident in the domain of cybersecurity where the world continues to experience several high profile malware attacks despite advancement in state-of-the-art research. The reason being that the constant evolvement of innovation in the development of tools and method deployed to carry out various attacks had given hackers and other …

Phishibl: A Systematic Evaluation Of Instance-Based Learning Model For Predicting Phishing Susceptibility, Shova Kuikel

Open Access Theses & Dissertations

Despite enormous efforts to develop defenses against phishing attacks, humans still struggle to detect phishing emails given the constantly evolving attacker strategies. This thesis aims to test the predictive capabilities of a cognitive model that represents the individual susceptibility to phishing emails. While training programs aim to raise awareness, most remain outdated and ineffective against evolving attack strategies. Recent advances in Machine Learning, Artificial Intelligence, and Large Language Models (LLMs) offer new defenses, yet understanding human decision processes remains crucial, as effective systems must emulate how people evaluate unfamiliar emails based on prior experience. This research introduces a cognitive model …

Lorentz Invariance And Quantum Coordination: A Neo-Aristotelian Framework For Entanglement And Relativity, Angel Rafael Sosa Muniz

Open Access Theses & Dissertations

The phenomenon of quantum entanglement has been at the center of heated debates among physicists and philosophers since the dawn of the quantum era. The early discussions initiated by Einstein, Podolsky, and Rosen—regarding potential violations of the Principle of Relativity by entangled particles, expressed in the so-called “EPR Paradox”—ultimately culminated in the demonstration of Bell’s theorem and its violations. Since then, philosophers of science and physicists have developed multiple proposals attempting to account for the ontological status of quantum formalism and its possible tension with relativistic principles. This thesis contributes to these efforts by advancing a Neo-Aristotelian ontological framework inspired …

Instance-Adaptive Gated Fusion Of Multi-Transform Image Representations, Prince Appiah

Open Access Theses & Dissertations

This dissertation proposes the Instance-Adaptive Gated Fusion (IAGF) framework, a novel deep learning architecture for adaptive and interpretable fusion of multiple time–series image transformations. While existing methods rely on static concatenation or dataset-level optimization, IAGF introduces a learnable gating mechanism that dynamically assigns per-instance weights to Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Gramian Angular Difference Fields (GADF). The gating layer performs a convex fusion of transformation-specific embeddings under a softmax constraint, ensuring mathematical stability and interpretability. An entropy-regularized objective prevents dominance collapse and promotes balanced exploration of transformations during training. Comprehensive experiments across eighteen benchmark datasets, spanning …

Hydrological Responses To Rainfall Pulses In Desert Vadose Zones: A Multi-Scale Approach Integrating Geophysical, Isotopic, And Eddy-Covariance Measurements, Alfredo Dagda-Torres

Open Access Theses & Dissertations

Drylands are shaped by natural episodic precipitation, where short, intense rainfall pulses interrupt prolonged drought and drive most hydrological and ecological activity. Yet the mechanisms that determine how pulse water infiltrates, is redistributed, stored, evaporated, and ultimately used by desert vegetation remain under quantified in landscapes underlain by shallow petrocalcic horizons. This dissertation integrates three complementary approaches 1) three-dimensional electrical resistivity tomography, 2) eddy-covariance evapotranspiration partitioning, and 3) stable-isotope tracing to improve a mechanistic understanding of pulse-driven water movement and plant water use in a caliche-rich piedmont at the Jornada Experimental Range. The first component characterizes how infiltration pulses interact …

Intelligent Predictive Frameworks Under Data Scarcity And Uncertainty, Solayman Hossain Emon

Open Access Theses & Dissertations

Modern predictive systems frequently operate under conditions of limited annotated data, high uncertainty, and the need for reliable decision-making. When the predictive models expand across heterogeneous data types (e.g., spatial, temporal streams), the challenge lies not only in accurate prediction but also in adapting in data distributions shifts or label scarcity. To address these issues, this thesis explores an Intelligent Predictive Framework that operates robustly under data scarcity and uncertainty across two distinct domains: medical imaging (spatial) and time-series forecasting (temporal). In the first part of this work, a semi-supervised mean teacher (MT) paradigm is tailored for medical image segmentation …

Development Of Oxide And Oxynitride Thin Films For Advanced Optical Applications, Nathan Christopher Episcopo

Open Access Theses & Dissertations

To develop Ga2O3 and TiOₓNᵧ thin films for optical applications, the process–structure–properties relationship was investigated using a comprehensive set of characterization techniques to establish the atomic structure and composition of the films in relation to the deposition conditions. The results provide critical insights into how deposition parameters influence the atomic structure and composition during magnetron sputtering. Furthermore, the correlation between atomic structure and composition with the optical and electrical properties of the films was examined to determine how variations in these factors affect functional performance. Collectively, these findings offer the necessary information to target desirable optical properties for Ga2O3 and …

From Morphology To Machine Learning And Genomics: Understanding Phenotypic Variation In Wild Ducks, Sara Gonzalez

Open Access Theses & Dissertations

Understanding the genetic underpinning and distribution of phenotypic variation within and between divergent groups is core towards shedding light into how populations diverge and adapt, as well as how hybridization breaks or builds on these scenarios; and thus, central to evolutionary biology. In wild organisms, however, quantifying and linking phenotypic traits to underlying genetic processes, like mutation, gene expression, epigenetics and allele interactions, remains challenging. This difficulty arises from the complex interplay between morphology, environment, and gene regulation, as well as the logistical barriers of collecting and standardizing large-scale data across individuals and populations. As a result, researchers are increasingly …

Facilitating Deep Learning Performance Analysis Through Automated Roofline Model Generation, Irvin Lopez-Audetat

Open Access Theses & Dissertations

This thesis presents a tool to profile deep learning (DL) and machine learning (ML) models by collecting FLOPs, memory movement, and timing data through cyPAPI to generate roofline performance models. The tool is containerized for portability and reproducibility, integrates directly with PyTorch workflows, and provides fine grained insights into computational bottlenecks across model components. Unlike prior system-level or benchmarking-centric tools, this project empowers developers and researchers with an accessible, modular framework for performance analysis and optimization.

A Half Century Of Biophysical Change In Polygonized Tundra On The Coastal Plain Of Northern Alaska, Mariana Mora

Open Access Theses & Dissertations

Climate change is amplified in the Arctic. As a result, Arctic tundra landscapes are undergoing physical changes through accelerated thaw and ice-wedge degradation. Understanding the magnitude and direction of these changes in the Arctic is a necessary step toward understanding the response of the Arctic to climate change and feedbacks to the Climate System. Through repeat measurements collected from 1973 through 2018, we were able to assess decadal time scale changes in polygon geomorphology, thaw depth, land surface compression and expansion, and vertical elevation within a highly polygonized tundra system, and their implications when scaled to a regional level. This …

What Limits Dryland Ecosystems? Patterns Of Soil Fertility And Resource Limitation In The Chihuahuan Desert, Dylan Stover

Open Access Theses & Dissertations

Human activities are substantially altering global resource cycles with widespread implications for biogeochemistry and ecosystem functioning globally. Drylands, regions where precipitation is outweighed by water losses, are especially sensitive to these large shifts in resource cycles due to their inherently low and variable resource availability. In these regions, resource availability and biological activity are often concentrated around plants – or fertile islands – and predominantly driven by pulses of water. However, our knowledge of the processes influencing biological productivity in drylands – patterns of soil fertility and resource limitation – remains lacking, and their unique biogeochemical and biological processes create …

A Unified Framework For Embedding-Based Synthetic Data Generation With High Cardinality Categorical Features, Cesar Iram Vazquez

Open Access Theses & Dissertations

High-cardinality categorical variables remain difficult to model in tabular data, where classical encoders encounter sparsity, susceptibility to leakage, and the loss of meaningful relational structure. This dissertation develops a unified framework for learning, evaluating, and synthesizing representations of such variables using both traditional encoders and modern embedding methods, including Word2Vec, FastText, Node2Vec, TF–IDF/SVD, and supervised entity embeddings. The framework is applied across three benchmark datasets (Adult, PetFinder, Breast Cancer) and a hierarchical educational case study (IPEDS/CIP). Embedding quality is examined through both downstream predictive performance and structure-focused diagnostics that quantify neighborhood behavior and geometric coherence. To assess whether synthetic data …

Hubert-Based Models And Evaluation Strategies For Pragmatically-Faithful Speech To Speech Translation, Javier Vazquez

Open Access Theses & Dissertations

Pragmatic fidelity in speech-to-speech translation (S2ST) has largely been understudied, leading to communication tools inadequate to support non-superficial dialog. We aim to improve pragmatic faithfulness in English-Spanish translation through the development of machine learning models that are able to predict a corresponding pragmatic representation in the other language. To evaluate performance, we developed a pipeline that utilizes a recently-developed pragmatic similarity evaluation metric to compare models. Further, we developed models that exploit HuBERT features as these have been found suitable for various prosody and pragmatics related tasks. Our models outperformed human and state-of-the-art predictions, albeit the methodology being limited to …