Digital Commons Network^™

Effective And Efficient Graph Foundation Model, Lecheng Kong

McKelvey School of Engineering Graduate Student Theses & Dissertations

Graph data has emerged as a central component in numerous real-world applications, spanning recommender systems, drug discovery, social networking, and traffic forecasting. While traditional and modern graph learning techniques—ranging from graph kernels to Graph Neural Networks (GNNs) and graph transformers—have achieved significant success, their task-specific nature and reliance on supervised learning limit their adaptability to new, unseen tasks. This rigidity becomes especially problematic in dynamic environments where retraining for every new task is costly and often infeasible. Inspired by the transformative impact of foundation models in natural language processing, this thesis explores the feasibility of developing a graph foundation model—a …

Scaling Quantum Systems: Quantum Networks, Distributed Quantum Computing, And Security, Zebo Yang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Recent advancements in quantum computing have opened up new possibilities across various fields, offering significant potential to enhance computation, communication, cryptography, and applications in areas like sensing, medicine, and chemistry. However, the capabilities of individual quantum devices are still limited, and scaling quantum hardware monolithically presents substantial challenges. Interconnecting quantum systems offers a promising alternative by aggregating capabilities across a network, thereby enabling quantum advantages for larger and more practical problems. This interconnection is achieved through quantum networking, which links external quantum systems, and Distributed Quantum Computing (DQC), which connects quantum processors within a system, such as in quantum data …

System Security Foundations For Ai-Enabled Systems, Yuhao Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

AI is being deployed broadly, from conventional computing systems like IoT systems to more advanced agentic systems. It is shifting from being a specialized component responsible for specific functions to becoming the core of agentic systems, where it drives autonomous decision-making and task execution. These AI-enabled systems bring tremendous benefits. For example, large language models can interpret user intent, select appropriate tools, and access data to complete tasks with minimal human guidance. However, they also introduce new security, privacy, and safety risks. These risks arise not only from the models themselves but also from the broader system design and integration. …

Towards Secure And Privacy-Preserving Machine Learning Systems, Han Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

In recent years, machine learning (ML) has advanced at an unprecedented pace, driving the widespread adoption of increasingly sophisticated models across a broad range of real-world applications, including healthcare, finance, autonomous systems, and critical infrastructure. While these models have delivered remarkable benefits and transformed numerous industries, they remain inherently vulnerable to a variety of security and privacy threats. Given their growing role in safety-critical domains, ensuring their security and privacy has become imperative. Systematically addressing these vulnerabilities requires comprehensive adversarial analyses to uncover weaknesses and inform the design of robust defenses. This dissertation systematically investigates ML vulnerabilities in adversarial settings …

Trustworthy Autonomy Through Robust Control And Alignment, Junlin Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

As artificial intelligence systems are increasingly applied in safety critical domains such as robotics, autonomous driving, and decision making under uncertainty, ensuring their trustworthiness has become a central challenge. This dissertation addresses two major facets of trustworthy AI: reliable control through formal guarantees and alignment against adversarial manipulation. The first part of the dissertation focuses on provably stable, robust, and safe control for nonlinear systems using learning based methods. We introduce the first general framework for synthesizing neural Lyapunov controllers in discrete time systems. This method combines a sound verifier based on mixed integer linear programming with gradient based counterexample …

Understanding And Mitigating Timing Issues In Autonomous Systems, Ao Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Autonomous systems, such as self-driving cars and drones, have become a part of our daily lives. Since these systems operate in and interact with the physical world, their correctness depends on both functional and temporal aspects. However, the increasing complexity of modern computing hardware and software often leads to unpredictable timing behavior in these systems, making temporal properties particularly challenging to ensure. This dissertation proposes novel approaches to specify and enforce temporal properties based on a comprehensive empirical study of real-world issues. The first half of this dissertation presents an empirical study that dissects the timing issues. The dissection begins …

High-Quality Factor Phase Gradient Metasurfaces With Dynamic Reconfigurability, Lin Lin

Arts & Sciences Graduate Student Theses and Dissertations

Free-space optical communication holds immense potential for surpassing the bandwidth limitations of conventional physically defined channels. Achieving flexible and efficient communication in free space requires compact, robust optical components capable of dynamically tuning laser light without relying on bulky lenses or mechanically driven mirrors. Metasurfaces, or flat optics, have garnered significant attention due to their densely arranged subwavelength nano-resonators, which impart phase, amplitude, and polarization changes comparable to those of traditional bulky optics. However, conventional phase gradient metasurfaces are constrained by passive designs based on geometric configurations and the weak light-matter interaction inherent in most dielectric materials, limiting their applicability …

Simplicity From Complexity: Sensing High-Dimensional Fluctuations Via Regulatory Network, Optimality In Allocating Cellular Resources, And Recovering Functional Groups Via Data-Driven Methods, Fang Yu

Arts & Sciences Graduate Student Theses and Dissertations

Biological systems must navigate dynamic and complex environments while operating under constraints such as limited molecular resources, biochemical noise, and structural trade-offs. This dissertation investigates three distinct cases in which biological systems allocate resources and process information efficiently, revealing emergent simplicity from underlying complexity. The first study develops a generalized end-product inhibition model with cross-talk and an excess of regulators, providing a proof-of-principle that simple biochemical circuits could, in principle, dynamically adjust their responsiveness to high-dimensional environmental variation. This result highlights how such circuits may allow adaptive filtering of both dominant and subdominant fluctuation modes. The second study, conducted in …

Investigations Of The Electrochemical And Photochemical Properties Of Earth-Abundant Metal Complexes Stabilized By A Pentadentate Bipyridyl Macrocyclic Ligand, Skylar Nichols

Honors Theses

The creation of a selective and efficient catalyst for the multi-electron reduction of carbon dioxide (CO₂) into valuable chemicals or renewable fuels is a crucial research area. As the use of non-renewable fossil fuels and the associated greenhouse gas emissions, predominantly CO₂, continue to rise, the need for renewable and sustainable approaches to energy increases. To address both the need for clean energy and to reduce the accumulation of CO₂ in the atmosphere, this work will investigate the synthesis and catalytic activity of Earth-abundant transition metal-based complexes supported by a pentadentate ligand. The macrocyclic ligand 7-methyl-4,7,10-triaza-1,2(2,6)-dipyridinacycloundecaphane was successfully synthesized and …

Challenges Us Study Abroad Students Face When Acculturating To A Foreign Food Environment, Sydney C. Davis

Honors Theses

Studying abroad presents a unique opportunity for college students to immerse themselves in new cultures, including experiencing different food environments and dietary customs. However, adapting to a foreign food culture can pose various challenges that impact students’ overall health, eating behaviors, and study abroad experiences. The purpose of this study to develop a survey instrument designed to investigate the extent to which U.S. study abroad students embrace, accept, and face challenges with different aspects of Italian food culture, using the Social Ecological Model (SEM) as a guiding framework. Initial survey items were developed with information obtained from focus group discussions …

Magma Evolution And Eruptive History Of Three Fingered Jack: A Mafic Composite Volcano In The Central High Cascades Of Oregon, Javaria Aziz

Dissertations and Theses

The central High Cascades graben in Oregon has been a center for mafic volcanism since its initiation. Three Fingered Jack is one of the larger mafic composite cones in the graben, located 20km south of Mount Jefferson. Three Fingered Jack erupted between 142.1 - 150.8ka, based on Ar⁴⁰/Ar³⁹ age dates. This study provides stratigraphic, compositional and petrographic evidence to understand the eruptive history of Three Fingered Jack and the processes involved in generating compositionally diverse basaltic andesites here.

The basaltic andesites of Three Fingered Jack show three compositional trends of magma evolution: 1) enrichment, 2) depletion and …

Automation Of Javanese Shadow Puppets Using Machine Control, Kristian Rice, Yinson Tso, Mukhammadali Yuldoshev

Publications and Research

The virtualization of Javanese shadow puppetry (Wayang Kulit) offers a unique opportunity to preserve and revitalize traditional performance art through immersive digital platforms. This project explores the development of a virtual Wayang Kulit experience using real-time 3D engines like Unity/Unreal Engine while focusing on simulating the mechanics and aesthetics of shadow puppet performance. The puppets are designed using detailed 2D planes and rigged with skeletal systems to reflect the stylized motion of traditional puppetry. An aspect of this project is integrating an AI-driven control system that autonomously animates the puppets, learning from recorded puppeteer performances to replicate gesture, rhythm, and …

Topological And Optical Properties Of Two-Dimensional Materials, Haonan Wang

Arts & Sciences Graduate Student Theses and Dissertations

Recent advances in condensed matter physics have revealed a wide range of novel quantum phenomena in two-dimensional (2D) materials. The discovery of unique topological, magnetic, and optical properties in solids has greatly expanded our understanding of quantum phases in electronic structures. As more material systems are proposed theoretically and realized experimentally, our research focuses on uncovering and explaining these phenomena through effective models and first-principles calculations. First, we explore topological mosaic patterns in moiré superlattices—structures composed of periodic, spatially distinct domains, each with different topological properties. These domains give rise to periodic edge states along domain walls. We explore the …

X-Ray Polarization Of Accreting Stellar-Mass Black Holes Through Accretion States, Nicole Rodriguez Cavero

Arts & Sciences Graduate Student Theses and Dissertations

In recent years, X-ray polarization has opened up a new window into our understanding of astrophysical sources. Among them are accreting stellar-mass black holes, which play a key role in testing the behavior of matter in extreme gravitational environments. These sources can be observed in different states of the accretion cycle, predominantly in the soft and hard states. Every state is dominated by a combination of thermal disk, coronal, or reflected emission—each type containing different information about the environment around the black hole that can be understood through polarization. Polarimeters such as the Imaging X-ray Polarimetry Explorer and XL-Calibur allow …

Bayesian Statistics: Origins And Applications, Evelyn Pulla

Publications and Research

Bayesian Statistics applies Bayes' Theorem to update beliefs through new evidence. In this project, I explored how Bayesian Statistics applies into real supporting decision-making under uncertainty. By solving problems using data, I was able to realize how prior knowledge and evidence collaborate to make better conclusions. The project also demonstrates how Bayesian reasoning corrects our intuition to make decisions based on logical reasoning. Through this project, I was able to learn why using probability to make informed decisions matters both in science and real life.

Performance Of Lasso And Ridge Regression For Variable Selection In Genome-Wide Association Studies Of Maize Flowering Time, Dipok Deb

Data Science and Data Mining

Genome-Wide Association Studies (GWAS) are instrumental in identifying genetic variants linked to complex traits, providing valuable insights into trait heritability and biological mechanisms. This study applies GWAS to investigate flowering time in maize, a critical adaptive trait, using a diverse dataset of 5,000 recombinant inbred lines across eight environments. Traditional GWAS methods often encounter challenges in high-dimensional datasets due to the presence of multiple small-effect genetic loci. To address this, we compared two penalized regression methods—LASSO and Ridge regression—to perform variable selection and regression analysis within a GWAS framework. LASSO effectively reduced the number of predictors by selecting the most …

Data Science Job Salary Prediction Using Linear Regression, Dipok Deb

Data Science and Data Mining

In the evolving landscape of data science, accurate salary prediction plays a crucial role in shaping career expectations, informing educational strategies, and guiding organizational hiring decisions. This study investigates the key factors influencing entry-level data science salaries in the United States by applying a multiple linear regression model to a recent dataset spanning from 2020 to 2024. Through data preprocessing, transformation, and diagnostic evaluation, we identify how job roles, experience levels, employment types, work arrangements, residency status, and company size impact compensation. Despite challenges such as outliers, heteroscedasticity, and non-normal residuals, model refinements like the Box-Cox transformation and variable selection …

Experimental Evaluation Of Resource Allocation To Organelles In Eukaryotic Cells, Ronan Hanley

Senior Honors Papers / Undergraduate Theses

Among the hallmarks of the eukaryotic cell is its organization into spatially defined sub-compartments known as organelles. Organelles provide optimized environments for otherwise incompatible biochemical reactions within the cell. It has been shown that there is significant variation in the properties of organelles in a cell, and that cells have access to a limited pool of resources that they can allocate to organelles. This begs the question: by what principles do cells dictate how resources are allocated? To begin to explore this question, we look at resource allocation in S. cerevisiaefrom two perspectives. First, we use a combination of …

Transmutation, Sam Berger

MFA in Visual Art

My artistic practice is about change itself. I am inspired by the doubling and layering effects that came out of Surrealist Photography. Scale, fracture, and doubling are intentional components of my process. I combine those methods to create complex works of art, focusing on the untamable light that I hope to capture when making my images, which often are self portraits. Poetry is an integral element of this thesis text as well as my art making process. Through my poetic work I hope to offer my audience a glimpse into how I see and experience the world. This thesis text …

Symmetry Induced Pairing In Dark Excitonic Condensate At Finite Temperature, Adham Alkady, Anatoly Kuklov

Publications and Research

Bose Einstein condensate of dark intervalley excitons must be inherently multi-component because of crystalline symmetries. Since valleys hosting such excitons are separated by large quasi-momenta, a minimal inter-component Josephson-type coupling can only be established between pairs of excitons from the time-reversed valleys. As a result, a paired condensate can emerge at finite temperature, that is, the off-diagonal order exists for the pairs from the time-reversed valleys, while the individual valleys are disordered. This prediction follows from the elementary mean field analysis regardless of the dimensionality. However, as Monte Carlo simulations show, no such a phase exists in 3D crystals. Instead, …

Improving The Reproducibility Of Deep Learning Software: An Initial Investigation Through A Case Study Analysis, Nikita Ravi, Abhinav Goel, James C. Davis, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns about reproducing the results of these deep learning methods. This is significant because reproducibility is the foundation of reliability and validity in software development, particularly in the rapidly evolving domain of deep learning. The difficulty of reproducibility may arise due to several reasons, including having differences from the original execution environment, incompatible software libraries, proprietary data and source code, lack of transparency, and the stochastic nature in some software. A study conducted by …

Real-Time System Availability For Cyber-Physical Systems, Jinwen Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPSs), such as autonomous vehicles, are increasingly being deployed. The sensing, control, and actuation loop in CPSs must complete within strict timing constraints. Missing a real-time deadline can lead to catastrophic consequences, as CPSs continuously interact with the physical world. This highlights the importance of real-time system availability (i.e., timely execution) in CPS tasks, going beyond traditional security goals that primarily focus on confidentiality and integrity. From a security perspective, two factors affect real-time system availability. First, attackers with access to hardware resources in CPSs may disrupt the execution timing of real-time tasks. Second, the deployment of security …

Weak Formulation For Solving Inverse Problems In Reproducing Kernel Hilbert Spaces (With Applications To Learning Dynamical Systems), Victor William Rielly

Dissertations and Theses

We combine numerical and machine learning techniques to present a general framework for solving inverse problems using vector valued reproducing kernel Hilbert spaces in a variational formulation. We present this framework in two papers. In the first paper, we present an original state-of-the-art method derived in the context of our general framework for learning dynamical systems. In the second paper, we generalize the method from our first paper to arrive at the framework for solving inverse problems. Then we apply our general framework to the task of learning dynamical systems. In both papers we consider numerous applications of our methods …

Handwritten Digit Recognition Using Machine Learning, Dipok Deb

Data Science and Data Mining

Handwritten Digit Recognition (HDR) remains a fundamental benchmark in pattern recognition and machine learning due to its practical applications and inherent classification challenges posed by diverse handwriting styles. This study investigates and compares two classical statistical classifiers—Gaussian Naive Bayes (GNB) and Linear Discriminant Analysis (LDA)—to recognize the digits from the MNIST dataset. Both models assume underlying normality in feature distributions and offer computational efficiency, making them suitable for high-dimensional input such as image pixels. Using 60,000 training and 10,000 test samples, we evaluate model performance through accuracy, precision, recall, F1 score, and confusion matrices. The results reveal that while GNB …

Clustering Dataset Using K-Mean Clustering, Dipok Deb

Data Science and Data Mining

Clustering is a fundamental technique in unsupervised machine learning, widely applied in various domains such as pattern recognition, data segmentation, and anomaly detection. This study evaluates the performance of the K-Means clustering algorithm on multiple benchmark datasets, including low-dimensional, high-dimensional, and imbalanced datasets. The clustering results are assessed using four key evaluation metrics: Mean Squared Error (MSE), Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Silhouette Score. Experimental results demonstrate that K-Means performs effectively on datasets with well-separated clusters, particularly in high-dimensional spaces, where it achieves near-perfect clustering accuracy. However, its performance deteriorates in datasets with overlapping clusters and …

On Metric Choice In Dimension Reduction For Fréchet Regression, Abdul Nasah Soale, Congli Ma, Siyu Chen

Faculty Scholarship

Fréchet regression is becoming a mainstay in modern data analysis for analysing non-traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data. Fréchet regression utilises the pairwise distances between the random objects, which makes the choice of metric crucial in the estimation. In this paper, existing dimension reduction methods for Fréchet regression are reviewed, and the effect of metric choice on the estimation of the dimension reduction subspace is explored for the regression between random responses and Euclidean predictors. An extensive …

Improving User Retention And Learning Through Interactive Tutorial Systems, Prakhyat Chaube

2025 Spring Honors Capstone Projects - Archive

The onboarding experience in software applications is crucial for user engagement and retention. Traditional static tutorials often fail to provide adaptive, role-specific learning, leading to user frustration and drop-off. This project introduces an interactive tutorial system tailored for students and tutors using the CSE Student Success Center App at the University of Texas at Arlington. Designed to enhance usability and accessibility, the system personalizes onboarding experiences through guided, role-based learning paths and real-time feedback. By streamlining the learning curve, the tutorial system fosters greater user confidence and engagement, ensuring a more intuitive transition into the application. User evaluations indicate a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

First-Principles Studies Of Excited-State Properties In Large-Scale Systems, Du Li

Arts & Sciences Graduate Student Theses and Dissertations

Density Functional Theory (DFT) is widely used as a powerful tool for studying the electronic structure of materials. However, due to the local or semilocal nature of exchange-correlation functionals, such as the local density approximation and generalized gradient approximation functional, DFT often underestimates the electronic band gap. Many-body perturbation theory (MBPT) within the GW approximation provides more accurate quasiparticle energies calculation by incorporating many-electron screening effects. Additionally, solving the Bethe-Salpeter Equation (BSE) allows for a detailed analysis of excitonic effects in optical spectra. Despite their accuracy, GW-BSE calculations are computationally expensive, particularly for large-scale systems such as defects and substrate-supported …

Modeling Literary Connections: Exploring Transregional Resistance In Dalit Poetry, Antara Bhattacharyay

Mathematics, Statistics, and Computer Science Honors Projects

Structuring socio-political identities, the caste system (a graded form of hierarchy) remains entrenched in contemporary Indian society. Dalits, marginalized by the caste system, have expressed their resistance through literature, envisioning substantive equality and social change. In this thesis, I draw on digital humanities methods to examine regional variation in translated Dalit poetry from Bengali, Hindi/Urdu, Marathi, and Tamil languages. I utilize topic modeling—a machine learning algorithm that detects latent semantic structures in a text—as a point of departure for poetry analysis. I observe how topic modeling enables newer readings of the poems, revealing regionally resonant and broader Dalit themes.