Computer Sciences Commons^™

Guardians Of The Record (Cs2 Edition): Heaps, Queues, And A Scarce Oracle, Ilan Goodman

Generative AI Teaching Activities

Students defend Wikipedia from vandals with data structures instead of infrastructure: a sliding-window edit-velocity tracker (hash map of queues), a hand-built binary min-heap, and a budget-bounded top-K selection decide which few suspicious edits earn a question to an expensive, rate-limited Oracle — a stand-in for a real LLM.

Guardians Of The Record: A Two-Tiered Streaming Cascade With Kafka, Flink, And A Real Llm, Ilan Goodman

Generative AI Teaching Activities

Students build a streaming vandalism detector for live Wikipedia edits in which a fast rule-based tier decides which few of ~1,500 edits per minute are worth escalating to a slow, rate-limited real LLM (Gemini) — confronting the cost, latency, and failure modes of putting AI inside a production data pipeline.

Toward Vehicle-Agnostic Driving Signatures For Cognitive Impairment Prediction From Naturalistic Driving Data, Aadarsha Gopala Reddy

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis studies whether naturalistic driving data can help predict binary Clinical Dementia Rating (CDR) status while accounting for differences across vehicles. The final analytic dataset comprised 26,968 participant-weeks from 304 participants. Weekly driving features were derived from real-world telematics data and combined with four demographic covariates. Primary model comparisons used leave-one-participant-out (LOGO) cross-validation, with one individual held out at a time and pooled participant-level metrics used as the main reporting surface.

The main comparison includes six model families evaluated on the same dataset under a shared LOGO framework. Performance remained modest overall. GRU-DANN had the highest participant-level ROC AUC …

Largest 2-Regular Subgraphs In Complete S-Partite Graphs, Yiyang Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

In this thesis, we focus on the class of complete $S$-partite graphs, for $S$ an undirected graph possibly with self-loops, and address the problem of finding largest $2$-regular subgraphs of these graphs, which can be formulated as an integer linear program. Roughly speaking, a complete $S$-partite graph is obtained by replacing every single node of $S$ with a number of nodes, preserving the edge/non-edge relations of $S$. Our motivation in studying largest $2$-regular subgraphs is rooted in the structural systems theory, particularly in the problem of finding largest subnetworks that can sustain controllability or asymptotic stability of the corresponding subsystems. …

Improving Semantic Precision In Text-To-Image Diffusion Models Via Latent-Space Optimization And Semantically-Parsed Evaluation, Mohammad Rouie Miab

McKelvey School of Engineering Graduate Student Theses & Dissertations

Text-to-image diffusion models can produce visually impressive images from natural-language prompts, but they often fail to satisfy the detailed semantic constraints expressed in compositional prompts. Typical failure modes include omitted objects, merged entities, incorrect quantities, incorrect attribute binding, and leakage of one entity's attributes onto another. This thesis studies the problem of semantic precision in text-to-image generation: how faithfully a generated image satisfies the structured meaning of its prompt.   The thesis makes two linked contributions. First, it presents a training-free inference-time refinement method for diffusion-based image generation. The method operates directly in latent space during denoising and uses noun-phrase-aware cross-attention …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Revisiting Visualization Literacy: What Standardized Assessments Reveal And Conceal Across Instruments, Cultures, And Ai Systems, Saugat Pandey

McKelvey School of Engineering Graduate Student Theses & Dissertations

Data visualizations are now a primary medium for public communication, scientific reasoning, and decision-making. By transforming complex data into accessible graphical forms, visualizations are widely assumed to make information comprehensible to broad audiences. Yet the ability to accurately read, interpret, and critically evaluate a visualization, what researchers call visualization literacy, is neither uniform nor universal. It is a learned, multidimensional skill shaped by prior exposure, educational opportunity, and context. While the field has developed standardized instruments to measure visualization literacy, these tools were built under narrow assumptions: participants are typically paid, English-speaking, and recruited from Western online panels. When any …

Smart Kitchen: Towards Real-Time Ai Systems For Cognitive Support In Daily Activities, Ruiqi Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The rapid growth of the aging population and the rising prevalence of Subjective Cognitive Decline (SCD) highlight the need for continuous, unobtrusive assessment of functional cognition during everyday activities. Vision-based smart home systems offer a promising pathway for monitoring behavior and supporting independent living. However, enabling real-time cognitive assistance remains challenging. It requires not only accurately interpreting complex human behaviors to detect cognitive errors, but also supporting real-time deployment on resource-constrained edge devices and under dynamic wireless network conditions. This dissertation presents Smart Kitchen, an AI-driven system for real-time cognitive error detection through the monitoring of daily cooking activities. Under …

Building Human-Aware Ai: Learning From And Assisting Human Decision-Makers, Saumik Narayanan

McKelvey School of Engineering Graduate Student Theses & Dissertations

As artificial intelligence systems become more capable, they are increasingly used not only as standalone problem-solvers, but as systems that learn from and interact with humans. In these settings, success depends not only on the strength of the model in isolation, but also on how well it fits the humans it is trained on or deployed alongside. This dissertation argues that human heterogeneity is a central ingredient in the design of effective human-aware machine learning systems. Rather than treating differences between people as noise to be averaged away, I show that variation in human expertise and preferences can provide useful …

Benefits Of Traffic Reprofiling For Delay Sensitive Networking, Jiaming Qiu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Deterministic networking systems, such as Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet), require strict end-to-end delay guarantees while efficiently utilizing limited network resources. Conventional approaches typically focus on fixed traffic profiles, which can lead to suboptimal resource utilization in scheduling or admission control problems. This dissertation investigates traffic reprofiling—the proactive reshaping of traffic arrival patterns—as a complementary mechanism for improving both resource efficiency and delay performance under strict service guarantees. The dissertation consists of three parts. The first part studies bandwidth minimization under hard delay constraints for Service Curve Earliest Deadline First (SCED) schedulers. We show that traffic reprofiling can …

Geometric Modeling Through Multiple Implicit Functions, Yiwen Ju

McKelvey School of Engineering Graduate Student Theses & Dissertations

Implicit representations have become a dominant paradigm in computational settings ranging from learning-based geometry generation to advanced manufacturing. While treating geometry as the level set of a black-box function provides significant modeling flexibility, converting these representations into explicit surface meshes remains a major challenge. Standard volumetric extraction methods are fundamentally designed for smooth manifolds and therefore struggle to capture sharp geometric features such as creases, corners, and non-manifold junctions that are critical for high-fidelity industrial design and engineering tasks. Many of these intricate features arise from modeling multiple implicit functions. Examples include Constructive Solid Geometry (CSG), material interfaces, and more …

Tests Without Borders: A Global Approach To Measuring Visualization Literacy, Olivia A. Guess

McKelvey School of Engineering Graduate Student Theses & Dissertations

Visualization literacy assessments shape how we understand people's ability to interpret data, yet most existing instruments embed Western datasets and assumptions that limit their relevance for global audiences. This thesis argues that because data is personal, assessments must also be culturally grounded. We introduce a unified framework for adapting the Mini-VLAT into 22 regionally responsive short-form assessments, each retaining the structure of the original test while incorporating datasets and scenarios tailored to specific regions around the world. To demonstrate how such adaptations can be customized and validated, we present a detailed case study of a Ghana-adapted Mini-VLAT, developed in collaboration …

Towards Fair Sequential Resource Allocation: Algorithmic Designs, Interventions, And Evaluations, Ashwin Kumar

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis develops a comprehensive framework for fair sequential resource allocation in multi-agent systems where a centralized allocator coordinates actions under global feasibility constraints, while satisfying preferences of different agents. Ranging from ridesharing platforms and homelessness intervention programs to power grid management, such systems play a critical role in shaping access to essential resources. Yet, existing approaches to resource allocation often prioritize aggregate utility, leading to systematic inequities across individuals and groups, particularly in sequential settings where decisions unfold over time. To address this challenge, we introduce the Distributed Evaluation, Centralized Allocation (DECA) framework, which unifies a broad class of …

Multimodal Representation Learning For Geospatial Soundscape Mapping, Subash Khanal

McKelvey School of Engineering Graduate Student Theses & Dissertations

Sound is one of the fundamental senses that helps us reason about our environment. There exists an intricate relationship between the visual appearance of a location and the distribution of sounds present there. We propose leveraging this relationship to formulate the task of soundscape mapping—predicting the most probable distribution of sounds that could be perceived at a given geographic location, as observed in its overhead imagery. To support research on this task, we curated a comprehensive dataset, GeoSound, which consists of geotagged audio recordings from various sources, paired with both low- and high-resolution overhead imagery. We approach the soundscape mapping …

Computational Imaging Under Incomplete Information, Weijie Gan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Computational imaging is a pivotal field that synergizes physical measurement principles with advanced algorithms to generate visual information. An important task in this field is solving imaging inverse problems that aim to reconstruct high-quality images from observed measurements. Model-based deep learning (MBDL) has emerged as a particularly powerful tool for tackling these inverse problems by integrating machine learning (ML)-driven priors with knowledge of the imaging physics. This dissertation focuses on the pervasive challenge of informational incompleteness in computational imaging, arising from various practical and physical limitations, that hinder the widespread adoption of ML-driven computational imaging algorithms in practice. This includes: …

Code Stories For Software Evolution, John Joseph Allen

McKelvey School of Engineering Graduate Student Theses & Dissertations

Programmers spend more than half of their time comprehending code, and in particular struggle to answer questions about the rationale, intent, and history behind software artifacts. Through this dissertation, I explore how history-aware tools can help programmers understand unfamiliar software artifacts. First, I investigated how providing additional context -- historical code changes grouped by the original developer's stated subgoals and the web foraging activity of the original developer impacted the process of code reuse. I found that programmers utilized these resources to 1) make better analogies between their reuse scenario and what code was already written, and 2) anchor into …

Combining Code Analysis And Pedagogical Guidance: Automated Tools For Teaching Debugging In Introductory Programming, Yana Malysheva

McKelvey School of Engineering Graduate Student Theses & Dissertations

The ability to debug code is critical to being a programmer and represents a distinct skill from writing code. Yet debugging is rarely explicitly taught in introductory programming and Computer Science courses. Instead, novices typically develop their own debugging habits and strategies when they encounter bugs in their code, which are often less effective than those of expert programmers. When students do seek help with debugging, they traditionally turn to office hours conducted by Teaching Assistants (TAs) or, increasingly, to Large Language Models such as ChatGPT. However, both sources of assistance have limitations. TAs are often students themselves with limited …

Towards Graph Foundation Models: Few-Shot And Zero-Shot Learning On Graphs, Hao Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Graphs naturally model complex relationships and interactions across various domains, including social networks, biological systems, and recommender platforms. Graph Neural Networks (GNNs) have emerged as powerful tools for learning effective graph representations through iterative message passing, significantly improving performance in tasks such as node classification, link prediction, and graph classification. However, the success of GNNs largely depends on abundant labeled data, posing challenges in practical scenarios where labeled data is scarce or unavailable. This dissertation addresses these challenges by exploring few-shot and zero-shot learning within the graph domain. We first propose COLA, a self-supervised few-shot node classification method that exploits …

Learning From Conditional Data Distributions, Jizhou Huang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Traditional machine learning paradigms often rely on a single global model trained on an entire dataset, aiming for broad generalization across all instances. However, in many real-world applications, the underlying data distribution is heterogeneous, and meaningful predictions often require models that focus on specific subpopulations rather than treating the data as a whole. This motivates the study of learning from conditional distributions, a framework where predictive models are designed to capture the structure and properties of restricted subsets of the data, leading to improved accuracy, fairness, and interpretability. This dissertation explores three key subproblems that exemplify different aspects of learning …

Advancing Political Science With Machine Learning: A Gaussian Process Approach, Yehu Chen

McKelvey School of Engineering Graduate Student Theses & Dissertations

The proliferation of data in recent decades including including survey, image and text data, has significantly transformed the landscape of political science research. Machine learning methods have played an instrumental role in analyzing these datasets, yet their application poses challenges in areas where political concepts are not directly measurable or the primary focus of inference is causality. In addition, the essence of machine learning algorithms being trained for prediction performance in a black-boxed manner, makes their outputs hardly interpretable and even unappreciated. This dissertation addresses these issues by proposing a novel methodological framework that employs Gaussian Process (GP) models, a …

Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri

McKelvey School of Engineering Graduate Student Theses & Dissertations

Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be …

Cyber-Physical Security Through The Lens Of Ai-Enabled Systems, Zhiyuan Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A unique aspect of these systems lies in their interactions with the physical world, by perceiving environments through heterogeneous modalities (perception), processing digital data with human-in-the-loop intelligence algorithms (computing), and autonomously actuating controls that affect physical processes (actuation). While this intricate fusion of cyber and physical components has unlocked unprecedented capabilities, it has also introduced new security challenges. However, traditional security measures often fall short in addressing these multifaceted threats. This …

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 …

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 …

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 …

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. …

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 …

Noninvasive Assessment Of The Tumor Using Cfdna, Irfan Alahi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Next-generation high-throughput sequencing, which is increasingly generating vast amounts of genomic data, offers opportunities for a deeper understanding of the multifaceted nature of cancer and, hence, better patient care. However, the inherently complex and heterogeneous nature of cancer and the significant challenges in the generated data demand advanced data-driven frameworks to decode the molecular underpinnings of cancer. Moreover, the undeniable need for non-invasive approaches presents additional technical challenges in this domain. This dissertation proposes novel frameworks addressing three key challenges in computational oncology. The first study of this dissertation develops a data-driven algorithm to identify stemness signatures in metastatic castration-resistant …