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We propose frameworks for dimension reduction in high-dimensional Vector Autoregressive (VAR) models using Spatial Quantile Regression (SQR). By incorporating adaptive Lasso and SCAD regularization, our methods enable robust inference under heavy-tailed or non-Gaussian errors while performing automatic
variable selection. To further address over-parameterization, we develop a tensor-based approach—Multilinear Low-Rank Spatial Quantile Regression (MLRSQR)—which restructures VAR transition matrices into low-rank tensors for simultaneous parameter reduction and quantile-wise modeling. Additionally, we
introduce the Sparse Higher-Order Reduced-Rank SQR (SHORRSQR) estimator, integrating Lasso penalties for sparsity, and design efficient ADMM-based algorithms.
Ultraviolet (UV) radiation in the 200–300 nm range is highly effective for microbial deactivation and has become a promising disinfection technology. There are several sources of UV radiation, including mercury vapor lamps, xenon arc lamps, deuterium lamps, and light emitting diodes (LEDs). Because of the associated damage to the skin, UV-LEDs are considered the safest and can provide a sustainable, energy-efficient, and environmentally friendly solution. However, there are some limitations in energy management, efficiency, automation, and optimization of UV-LEDs due to their wavelengths. Therefore, to optimize the efficiency in view of the application to microbial disinfection four UV-LED wavelengths (255 nm, 265 nm, 275 nm, 285 nm) were investigated. Thus, the dissertation work focused on the design, prototyping, and testing of an automated solar-powered UV-LED-based disinfection system, which serves as a sustainable and efficient solution for microbial deactivation in water, air, and surface applications.
Using the double-agar layer technique, the testing of these UV-LEDs for their ability to disinfect microbes/bacteriophages (MS2 and Phi6) was performed. Experimental results indicate that shorter wavelengths (255 nm) achieved the highest microbial inactivation with lower energy input, while longer wavelengths (285 nm) required significantly higher doses. The 255 nm UV-LED system, at a UV dose of 94.3 mJ/cm², achieved a 2.03 log reduction (99.08% reduction) for MS2, and at 117.9 mJ/cm², it achieved a 2.14 log reduction (99.28% reduction) for Phi6. In contrast, the 285 nm UV-LED system required 174.6 mJ/cm² for a 1.65 log reduction (97.78% for MS2) and 192.06 mJ/cm² for a 1.75 log reduction (98.25% for Phi6). The regression analysis and two-way ANOVA showed that the UV dose and wavelength are statistically significant in deactivating the microbial organisms. Finally, an automated solar-powered UV-LED-based disinfection system was designed, built, and tested. Similar results were obtained with the same statistical significance.
This research established solar-powered UV-LED disinfection systems as a scalable, sustainable, and highly effective solution for microbial decontamination. These findings will contribute to advancements in UV-LED technology, renewable energy integration, and automated disinfection, with diverse applications in healthcare, surface, water treatment, and public sanitation.
Exterior wood wall studs that serve as an integrated wall system in conventional house framing perform as very inefficient components when referring to the resistance of the conductive flow of heat (R-value). Affordable and available technology for wooden wall studs has not improved in decades, consistently resulting in lower R-values of the wall at the location of the studs as compared to the cavity between them. Typical wall stud sizes range from 2”x4” (1-1/2”x3-1/2”) to 2”x8” (1-1/2”x7-1/4) and net R-values of 4.38 and 9.07 respectively, while the insulation cavities would net R-13 and R-22 using standard fiberglass batts.
Construction techniques have evolved for outer surface areas of exterior walls by using various materials adhered to the outside of the wall surface. Modulating the actual stud on the interior, center, or outside with an insulative material could increase the R-value of the actual stud to a resistance of conductive flow of heat that is more in line with the insulated cavity. Aligning the R-values of the modified wooden stud and the insulated cavity could hypothetically increase the efficiency of all other exterior applied insulative materials, creating a higher thermal efficiency than is currently realized.
While teachers typically receive thorough training in their specific curricular areas through state licensure programs, they often do not receive adequate instruction on implementing evidence-based practices and interventions in general classroom settings (Mahoney, 2020). Consequently, there is an increasing concern that high school educators, in particular, may lack the knowledge and support necessary to effectively utilize evidence-based practices and interventions within a multi-tiered framework such as the Multi-Tiered System of Supports (MTSS). Educators who are not adequately trained or do not have a clear understanding of the implementation process and insufficient support from their school or district may struggle to maintain fidelity in MTSS implementation.
Implementing MTSS in high schools can be challenging due to many systemic factors. Leaders sometimes lack clear guidance due to the autonomy and flexibility permitted by different state education departments. This situation requires district and school administrators to navigate the complexities of selecting cost-effective resources, timely data-based decision-making, stakeholder collaboration, and the availability of qualified personnel to implement MTSS effectively (Clark & Dockweiler, 2019). This study utilized a multiple-case study methodology to explore the experiences of three Algebra I teachers and two English II teachers who implemented MTSS in their high schools across three districts in South Carolina. The findings underscore the importance of developing a strategic leadership plan that enables stakeholders to collaborate effectively and comprehensively understand the MTSS framework. The study findings emphasize a need to enhance teacher capacity throughout the MTSS implementation process by providing targeted professional development centered on innovative strategies and essential tools. Results offer valuable insights into the challenges high school English and Math teachers encountered during the implementation of MTSS at their schools and provide recommendations for school and district leaders aimed at creating clear and well-defined MTSS implementation plans.
Serendipity is a concept associated with accidental and unexpected discoveries that are valuable. In the recent decade, many researchers have advocated serendipity, as part of the “beyond accuracy” metrics, to encourage a recommender system to be an exploratory discovery tool instead of a narrowly focused machine. However, due to serendipity’s elusive and subjective nature, it is challenging to model. Collecting large-scale ground truth data is also a challenge. In this dissertation, I addressed both the challenges of serendipity model construction and the ground truth collection for recommender systems.
Leveraging the recent breakthrough in generative AI and large language models, I utilized three types of generative AI models: Large Language Models (LLMs), Transformers-based cross-domain models, and Diffusion Models (DMs), to construct a serendipity recommendation model. In addition, I used Large Language Models to collect serendipity ground truth data from large-scale e-commerce reviews data. The extensive experiments demonstrated the effectiveness of generative AI in modeling serendipity and ground truth collection. This dissertation advances the understanding and implementation of serendipity in recommendation algorithms, which will empower ordinary people with opportunities of bumping into unexpected but valuable discoveries.
Bulk RNA sequencing (RNA-seq) provides a cost-effective overview of gene expression but lacks resolution to identify cell-type-specific contributions in heterogeneous tissues. Computational deconvolution methods address this by estimating cell-type proportions from bulk data, enabling finer biological insights. This dissertation develops and applies statistical frameworks to improve the accuracy and interpretability of deconvolution results.
We begin by reviewing RNA-seq technologies and the impact of cellular heterogeneity. Deconvolution is then framed as a nonnegative matrix factorization (NMF) problem, with attention to challenges like non-uniqueness and noise sensitivity. Building on recent identifiability theory, we propose a geometric structure-guided NMF (GSNMF) that incorporates biological priors—such as marker genes—and local manifold structure to stabilize estimation.
To further enhance reference-free deconvolution, we introduce pseudo-bulk augmentation: a strategy that synthesizes single-cell-derived mixtures to enrich bulk data. This approach mitigates issues related to underdetermined solutions and improves robustness.
A comprehensive benchmarking study compares reference-based and reference-free methods using metrics like correlation, root mean squared error, and mean absolute deviation. Results show that while high-quality reference data can improve performance, augmented reference-free approaches like GSNMF are highly effective when reference data are scarce. We conclude with future directions and ongoing challenges.
This dissertation takes a liberatory socio-technical approach to explore how technology can empower Black undergraduate women in computing (BWIC) by fostering a sense of belonging, strengthening computing identity, and enhancing self-efficacy. Given the underrepresentation of Black women in STEM, this research challenges traditional approaches by centering their lived experiences and perspectives in the design of inclusive and supportive spaces. Unlike many existing interventions that are designed without direct input from those they aim to support, this study prioritizes active participation, agency, and decision-making among BWIC, ensuring that the solutions created truly reflect their needs. Led by a Black woman in computing, this research builds on personal and collective experiences to develop an authentic and affirming virtual community. Using a multi-modal (Discord) community as a technology-driven intervention, this study examines how virtual spaces can provide a sense of belonging, community, community support, computing identity, peer support, and networking opportunities that help BWIC navigate academic challenges in a predominantly white and male-dominated field. By leveraging qualitative and quantitative methods, this research investigates the ways in which participation in a virtual community influences engagement, confidence, and long-term persistence in computing. Findings reveal that the virtual community plays a critical role in mitigating feelings of belonging, isolation, providing access to a community, peer support, and fostering meaningful connections. While some participants engaged actively in discussions, others participated through passive engagement ("lurking"), yet both forms of involvement contributed to a greater sense of inclusion and identity in computing. Barriers to participation, such as academic workload, social anxiety, outside responsibilities and lack of awareness of available resources, underscore the importance of low-pressure, flexible engagement opportunities and targeted outreach to early-career students. Additionally, the presence of Black women in the community served as a powerful motivator, reinforcing participants' belief in their ability to succeed in computing. This dissertation contributes to computing education by providing evidence-based insights on the role of virtual communities in fostering diversity and inclusion. It highlights the potential of technology-driven interventions to break down systemic barriers and offers practical recommendations for institutions, educators, and policymakers to build sustainable, culturally responsive support networks. By demonstrating the impact of virtual communities in creating empowering and affirming spaces, this research emphasizes the need for intentional efforts to ensure Black women in computing are not just included, but supported, valued, and positioned for success in the field.
Most research on recommendation sources focuses on what happens before consumers make a purchase. This dissertation asks a different question: How do recommender types (AI vs. human) and product framing (utilitarian vs. hedonic) shape how consumers feel after the experience? Using Expectancy-Disconfirmation Theory (EDT) as a framework, this dissertation explores the influence of the variable, post-consumption satisfaction. It also examines whether consumer expertise moderates these effects.
A 2x2 between-subjects experiment (N = 500) tested the impact of recommendation source and product framing using a common stimulus—a jazz clip—framed as either utilitarian or hedonic, and recommended by either an AI or a human. Results revealed that consumer expertise was the most consistent and powerful predictor of post-consumption satisfaction across all conditions. The type of recommender and product framing had little to no direct effect. Of the anticipated effects, only a modest increase in satisfaction for AI-recommended utilitarian products, approached statistical significance.
Contrary to expectations, the post-consumption experience is less about who recommended the product and more about how confident the consumer feels in the category. These findings challenge assumptions about matching recommender type to product type and suggest a shift toward tailoring recommendation strategies based on consumer expertise.
Generative Artificial Intelligence (AI) has emerged as a transformative force in marketing, transitioning from traditional technological tools designed to execute predefined tasks to intelligent agents capable of learning, creating, deciding and adapting autonomously. Unlike other technologies, AI possesses the ability to analyse data, generate insights, and autonomously determine optimal courses of action in real time. However, this technological progress is accompanied by a significant gap in understanding AI’s full potential and limitations, leading to widespread misconceptions on how AI agents can be used effectively and ethically. These uncertainties highlight the need for a comprehensive framework to guide Generative AI integration into marketing practices.
The Primary challenge addressed in this dissertation is driven by the lack of a marketing-specific theoretical framework to guide the integration of AI logic into marketing practices. To bridge this gap, this study proposes a novel theoretical framework based on Hunt’s inductive realist approach, specifically designed for AI-driven marketing practices. By positioning AI as both a creative and decision-making agent, the framework highlights the necessity of iterative refinement and ethical alignment to ensure AI applications resonate with societal values and address evolving consumer expectations.
This research adopts a novel two-step methodology, grounded in the indigenous theory development inductive realist approaches to construct an initial theoretical framework for AI in marketing. The approach emphasizes foundational premises and iterative propositions, providing a structured yet adaptable model ideal for addressing the complexities of emerging research domains. Further an empirical study is designed to identify perceptions of AI, uncovering key themes above. Cognitive maps are constructed to visualize the relationships among these themes, providing insights into how they interact and influence marketing outcomes. This empirical analysis designed to further assist theoretical advancements, offering a robust foundation for future research and practice of AI-driven marketing strategies.
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As heterogeneous parallel architectures grow increasingly complex, achieving high performance and effectively teaching parallel programming have become more challenging. Benchmark suites are powerful tools for illustrating and evaluating optimization techniques in practical, performance-critical scenarios. But commonly used parallel benchmark suites (e.g., SPEC OMP and Rodinia) are primarily designed for performance assessment purposes only. They are not intended for performance optimization training or educational instruction in parallel programming. Furthermore, their complexity in configuration and deployment often limits their accessibility, reducing their practical utility for researchers and students in educational settings. To address these limitations, this dissertation presents two novel benchmark suites, NeoRodinia and CUDAMicroBench, that support not only performance evaluation, but also the exploration of optimization strategies. These suites are further augmented with educational features, such as integration with large language models (LLMs) for optimization guidance and interactive, browser-accessible execution environments.
NeoRodinia features a structured three-level parallelization model (P1, P2, P3) across CPU worksharing, GPU offloading, SIMD, and tasking. It provides standardized execution workflows, automated performance evaluation scripts and visualization tools. Additionally, NeoRodinia integrates AI-assisted analysis, allowing LLMs to offer optimization recommendations and debugging insights. CUDAMicroBench is a modular microbenchmark suite targeting key GPU optimization challenges such as memory hierarchy usage, warp divergence, and concurrent kernel execution, serving as a practical reference for GPU performance tuning.
In addition to benchmark-based contributions, this dissertation advances parallel programming education by introducing the Interactive OpenMP Programming book. By employing deliberate prompt engineering strategies, it effectively leverages large language models (ChatGPT-4, Gemini Pro 1.5, and Claude 3) to enhance the quality, relevance, and pedagogical value of the generated content. Delivered via a Jupyter-based environment, it enables real-time experimentation with OpenMP constructs, promoting hands-on learning and deeper understanding.
Collectively, these contributions form a unified educational infrastructure for modern parallel computing. By combining benchmarking, structured optimization guidance, and LLM-driven interactive learning, this work bridges performance engineering and pedagogy, providing a scalable and adaptable solution for educators and learners in today's heterogeneous HPC landscape.
