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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.
ABSTRACT
Ryan Chester
ATHLETIC IDENTITY AND THE CAREER DEVELOPMENT EXPERIENCES OF DIVISION II BLACK STUDENT ATHLETE BASKETBALL PLAYERS
(Under the direction of Dr. Mark D’Amico)
The purpose of this qualitative study was to explore the lived experiences of Black men and women, NCAA Division II basketball players regarding their athletic identity and career development experiences. The goal of the study was to identify better ways for institutions to equip this population with the support, guidance, and the skill set they will need for their inevitable retirement from their athletic playing careers. The study was guided by three research questions, (1) How do Black student athletes, who compete in men’s and women’s basketball at the Division II level, perceive their athletic identity? (2) What are the career development experiences of Black student athletes who compete in men’s and women’s basketball at the Division II level? (3) What is the potential relationship between athletic identity and the career development experiences of Black student athletes who compete in men’s and women’s basketball at the Division II level? The study gauged participants level of athletic identity using the abbreviated 7-item version of the Athletic Identity Measurement Scale (AIMS) (Brewer & Cornelius, 2001) and utilized the Interpretative Phenomenological Analysis (IPA) approach to analyze the data of eight individual interviews. Findings outlined the importance of exposure for Black student athletes and demonstrated how a student athlete’s professional athletic playing desires can impact their career-related decision making. Findings also explored the specific challenges that Black student athletes face while on their college campus.
This study examines the relationship between Enterprise Risk Management (ERM) maturity and firm performance, with an emphasis on how organizational complexity, industry type, and board engagement may moderate this relationship. Although prior research has broadly acknowledged ERM’s role in mitigating risks, few studies explore how ERM maturity contributes directly to firm performance outcomes. Additionally, the literature often treats ERM as a static process rather than a dynamic capability that evolves and adapts within different organizational structures and industry-specific environments. This gap highlights an important opportunity to examine ERM maturity as a strategic asset that enhances decision-making, optimizes resource allocation, and drives sustainable competitive advantage.
Drawing on the Resource-Based View (RBV), Agency Theory, and Contingency Theory, this study addresses these gaps through a quantitative survey of risk professionals from 111 publicly listed companies. By focusing on how ERM maturity impacts firm performance and controlling for variables such as firm size, market volatility, and leverage, this research seeks to provide nuanced insights into ERM's role as a driver of resilience and adaptability. The findings will contribute to the literature by offering a more comprehensive view of ERM maturity’s influence across diverse organizational and industry contexts, ultimately providing practical guidance for companies aiming to refine their risk management capabilities in pursuit of competitive advantage.
Policies and procedures in modern society have unintentionally perpetuated inequities for marginalized students, particularly African Americans, exacerbating math anxiety. Despite efforts to improve mathematics education, African American students continue to experience disproportionately high rates of math anxiety, negatively impacting academic performance, mathematical identity, and future opportunities. While there is extensive research on math anxiety among African American girls, there is a notable lack of studies focusing on African American, elementary aged, boys. Through a qualitative, case study design, the research utilizes a questionnaire, classroom observations, and student interviews to gain insights into the emotional and psychological challenges African American 3rd grade boys face in mathematics. The study investigates the role of race, gender, and socio-economic status in shaping African American 3rd grade boys' experiences with math, as well as the impact of instructional practices and teacher-student relationships on mitigating or exacerbating math anxiety. Findings from this research contribute to the broader conversation about inequities in mathematics education, highlighting the need for culturally responsive teaching strategies and support systems to foster confidence and resilience in students from marginalized communities. This dissertation aims to provide actionable recommendations for educators to recognize, address, and reduce math anxiety in young learners, with a particular focus on African American boys, to promote positive mathematical identity and long-term academic success.
Flooding is one of the most frequent and destructive natural disasters, posing significant risks to communities, infrastructure, and economies worldwide. In North Carolina, diverse flood types—driven by varied topographic and climatic conditions—necessitate adaptable forecasting methods. Traditional flood prediction models rely on hydrological and meteorological data but often struggle to incorporate the complexity of different flood types within a single framework. This research explored the application of machine learning (ML) techniques in flood forecasting, specifically evaluating how different ML models handle varying data requirements across different regions with flood types.
Focusing on the Upper Haw River and Cape Fear River watersheds in North Carolina, this study employed a Convolutional Neural Network (CNN) to predict flood stages using hydrometeorological data, including rainfall, elevation, distance from the river, soil type, land use/cover, wind speed, wind direction, soil water volume, lag feature, and gauge stages. Rather than comparing ML accuracy against traditional hydrological models, this study examined how ML models can adapt to diverse flood conditions and data constraints. The results indicate that CNN-based models effectively capture spatial dependencies and patterns, providing valuable insights into the role of different input features, such as lag effects and rainfall distribution, in flood prediction.
A Command Line Interface (CLI) was developed to enable real-time interaction with the model, enhancing its usability for decision-makers. The study highlighted the strengths and limitations of ML-based forecasting, demonstrating its potential while identifying areas requiring further refinement, such as incorporating additional meteorological variables and real-time data. Aimed to evaluate the feasibility of using ML for statewide flood prediction to encompass a range of flood causes, this research also contributed to determining the boundaries of coastal and piedmont regions and the type of data requirements to develop flood forecasting models in these regions.
Partly interval-censored failure time data are common in clinical and epidemiological studies, where the failure time of interest is either exactly observed or known to lie within a specific interval. Additionally, two-phase sampling designs are often employed to measure covariates for a subset of participants, reducing study costs. This paper addresses regression analysis of partly interval-censored failure time data under stratified semiparametric transformation models, incorporating time-dependent covariates subject to: (i) missingness due to two-phase sampling and (ii) measurement errors during observation. We propose a maximum weighted likelihood estimation method and develop an EM algorithm for implementation. A weighted bootstrap approach is introduced for variance estimation, and the asymptotic properties of the proposed estimator are established. Extensive simulation studies demonstrate the method’s satisfactory finite-sample performance, and its practical utility is illustrated through an application to data from the HIV prevention trials HVTN-703/HVTN-704.
Partly interval-censored failure time data are common in clinical and epidemiological studies, where the failure time of interest is either exactly observed or known to lie within a specific interval. Additionally, two-phase sampling designs are often employed to measure covariates for a subset of participants, reducing study costs. This paper addresses regression analysis of partly interval-censored failure time data under stratified semiparametric transformation models, incorporating time-dependent covariates subject to: (i) missingness due to two-phase sampling and (ii) measurement errors during observation. We propose a maximum weighted likelihood estimation method and develop an EM algorithm for implementation. A weighted bootstrap approach is introduced for variance estimation, and the asymptotic properties of the proposed estimator are established. Extensive simulation studies demonstrate the method’s satisfactory finite-sample performance, and its practical utility is illustrated through an application to data from the HIV prevention trials HVTN-703/HVTN-704.
Hydrodynamic simulations are computational models used to study the behavior of fluids. The data generated by these simulations contains critical information about fluid dynamics, and researchers utilize data analysis techniques to extract meaningful insights, which are essential for optimizing designs and making informed decisions across diverse research fields. Traditionally, data analysis has been performed through post-analysis. For post-analysis, the data is saved during the simulation, and the analysis is performed afterward using the previously saved data. This approach involves extensive data storage and transfer, which becomes increasingly challenging as simulation data grows in volume and complexity. Another approach named in-situ analysis has emerged as an alternative, where data is analyzed directly within the system or environment where it is generated during the simulation. This approach eliminates the need for storing and transferring large amounts of data, making it more scalable and efficient for analyzing hydrodynamic simulations.
However, existing in-situ analysis methods and tools have three challenges: First, human intervention is frequently required in in-situ analysis to ensure high-quality data analysis, but it often interrupts the simulation. In this way, the simulation needs to be paused to wait for human interpretation. The requirement for expert knowledge further hinders the efficiency of in-situ analysis, as non-expert users may struggle to make appropriate decisions during runtime. Second, in-situ analysis struggle to balancing data analysis with requirement for minimal computational cost. As simulation data grows in volume, the complexity of data analysis algorithms also increases for extracting more information from the simulation to enhance the quality of data analysis. They often come at the expense of increased computational overhead, potentially disrupting simulation efficiency. Third, implementing in-situ analysis is not straightforward. While existing frameworks typically support in-situ data collection or visualization, extracting meaningful insights from the collected simulation data still requires additional manual effort.
To address these challenges, this dissertation proposes an automated in-situ analysis approach for hydrodynamic simulation. While prior in-situ analysis efforts have focused on data collection and visualization, we approach the problem from a new perspective by formulating in-situ analysis as a feature extraction task. This shift enables more targeted and automated insight generation within the simulation process. First, based on domain discretization and the iterative nature of hydrodynamic simulations, our method aligns tasks to simulation steps and spatial decomposition. This allows data collection and analysis to be triggered automatically. Users define analysis goals and conditions before the simulation; once running, the system triggers analysis when conditions are met, continuing until objectives are achieved. Second, to balance analysis quality and computational performance, we employ lightweight, simulation-agnostic algorithms such as tuning, variable tracking, and surrogate modeling. These algorithms efficiently extract meaningful insights with minimal overhead, ensuring simulations remain uninterrupted. Third, to simplify implementation, we provide a flexible framework with a user-friendly interface. Users only need to specify key variables, models, and triggering conditions, reducing programming effort and making high-quality in-situ analysis accessible to non-experts. We evaluate the effectiveness of our approach across a range of hydrodynamic simulation applications, including proxy simulations such as LULESH, Laghos, and Kripke, as well as large-scale simulations like Castro and ImpactX. Our findings demonstrate that this automated in-situ approach provides an efficient, scalable solution for hydrodynamic simulation analysis, bridging the gap between usability, accuracy, and computational efficiency.
Limited research has explored the impact оf non-academic extracurricular programs for Black girls. This study addresses that gap by examining how such programs can empower and affirm Black girls' identities. Existing literature often frames Black girlhood through respectability politics оr as a problem tо be solved. This research focuses оn STARS, an in-school gender-specific program іn Washington, D.C.’s majority-Black wards, designed tо foster safe, supportive environments for Black girls. Drawing оn the researcher’s experiences as both a program leader and participant, the study investigates how STARS promotes identity affirmation, leadership, and community-building іn urban schools. Using an embedded single case study design, STARS іs analyzed through Edward Soja’s Thirdspace Theory, viewing іt as a transformative space where marginalized students navigate social norms and community challenges. Additionally, Mikki Kendall’s Hood Feminism frames how STARS, as a thirdspace, intersects with race, gender, and socioeconomic status tо enhance agency, challenge systemic barriers, and reimagine belonging іn schools. Through interviews, focus groups, and document analysis, this study offers insights into the effectiveness оf STARS and its potential for creating inclusive, empowering learning environments. It aims tо enrich literature оn Black girls’ educational journeys by centering their voices and advancing equity and empowerment.
