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Towards A Programmable Nanomechanical Interface for Mediating Spin-Spin Interactions
No full textSolid state spin qubits are promising candidates for quantum information processing. In particular, the nitrogen vacancy (NV) center in diamond is known to have coherence times exceeding milliseconds even at room temperature. However, due to the limits of qubit fabrication and the short-range nature of magnetic dipolar interactions, it remains difficult to generate programmable interactions between a large number of NV centers. To address this challenge, it has been proposed to use nanomechanical resonators as a mesoscopic interface between solid state spin qubits. In this thesis, I will describe experimental efforts in building a scanning probe platform, where individual NV centers in diamond nanopillars are coupled to magnetially functionalized silicon nitride mechanical resonators. The scanning probe configuration enables programmable connectivity via mechanical transport of the nanopillars. Proof-of-principle measurements show that the coherence of the NV center is preserved despite relative movement in a magnetic field gradient, by utilizing the nitrogen nuclear spin as a quantum memory. I will also describe measurements of the spin-mechanical coupling via both DC and AC magnetometry. Finally, I present some preliminary results related to sensing of a single NV center with the mechanical resonator, which demonstrate the high level of control over each subsystem. With realistic improvements to several system parameters, high spin-mechanical cooperativities are feasible, offering a new avenue towards scalable quantum information processing with spin qubits.Physic
To Heat or Eat: Current Patterns of Energy Poverty and Redlining in the United States
No full textRedlining, a federal housing policy that began in 1934 and continued until 1968, continues to shape economic and environmental injustices across the United States. This housing policy restricted minority groups and people of color from accruing the most common form of social and monetary equity and wealth over much of the mid-20th century. It is possible that people in these groups currently face a higher energy burden, defined as the percentage of gross household income spent on energy costs (National Renewable Energy Laboratory, 2022). The objective of my research was to understand the relationship between the current spatial distribution of energy burden and the historical redlining process in the residential housing sector across different cities in the United States. The two major questions I addressed were: What is the pattern between current instances of energy burden and places where redlining was applied? And based on the statistical regions identified by the U.S. Census Bureau (e.g. Northeast, Midwest, etc.), what are the regional differences in these patterns when comparing energy burden vs temperature?
Related to question 1, I hypothesized that the chance of being energy burdened was higher in formerly redlined communities compared to the three other respective classes that comprise the non-redlined communities. I further hypothesized that the northern regions of the United States have higher variances in the instances of energy burden when comparing formerly redlined communities to non-redlined communities within a region, due to the longer and colder winters in these areas compared to the southern regions. A new method for dataset development was used, followed by statistical analysis to identify the potential patterns between redlined areas and rates of energy burden.
The results showed that census tracts located within formerly redlined communities have higher instances of energy burden, with the lowest energy burdens in communities that were graded A, and slight increases in energy burden for the remaining grades C, B and D. This trend was consistent for all U.S. statistical regions. Cities in the South showed the widest range of energy burden values for communities formerly graded at C and D. The research results could be used in policymaking to justify programs targeting formerly redlined areas to alleviate energy burden through weatherization and energy affordability programs from the state and federal level.Extension Studie
Fourth Down and Forward: Predictive Modeling and the Future of Football Analytics
No full textThis thesis explores the analytics behind fourth-down decision-making in football, with the goal of building a data-driven framework that can recommend the best option—go for it, kick, or punt—based on game context. While debates about fourth-down strategy have grown in recent years, most conversations lack a structured model that links outcome probabilities, expected field position, and long-term value into a single decision-making pipeline. This project takes on that challenge, building out a modular system of predictive models designed to work together to simulate and evaluate real fourth-down scenarios.
Two modeling pipelines were developed: one based on generalized linear models, and another using XGBoost. Each approach included multiple components: success probability classifiers, expected yard line regressors, expected points calculators, and a decision bot that combines outputs to recommend the optimal play. The generalized linear models provided a solid baseline, particularly in structured scenarios like field goal predictions. The XGBoost models, however, showed stronger performance in complex or nonlinear situations, thanks to more tailored feature engineering and specialized model design, especially the use of separate expected points calculators for first-and-10 versus fourth down.
Results showed that many of the bots’ decisions aligned with standard coaching logic, while others revealed opportunities for more aggressive or unconventional play-calling. Still, both modeling pipelines struggled with extreme edge cases, highlighting the need for future models to better capture sharp drop-offs and rare outcomes. This thesis closes by outlining a roadmap toward reinforcement learning as a more dynamic solution—one capable of reasoning across entire drives, adapting to context, and optimizing for long-run reward.
By connecting predictive modeling with real-world strategy, this work offers both a proof of concept and a foundation for future systems that aim to bring more clarity, consistency, and context to in-game decision-making.Computer Scienc
Social Response to Mindfulness in Azerbaijan: The Impact of Skepticism and Stigma on Interest in and Willingness to Practice Mindfulness Among Azerbaijani Adults.
No full textMindfulness practices are known for their effectiveness in improving people’s subjective well-being on both physiological and psychological levels. Research on this topic is gaining popularity. The psychological research in Azerbaijan, on the other hand, is not well-developed, and many gaps in existing research need to be addressed to gain a better understanding of the psychological mechanisms of this culture. To address the question of whether skepticism and stigma around psychology and mental health present in Azerbaijan would serve as a barrier to people’s perceptions of mindfulness practices, I conducted a pilot study that aimed at measuring Azerbaijani adults’ interest in and willingness to practice mindfulness. I examined whether informational triggers regarding physical versus mental health benefits of mindfulness would influence their responses. I hypothesized that because of skepticism and stigmatization that create negative beliefs and expectations around psychology and mental health, with the presence of informational trigger in the context of mental health, there would be a barrier to being open-minded and willing to participate in mindfulness-related activities and practices, despite them being beneficial. The responses of 91 participants were analyzed by performing a one-way ANOVA. The hypothesis was supported by the results of the study that suggest that people in the “mental health” information group demonstrated lower levels of interest in and willingness to practice mindfulness. Implications, limitations, and future directions arising from these findings are also discussed in this paper
Preparing Students for College-Level Mathematics Through Secondary Advanced Placement Courses – IB, A-level and AP
No full textStudents in international schools are asked to learn subjects from foreign curricula, enabling them to pursue international education in college. Three of the more common curricula chosen by parents and students are International General Certificate of Education (IGCSE), the International Baccalaureate Diploma (IB DP) and the Advanced Placement (AP). At the end of their intensive one- or two-year studies with these foreign curricula, students must take exams that not only serve as benchmarks for schools’ reputations, but also signal the students’ readiness for college mathematics. The advanced curricula also play crucial role in shaping the students’ learning styles and significantly influence their perceptions of mathematics as they enter undergraduate studies.
This thesis examines the extent to which advanced high school mathematics exams (A-Level, IB and AP) prepare students for college-level mathematics by analyzing the cognitive demands of the associated exam questions. Past exam questions from May and June of 2021 2022, and 2023, published by the Cambridge International Examination (CIE), International Baccalaureate (IB), and College Board, are analyzed. The cognitive demands of these assessment papers are evaluated using two distinct frameworks: the Complexity, Abstractness, and Strategy (CAS) framework and the Mathematical Assessment Task Hierarchy (MATH) Taxonomy.
This thesis reveals that all three exams contain many low-cognitive demand questions that can be solved through rote memorization and mimicry of mathematical reasoning while lacking probes for students’ conceptual understanding. As a result, these exams often fail to bridge the gap between high school and undergraduate mathematics, perpetuating misconceptions about the nature of the subject. Despite these commonalities between the programs, the findings also indicate differences between the exams, which could serve as stepping stones toward undergraduate mathematics
Dear Church: Hope and a Future from the Wilderness of American Purity Culture
No full textThis project-in-process examines the legacy of evangelical purity culture (EPC) through narrative theology, textual analysis, and pastoral reflection. It foregrounds lived religious experience, arguing that EPC's emphasis on behavioral control and moral absolutism has caused significant relational and spiritual harm. Framed as a letter to the Church, the project critiques EPC's internal logic while speaking in its own vernacular: testimony, confession, and biblical allusion, attempting to engage on shared moral ground. Drawing from trauma theory and the corpus of post-EPC literature, it proposes reparative tools grounded in sacred values reframing work for individuals and their communities as both or either seek reparative action. It calls for institutional and interpersonal healing rooted in biblical repair process (witness, confession, and atonement) as an act of faith, not a promise of outcome: hope, not certainty, for a future for the church and all her children.Author's Origina
That Hill Called Calvary and Other Parables
No full textA collection of stories written with the intention of upsetting and questioning many of the beliefs and assumptions Christians take for granted. This collection follows in the parabolic tradition of Christ with the hope that through these parable faith might be challenged and edified.Author's Origina
OSEP, SSIPs and the Future of Special Education: Practitioner Leadership in an Evolving Federal Landscape
No full textThe Office of Special Education Programs (OSEP) is a division of the United States Department of Education (Ed). OSEP’s mission is to lead the nation’s efforts to improve outcomes for infants, toddlers, children and young adults with disabilities, birth through twenty-one years old. However, a decade ago OSEP recognized a need to place a greater emphasis on student outcome results as compared to procedural compliance. In 2014, the State Systemic Improvement Plan (SSIP), a comprehensive multi-year plan, was developed as part of OSEP’s Results Driven Accountability (RDA) initiative to improve early intervention and educational services, including special education and related services for children with disabilities.
The SSIP was intended to be a key lever that allowed states to formally focus on system-wide improvement strategies. While each year states have submitted these plans as a part of their annual performance report, because of competing and shifting priorities, OSEP has placed varying levels of focus on supporting implementation of the SSIP, and ongoing staffing capacity issues have negatively impacted OSEP’s ability to assess the fidelity of SSIP implementation.
As a resident, I worked in the Office of the Director (OD) in OSEP examining SSIPs from all sixty states and territories. Additionally, my role involved creating a SSIP advisory group to address critical questions about the effectiveness, sustainability, and scalability of these plans, diving deeply into the data to understand both the successes achieved and the ongoing challenges. With a decade of SSIP data and years of implementation by states, this work offered a rare chance to provide insights into how large-scale educational reforms evolve over time, adapt to emerging needs, and drive lasting change in special education.
This Capstone chronicles my efforts to support OSEP’s desire to examine national SSIP impact as it works toward its mission. My analysis offers recommendations for OSEP to 1) add to the already existing OSEP infrastructure to routinely examine the SSIP and build MSIP State Lead capacity to support states, 2) work collaboratively through effective teaming as a necessary and consistent framework to engage in data improvement cycles to inform decision making and, 3) utilize psychologically safe containers to build and strengthen relationships within shifting political environments to maintain focus on the progress leaders are trying to achieve.Educatio
"Is this data?" Investigating How Curators Define, Recognize, and Repair Research Data in Data Repositories
No full textThis dissertation project empirically answers the question: What is research data?
Data has many definitions, meanings, and forms; the concept of data as recorded evidence is popular among scientists. However, what counts as data depends upon the discipline, its role in a research study, how data is created or collected, and who defines it. Furthermore, definitions are not neutral; they are informed by scientific discourse and use. Images, numerical tabular files, and other commonly accepted examples are abundant. In contrast, edge cases may highlight unusual, dissenting, or marginalized viewpoints and the politics underlying consensus formation. This project investigated the characteristics of these data anomalies and curators’ engagement with them.
To understand how data curators recognize anomalous datasets deposited in research data repositories and transform them into acceptable research datasets, I used quantitative and qualitative methods to answer two research questions: RQ1: What are the characteristics of anomalous datasets? and RQ2: How do data curators identify and repair anomalous datasets? Acceptable research datasets meet minimum repository-specific curation expectations for metadata, data, and documentation files. The term does not imply that observations or values within the files are valid or correct, nor that the dataset meets a Platonic ideal. In contrast, anomalous datasets do not contain data files, are missing other essential elements, or otherwise leave curators uncertain about the presence or absence of research data.
During this study, while working as a Dataverse Project software manager at the Institution for Quantitative Social Sciences (IQSS), I used trace information analysis and quantitative methods to answer RQ1, examining the characteristics of 89,625 datasets deposited in the Harvard Dataverse Repository from 2007 to August 22, 2023. I combined this metadata with information from 315 user support tickets about anomalous datasets in the IQSS Request Tracker (RT) system to understand the differences between acceptable and flawed datasets.
My nine quantitative hypotheses investigated the relationships among datasets classified as Acceptable, Anomalous, or Unknown, as well as among subjects, file formats, the presence of optional metadata blocks, collection categories, publication status, the existence of restricted files, and the overall distribution of anomalous dataset types (e.g., Not data, Missing data). All study hypotheses were supported, indicating that anomalous datasets display distinct characteristics whose presence points to opportunities for workflow, repository software, and machine-assisted dataset quality improvements. As expected, anomalous datasets were more likely to be associated with rarer subject areas and file formats, to use fewer subjects and fewer optional metadata blocks, to have restricted files, and to be deaccessioned more frequently. Additionally, my analysis revealed a relationship between dataset classifications and collection category. However, group collections were unexpectedly more likely to contain anomalous datasets than individual collections. Also, as expected, anomalous dataset types were not equally distributed, with more Not data datasets present than other types. My quantitative investigation also indicated how well-curated collections can affect overall repository data quality and discussed the benefits and limitations of trace information analysis for capturing and interpreting past critical incidents and curator interventions.
During the qualitative phase of the study, I used critical incident technique (CIT) and semi-structured interviews to answer RQ2. I asked 19 data curators at North American, European, and African repositories what they look for and how assess the “dataness” of repository users’ datasets. During each hour-long interview, participants shared their data inspection, recognition, and repair procedures and described indicators signaling the presence or absence of research data in users' deposits. They also shared how repository policies, academic and professional experience, curation goals, and perceptions of future data reusers shaped their definitions of research data, acceptable dataset characteristics, and data curation. Participants reported 93 encounters with anomalous datasets in 10 categories, including Missing content, Perhaps data, Not data, Broken data, and other types not mentioned in the literature. They also noted 19 specific anomalous dataset indicators of the presence or absence of file- and deposit-level characteristics, six categories of data sensemaking procedures, and four data repair categories. These results indicate that data curation involves sensemaking and that curators perform invisible work while recognizing and repairing datasets.
Results from both streams of inquiry enhance research and practice in data curation, showing how repository policies, technical requirements, capabilities, and curators’ knowledge influence their research data operationalization procedures. The findings deepen our understanding of how curators implement acceptable research data in research data repositories and how repository infrastructure and data curators' competencies shape these engagements and outcomes. Furthermore, they contribute to research on curation workflow modeling, the characteristics of research data, and the often-overlooked work of curators’ invisible data sensemaking and repair. The results also underscore how curation tends to homogenize research datasets’ forms and characteristics, revealing that acceptable dataset definitions are not neutral but are political acts involving power dynamics. This confirmation furthers the discussion about how information and data workers' methods and goals may silence, devalue, or eliminate ways of knowing, such as traditional knowledge systems whose outputs may not conform to common curation practices.
Additionally, I make practical recommendations for reducing the number of anomalous repository deposits, improving dataset findability and reusability, accounting for and supporting data curators’ work, and improving data curation training. These developments will help improve dataset reusability, reduce curation workflow frictions, and help further awareness of curation costs.Accepted Manuscrip
Closing the Immunization Gap in Mozambique: A Behavioural Science Approach to Vaccine Uptake
No full textThis report applies behavioural science to analyse the persistent decline in routine childhood immunization in Mozambique. Using Ipsos household data, latent class analysis, and gender-dynamics mapping, it identifies socio-cultural, cognitive, and informational barriers that limit vaccine uptake. A review of 58 global interventions informs a set of behaviourally grounded recommendations—spanning community-led communication, mHealth reminders, social-norm messaging, and improved health-worker interaction—to address demand-side frictions. The report offers a scalable framework for strengthening routine immunization in low-resource settings.Author's Origina