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Disease(d) Women: Race, Medicine, and Womanhood in Medical Illustrations
Get PDFWhat is a woman, and who gets to decide? And how have artists and physicians been thinking about and visualizing the answers to these concerns over time? In this thesis, I investigate how visual materials have addressed these concepts in a series of case studies of medical illustrations spanning both space and time. In doing so, I examine the relationship between gender and the medicalization of the body through the lens of art history. I argue that medical illustrations play an essential role in both constructing and upholding dominant ideologies of womanhood.
I focus on anatomical images and accompanying texts describing the uterus and related bodily structures from three distinct historical moments and geographies: the thirteenth century English medical treatise MS. Ashmole 399, a surgical text by nineteenth century American gynecologist J. Marion Sims, and the twenty-first century textbook Netter’s Atlas of Human Anatomy. Each of these periods and case studies represents moments marked by increased interest in and desire to understand, describe, define, and medically intervene in women’s bodies. I assert that these motivations are made visible in the resulting medical illustrations.
While scholars from various fields have considered these three periods of time and their respective ideologies of gender and medicine separately, I propose that putting these case studies in conversation allows for a more capacious understanding of each. Furthermore, I propose that situating this analysis within the field of art history provides productive interdisciplinary interventions into the field. Through my work, I trace a cross-temporal and cross-geographical thread that demonstrates how medical imagery naturalizes conceptions of womanhood linked to physiology, whiteness, and transphobia.</p
Ecomedia Art: Emerging Platforms, Practices, and Performances for DIY Citizenship
Get PDFThis practice-led study examines how ecomedia art and DIY citizenship counteract disempowering climate narratives by making engagement more accessible and fostering collaborative, participatory approaches. Through hybrid workshops, meetups, and interactive installations, this dissertation emphasizes creative media-making as a form of ecological engagement, and examines how ecomedia practices generate dialogue, expand the possibilities of environmental storytelling, and reinforce connections to the ecosystems we inhabit.
Climate change communication often relies on climactic narratives that evoke fear, guilt, or disempowerment rather than fostering meaningful engagement or addressing the systems of extraction and exploitation that drive ecological crises. My creative contributions, including Ecomedia Colorado, Extemporal Media Arts Meetups, and Disaster Girl performances and installations, merge storytelling with local ecologies while examining the potentials and limitations of participation in fostering sustained engagement. Through these works, I argue for the role of interactive and participatory methodologies in reshaping climate discourse and fostering agency in response to ecological crises. The findings suggest that transformation occurs through both meaning-making and material-making, turning engagement with environmental issues into an interactive experience. Yet, without intentional structures, even interactive projects risk becoming ephemeral rather than fostering sustained ecological engagement.
This dissertation brings together works from media art, literature, and practice-led research, exploring how ecomedia art and DIY citizenship foster collaborative, participatory approaches to counter disempowering climate narratives. It examines three key themes: Constraints, Feedback, and Emergence, that reveal multimodal experiences, connecting participants to environmental and social challenges. The findings advocate for participatory, place-based, and multimodal making to support sustained ecological engagement, culminating in a call for generative and regenerative practices in media arts and climate change communication.</p
Identifying Sources of VOCs in Commerce City, Colorado: Positive Matrix Factorization Applications
Get PDFVolatile organic compounds (VOCs) are a diverse class of air pollutants originating from both natural and anthropogenic sources, playing a significant role in environmental degradation and adverse public health outcomes (Kampa & Castanas, 2008; Liu et al., 2022). VOC monitoring has expanded in recent years, but often these data are not put to use to answer valuable questions due to the significant challenges associated with working with multiple datasets collected through diverse methods. However, given the current landscape within research, regulatory, and citizen science data collection practices, determining issues and best practices to effectively utilize disparate data sets for complex analysis is a beneficial, and increasingly necessary, endeavor. Commerce City, Colorado, presents a unique case for VOC analysis due to its disproportionate exposure to industrial pollutants and the socio-economic vulnerabilities of its residents (Community Health & the Environment in Commerce City-North Denver | Colorado Environmental Public Health Tracking, n.d.). This work leveraged Positive Matrix Factorization (PMF) to identify and quantify the primary sources of VOCs and their contributions to hazardous air pollutants (HAPs) in the North Denver region.
This work will detail the procedures established within the research framework to ensure robust VOC source apportionment. Sensitivity analyses were conducted for several typical PMF implementation choices including uncertainty levels, missing data imputation, and background subtraction. These analyses highlight the importance of methodological precision in source apportionment studies. 10-15 percent uncertainty in PMF analysis was found to produce more consistent factor resolution. Additionally, background subtraction played a critical role, particularly for non-VOC species like CO, NOx, CH4, CO2, and ethane due to their comparatively longer atmospheric lifetime, with percentile species-based subtractions between the 5th and 20th showing the best results for reducing residuals and resolving more local sources. Moreover, the research emphasizes a cautious approach to data imputation for missing or below detection limit values, where median substitution should be used under strict criteria to avoid biasing results. This work also explores the potential of PMF and complementary methods like Clustering and interval based Conditional Bivariate Probability Function (CBPF) to refine the identification of pollution sources. Although source apportionment across diverse datasets presents specific challenges, careful implementation can provide opportunities to enhance factor identification by examining profile and contribution relationships across different sites.
The research analyzes data from multiple monitoring sites across the Commerce City North Denver region (CCND), including: three fixed location continuous monitoring sites, quarterly, 1-hour, canister data collected at 10 sites dispersed through the CCND region, and one mobile monitoring campaign in the CCND region. These results reveal six main sources of VOCs: Oil Processing, Short-Lived Oil and Gas, Liquid Gasoline, Vehicle Exhaust, Fugitive Natural Gas, and Semi-Complete Natural Gas Combustion. While the identified sources remain consistent across the region, their contributions vary spatially. Contributions to HAPs from Oil and Gas Refinery activities are reduced with distance making vehicle and other industry related transportation a dominant source of HAPs in some CCND neighborhoods. These findings illustrate the need for site-specific air quality management strategies, particularly when addressing total VOCs for ozone reduction versus HAPs for public health protection.
This study offers critical insights into the challenges and opportunities associated with integrating multiple data sets for source apportionment, contributing to a growing body of knowledge that can guide future air quality research. These findings have direct implications for ongoing air quality management efforts by the Colorado Department of Public Health and Environment (CDPHE), supporting more informed decisions for protecting vulnerable communities, particularly in regions facing disproportionate exposure to industrial emissions, ultimately aiding in the pursuit of environmental justice and public health improvements. </p
Investigations into the Social-Cognitive Impacts of Modality, Teacher Behavior, and Prior Knowledge on the Tertiary Computer Science Learning Environment
Get PDFWe live in a world defined by computer science. The ability to understand computers well is both a necessary skill and a difficult challenge in the contemporary educational landscape. This dissertation reports on three studies—corresponding to three articles—which investigate the social and cognitive impacts of (1) modality, (2) teacher behaviors, and (3) prior knowledge on the tertiary computing learning environment, with the goal of improving student recruitment, retention, and learning. Sense of belonging (feelings of social support), programming self-efficacy (confidence in capability), and cognitive load (management of limited working memory resources) are the primary mid-level theoretical lenses for this work. In alignment with modality, the article Student Perspectives on Distraction and Engagement in the Synchronous Remote Classroom reports on an interview study investigating tertiary students’ experiences in synchronous remote computing courses, particularly considering how the synchronous remote modality influences the social and cognitive factors of cognitive load and social presence. In alignment with teacher behaviors, the article How do Teaching Practices and Use of Software Features Relate to Computer Science Student Belonging in Synchronous Remote Learning Environments? reports on a survey study investigating how tertiary computing students evaluated different teacher behaviors teacher use of tools, as well as how these practices correlate with student sense of belonging and overall course experience. In alignment with prior knowledge, the article How Can Instructor-Facilitated Transfer of Algebra Knowledge Support Learning for Novice Programming Students reports on a mixed-methods qualitative study investigating a strategy of teaching variable and function abstraction in introductory programming by encouraging transfer from student prior knowledge in algebra, with the social and cognitive goals of increasing student self-efficacy in programming and decreasing cognitive load during instruction. In my concluding chapter, I reflect on what my findings suggest about the consideration of social and cognitive psychological factors in computer science education research, what my findings suggest about the ability of computer science education researchers to learn from education in other disciplines, and directions for my future work.</p
Modeling Strategic Decision-Making on Networks with Context-Aware Agents
Get PDFMathematical models allow for deep analysis of observed phenomena by providing a framework to simulate reasonable outcomes when designing appropriately scaled experiments is not feasible. In our work, we develop several models to study the decision-making processes and overall success of individuals on a structured network when given a task and asked to come to consensus. Based on experiments, we investigate four cases: two different network structures (homogeneously mixed and spatially embedded) and two tasks of varying complexity. Importantly, we focus on effectively modeling the use of both contextual information (i.e., learning gained by interacting with others directly about a topic) and background information (i.e., personal biases about a topic brought into a learning situation) to make selections. A robust model for these experiments can give us insight into the ways that people combine social information and past experience to make decisions in groups. Implications of modeling these scenarios are a deeper understanding of how information is exchanged in online spaces and how regulations may be targeted directly at harmful discourse.</p
Multi-fidelity Uncertainty Quantification and Optimization
Get PDFThis thesis presents four novel bi-fidelity modeling approaches designed to enhance computational efficiency and accuracy in uncertainty quantification and optimization. First, Bi-fidelity Boosting (BFB) introduces an effective sketching-based subsampling method, accompanied by theoretical analysis of how inter-model correlation impacts performance. Second, the Bi-fidelity Variational Auto-encoder (BF-VAE) leverages deep generative models and transfer learning to achieve high performance with minimal high-fidelity data, also revealing connections between multi-fidelity learning and information bottleneck theory. Third, Langevin Bi-fidelity Importance Sampling (L-BF-IS) develops an efficient score-based Metropolis-Hastings importance sampling estimator for uncertainty quantification, whose effectiveness is linked to the discrepancy between model failure probability measures. Finally, a bi-fidelity zero-order optimization framework employs local multi-fidelity surrogates and an Armijo-based line search for optimal step sizes, demonstrating strong empirical performance supported by theoretical convergence guarantees under specific conditions. Collectively, these contributions advance multi-fidelity modeling by providing efficient, theoretically grounded methods for tackling complex computational challenges.</p
Buscando la Luz: Children’s Expressions of Dignity in a Co-Designed Workshop
Get PDFThis dissertation was designed to understand learning experiences that affirm and recognize the fundamental human right to learn. Human dignity is the mother value of the human person from which all rights flow (Barak, 2015). Therefore, to design for the fundamental right to learn is to design in accordance with human dignity. Educational dignity is defined as the cultivation of our mind, humanity, and potential (Espinoza et al., 2020). As part of this project, I wanted to understand how children experience education dignity. I worked with six of my former kindergarten students, who were then in fourth grade, and four of their siblings to create a social design-based experiment (SDBE)—an envisioned learning ecology of their desire. SDBEs seek to re-mediate unjust structures in pursuit of equitable and just outcomes (Gutiérrez, Jurow & Vakil, 2020). My SDBE re-mediated the ecology of learning by consulting with child partners to collectively plan and design the goals and participation structures of our summer workshop. The analysis for this dissertation was guided by three questions: 1) How did the child partners articulate learning and their desires for learning in the interviews and throughout the co-designed workshop? 2) Was there empirical evidence of the children experiencing educational dignity in the co-designed workshop? If so, how was it empirically observable? 3) Did my role as their former teacher transform through the co-design, and if so, how? Through a storytelling approach to presenting the findings, this study identifies children’s expressions of dignity articulated throughout the workshop as well as my pedagogical innovations (Alvarez, 2023) in pursuit of respecting the dignity of my child partners. Consequently, the child partners exhibited the development of a new consciousness in the ways they participated in our newly co-created interdependent learning ecology. Finally, this study underscores the role of critical reflexivity on my part as the researcher, participant, and former teacher for the actualization of the experience of educational dignity.</p
Complex Social Systems: Emergent Phenomena, Social Contagions, and Opinion Consensus
Get PDFThe study of opinion dynamics provides useful insights into large-scale trends in the formation and evolution of opinions in social networks and acts as a platform for rich dynamical behavior. The increased use of online social media, which allows for the rapid dissemination of information throughout a social network, makes the field more important than ever today. In particular, topics such as the propagation of false information and the formation of echo chambers and radicalized communities are relevant in today's social climate. In this Thesis I will present my research in the fields of opinion dynamics and social contagion theory. First, I discuss my work on a binary opinion model on hypergraphs where both individual agents and groups of 3 agents have opinions that evolve both through dyadic (i.e., pairwise) interactions and group memberships. This model contains parameter regimes with oscillatory and excitable dynamics that are highly sensitive to the structure of the underlying hypergraph. Second, I discuss my work on the spread of two competing beliefs in a social network where individuals have an internal opinion models their cognitive biases and modulates their likelihood of adopting one of the two beliefs. The addition of cognitive biases in the spreading process enriches its transient dynamics, facilitating behavior such as the revival of a dying belief and the overturning of an initially widespread opinion. The model is also studied with the presence of external recruitment of spreaders to examine how the intentional spread of information can lead to the eventual dominance of one of the two beliefs. Lastly, I discuss my ongoing work on the social compass model, a model of opinion depolarization. Particularly, I apply the Ott-Antonsen ansatz to successfully produce a low-dimensional description of the dynamics and study the onset of consensus for a large class of distributions of initial opinions. I also study a generalization of the model that includes community structure and external forcing.</p
Factual Knowledge-Enhanced Question Answering in Dynamic Environments
Get PDFThis thesis explores the realm of question answering (QA) systems, with a focus on thosethat leverage external knowledge to enhance their capabilities. While standard QA systems have demonstrated success, their integration with external knowledge unlocks new potentials, enabling them to reason over novel or conflicting information beyond their parametric knowledge. However, existing systems still face significant challenges, such as inaccuracies due to a lack of factual ground- ing, limited adaptability to dynamic environments. This research addresses these shortcomings and others by proposing innovative approaches that elevate the efficacy and reliability of QA systems.
Central to this work is the development of QA systems capable of handling a broad range of topics, modalities, and knowledge representations, with an emphasis on factual accuracy and contextual reasoning. Such systems hold immense potential to assist large populations, particularly in high-stakes domains like biomedicine, where individuals often face fear, confusion, and barriers to accessing affordable, quality healthcare. By providing accurate and actionable responses, these systems can empower users to make informed decisions in urgent situations.
The research progresses through three core areas: knowledge probing, knowledge usage, and model improvement. In Knowledge Probing, novel methods are developed to assess what models have learned internally, particularly in text-based and KG-grounded systems, given the significance of parametric knowledge in shaping model outputs. In Knowledge Usage, research focuses on charac- terizing how models leverage internal and external knowledge when answering questions, shedding light on their decision-making processes. Finally, in Model Improvement, methodologies are de- signed to address issues identified in prior research, enhancing QA systems’ performance, reliability, and factuality based on established desiderata.</p
Humanitarian Engineering Education: Exploring the Evolution of Student Self-Efficacy, Career Expectations, and Capacity To Transform Engineering Structures During Their Graduate Education
Get PDFInfrastructure and public service disparities persist globally, with many communities lacking reliable access to essential services like water, sanitation, and energy. Humanitarian engineering (HE) programs aim to address these disparities by training engineers to implement sustainable and equitable solutions. However, as the field undergoes a fundamental reckoning with its colonial legacy and impact, there is limited understanding of how these programs develop students' capacity to create meaningful social change.
Through four interconnected studies, this dissertation progressively examines key aspects of students' development in humanitarian engineering education. The first study employs Social Cognitive Career Theory to investigate how students question and reassess their equity-focused career aspirations during graduate school. This foundational analysis reveals patterns in how learning experiences shape students' career confidence and outcome expectations, providing crucial insights for supporting professional development in the field. Building on these findings about students' growing awareness of systemic barriers, the second study utilizes the Transformational Resistance Framework to analyze educational practices that inhibit or facilitate students' capacity to identify and resist oppressive structures in HE. This examination illuminates how learning environments can nurture or stifle students' development into agents of social change. Given the importance of self-efficacy in sustaining social justice work, the third study then applies the Social Justice Self-Efficacy construct to track how students develop confidence across multiple domains of activism - personal, interpersonal, community, and institutional. This longitudinal analysis reveals distinct phases in students' development of social justice capabilities, informing how programs can sequence and structure learning experiences. Throughout this investigation, the perspectives of students from low and middle-income countries emerged as crucial for enriching social justice development in HE programs. This led to the fourth study's use of Yosso's Community Cultural Wealth framework to examine how to create supportive learning environments for these students.
The findings advance theoretical understanding of how career expectations, resistance behaviors, and social justice capabilities develop in HE education while providing practical recommendations for nurturing students' capacity to create change. These insights can help programs prepare a diverse generation of humanitarian engineers equipped to address global infrastructure disparities through sustainable and equitable approaches. </p
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