Dissertation Defense Announcements

This dissertation seeks to improve the budget allocation process for economic mobility policy portfolios by leveraging multi-objective optimization as a decision support tool, accounting for population, political, social, and budgetary constraints. Economic mobility is measured as the difference in income between Black and White populations, known as the racial wealth gap. First, I run regression, mediation, and moderated mediation analyses to understand the impact of local authority, consolidation, local partisanship, unified government, and racial demographics on aid, budget expenditures and economic mobility. I then propose a novel application of multi-objective optimization1 to identify optimal mixes aimed at increasing economic mobility in urban cities. In doing so, I seek to improve decision support tools available to local urban governments. My work intends to enable local urban governments to leverage multi-objective optimization to guide their decisions regarding policy selection and budget allocation. Better informed policy processes lead to a better mix of policies, which allows for more holistic solutions with greater societal returns. This not only improves outcomes for residents; it also recovers waste in the governmental process, increasing effectiveness and efficiency.

Beta-lactamase proteins are major contributors to antibiotic resistance, rendering beta-lactam antibiotics ineffective against bacterial infections. The emergence of novel beta-lactamases with expanded substrate specificity poses a global health threat. This study utilizes computational techniques to investigate the mechanisms by which beta-lactamases expand their substrate specificity, enabling bacteria to resist new antibiotics. By exploring the relationship between protein dynamics and function, the impact of enzyme motion on substrate specificity is elucidated.
Molecular dynamics simulations are conducted and analyzed to identify the functional dynamics involved in substrate recognition in beta-lactamase. Dynamic signatures are identified using a novel approach called Supervised Projective Learning with Orthogonal Completeness (SPLOC). Increased flexibility in loops neighboring the enzyme's active site facilitates optimal interactions with different antibiotics through local conformational flexibility. Notably, dynamic signatures differ between protein-antibiotic systems, highlighting the complexity of antibiotic binding mechanisms. These dynamic signatures are demonstrated as viable predictors of antibiotic resistance in beta-lactamase enzymes.
A proof-of-concept is presented for designing de-novo peptides that target these regions, offering a potential new class of beta-lactamase inhibitors capable of hindering the motions necessary for substrate recognition. This approach presents a promising strategy for controlling beta-lactamase-mediated antibiotic resistance.

The increased reliance on Big Data Analytics (BDA) in society, politics, policy, and industry has catalyzed conversations related to the need for promoting ethical reasoning and decision-making in the mathematical sciences. While the majority of professional data scientists today come from privileged positions in society, those processed by the decisions made using data science are more often members of one or more marginalized social groups, translating into disproportionately negative outcomes for these individuals in society. Thus, it is argued that future citizens must develop an ethical mathematics consciousness (EMC) that human beings do mathematics; thus, there are potential ethical dilemmas and implications of mathematical work which may affect entities at the individual, group, societal, and/or environmental level. Drawing from this conjecture, the purpose of this Design-based research study was to develop a local instruction theory and materials that promote students’ ethical mathematics consciousness in a high school Ethical Data Science (EDS) course grounded in a feminist, relational ethic of caring and social response-ability. Outputs include the identification of design heuristics, including the task structures, participation structures, and discursive moves that supported students' development of EMC and equitable participation in classroom activities, an initial curriculum for the EDS course, and a student-use protocol and corresponding analytic framework for making critically conscious ethical decisions in data science.

In contemporary society, individuals with substance abuse histories face a multitude of challenges that extend far beyond the physical and psychological effects of addiction. As they embark on the path of recovery and strive for reintegration into society, they are confronted with an additional formidable barrier: the pervasive stigma and discrimination that persistently accompany their past struggles. This dissertation seeks to illuminate the profound impact of stigma and discrimination on individuals with substance abuse histories, exploring the underlying factors that perpetuate these harmful attitudes, and proposing potential strategies to alleviate their burden. Comprised of three interconnected papers, this research analyzes trust dynamics, stigma, and social support towards this population, offering valuable insights for combating stigma and fostering a more inclusive and compassionate society.

The first paper focuses on the power of positive information to counteract negative stereotypes and enhance trust in everyday interactions involving individuals with substance abuse histories. By examining the ways in which positive information can mitigate stigmatizing perceptions, this paper uncovers strategies to promote understanding and empathy in social encounters, paving the way for more meaningful connections and reduced discrimination.
Moving forward, the second paper explores participants' perceptions of trust and trustworthiness when engaging with partners who possess varying substance abuse histories in a trust game. By investigating how participants' knowledge of their partners' backgrounds influences expectations of reciprocity and trustworthiness, this paper unravels the complex dynamics that shape interpersonal relationships. The findings shed light on the potential for shifting perceptions and dismantling biases, ultimately fostering an environment where trust can flourish. Lastly, the third paper investigates the social and relational factors that influence cooperation and support for individuals with substance abuse histories within familial and friendship networks. By identifying the barriers that hinder cooperation and providing recommendations for creating supportive environments, this paper aims to strengthen social support networks and facilitate a more compassionate and inclusive community for individuals in recovery.

Collectively, these three papers contribute to the broader goal of combating stigma, building trust, and fostering cooperation towards individuals with substance abuse histories. The findings underscore the pivotal role of positive information, perceptions of warmth and trustworthiness, and the significance of individual attitudes and social support networks in reducing stigma and cultivating an environment that embraces recovery. By revealing the complexities of stigma and discrimination, this dissertation aspires to inform policies, interventions, and societal attitudes that empower individuals with substance abuse histories to thrive and reintegrate into society with dignity and respect.