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The Motor of Innovation: Applying the Learnings from a Networked Improvement Community (NIC) to a Software as a Service (SaaS) Organization's Thought Leadership Strategy
No full textLeadership and Learning in Organizations capstone project“GlobalWorksTech” is a business-to-business software as a service (SaaS) technology company whose mission is to support learning through its various educational platforms. The organization currently lacks a clearly defined, organization-wide vision and operational definition for “thought leadership” (TL). This lack of vision and definition is resulting in fragmented understandings and inconsistent executions of TL activities across departments and divisions. Thought leadership as a motor for innovation remains underutilized.
This quality improvement project engaged senior, divisional leaders in a networked improvement community in service of answering the following questions:
Q1: How do stakeholders characterize the nature and/or quality of thought leadership?
Q2: In what ways, if any, does user feedback inform the organization’s structure or actions of its thought leadership design and practices?
Using our conceptual framework from our literature review on the construct of thought leadership, organizational improvement, and equity in thought leadership, we deductively coded then thematically analyzed our findings based on the theoretical lenses of organizational behavior and organizational design. After coding and analyzing session recordings in addition to survey responses and 1:1 interviews, we found the following:
Finding 1 (PQ1): GlobalWorksTech Steering Committee members mainly characterize TL in terms of the individual functions of thought leaders within the organization.
Finding 2 (PQ1): GlobalWorksTech Steering Committee members frequently connected micro-level operations of TL to macro-level implications.
Finding 3 (PQ1): GlobalWorksTech Steering Committee Members construct and communicate understandings of TL through symbolic and structural organizational behavioral framing.
Finding 4 (PQ1): Although GlobalWorksTech’s Steering Committee members showcase their understanding of TL through symbolic and structural organizational behavioral frames, the lack of a clear organizational design of TL in its current state creates tension in an otherwise highly bureaucratic environment.
Finding 5 (PQ2): GlobalWorksTech Steering Committee members hold various perceptions of who “users” of TL are.
Finding 6 (PQ2): External and internal stakeholders influence TL activities.
Finding 7 (PQ2): User feedback-informed TL varies across divisions.
Taken together, the findings highlight a critical gap between how thought leadership is enacted and how it is structurally supported at GlobalWorksTech. Therefore, we recommend that the organization establish a vision, norms, and definition explicitly for thought leadership, continue the networked improvement community, create additional spaces in which thought leaders can come together across the organization, and task the networked improvement community with developing and managing a “Thought Leadership Portfolio.
Constructing a Secure and Autonomous Infrastructure for Smart Cities
No full textThe rapid evolution of urban environments, propelled by advances in the Internet of Things (IoT) and autonomous vehicle technologies, has ushered in a new era of smart city infrastructure. This dissertation investigates four critical components that together form the backbone of a secure, efficient, and resilient urban digital ecosystem.
First, this dissertation develops a flexible access control technique for large-scale public IoT services to manage vast, interconnected networks securely, ensuring data integrity and user privacy.
Second, this dissertation proposes an AI-enabled efficient traffic scheduling for autonomous vehicles, which uses real-time decision-making to optimize routes, reduce congestion, and improve urban mobility.
Third, to mitigate the risks of data exposure in centralized systems, this work extends to a privacy-preserving model that trains locally while sharing statistical traffic data for global optimization, ensuring secure and efficient decentralized traffic management.
Finally, this dissertation proposes a neighborhood watch mechanism for attack detection and evacuation, which enhances intersection security by monitoring data for anomalies and triggering safety protocols to prevent cyber threats and system failures. These integrated solutions address critical challenges in modern urban environments.
This dissertation is organized around the synergistic integration of these research areas. By establishing a secure IoT framework, enhancing autonomous traffic scheduling through AI, provide scalable and privacy-preserving traffic optimization, and implementing robust intersection safety measures, this dissertation provides a holistic solution to the challenges facing modern smart cities and illustrates how a multi-faceted approach can drive forward the next generation of urban infrastructure
“Because, at the end of the day, it's their school:” Centering Student Voice in Restorative Practices
No full textThis dissertation centers student voices in Restorative Practices (RP) through three interrelated papers. The first paper is a systematic review analyzing how RP is conceptualized, implemented, and evaluated in United States (U.S.) schools, highlighting inconsistency in conceptualizations and implementation of RP, and evaluation methods that often prioritize adult perspectives over student voices. Building off of these findings, the second paper presents a qualitative study centering student perspectives on their experiences with RP at an urban-emergent high school, using the Social Discipline Window as a framework. Findings reveal distinct experiences with student-teacher relationships and the need for an “Emergent Social Discipline Window” to more closely represent student experiences by accounting for harm. The third paper examines student-teacher collaboration in addressing classroom challenges through two interrelated studies. Specifically, Paper Three explores how a Youth Participatory Action Research (YPAR) study with middle school students and their teacher identified classroom problems and brainstormed solutions, while also investigating the impact of combining YPAR with RP. Findings suggest that while the YPAR processes facilitated dialogue and some elements of problem-posing education, it did not foster critical consciousness or praxis, but incorporating RP encouraged respectful engagement and discussion, even on sensitive topics. Collectively, these dissertation manuscripts underscore the need for RP implementation that is more clearly defined, student-centered, and critically engaged with issues of power and systemic inequities. This dissertation calls for reimagining RP as a transformative framework that prioritizes student agency and structural change in K-12 schools
Essays in Health Economics: Maternal and Infant Health
No full textThis dissertation consists of three papers that examine the impact of policies and shocks to healthcare on the take-up of care and the health of mothers, infants, and reproductive-aged women. The first chapter studies the effect of increasing access to healthcare providers on birthing location decisions and health. Home births in the US have increased twofold since the early 2000s, yet little is known about the policies that have driven the increase. In this chapter, I use natality data from 1989 to 2021 to estimate relative changes in the prevalence of home birth and subsequent health outcomes before and after state legislation licensing non-nurse midwives. Staggered difference-in-differences estimates indicate a 20-30 percent increase in home births when states increase provider choice through non-nursing midwifery licensing. I find the change in access is most salient for college-educated women, low-risk pregnancies, and those who pay out of pocket. Overall, I find little evidence of an effect on infant or maternal health measures. The second chapter studies how increasing access to contraceptives for low-income women affects childbearing. We focus on the setting of the Virginia Contraceptive Access Initiative (CAI), which provided funding for family planning clinics to offer no-cost long-acting reversible contraceptives (LARCs) and other contraceptive devices to low-income women in Virginia from 2018 to 2023. We show that when more Virginia clinics had funding to provide free contraceptive devices, LARC take-up increased. Then, using difference-in-differences, we show that implementing the CAI in Virginia, a state with existing low consumer-facing prices and high baseline LARC take-up rates, reduced birth rates by approximately 2.2 percent, with larger effects for women over the age of 25. In the third chapter of my dissertation, I estimate the impact of changes to care quality during pregnancy on maternal and infant health. Specifically, I study state implementation of Perinatal Quality Collaboratives (PQCs) that aim to improve health through interventions at hospitals within the state. I provide evidence that PQCs improve the quality of care received by non-Hispanic Black mothers, evident by decreased rates of eclampsia and ICU admissions
Navigating Rough Waters: Toward a Generative Ethics of Care in Child Advocacy and Ella J. Bakers’ Praxis of Social Change
No full textThis study situates contemporary child advocacy programming within a historical genealogy of Black women's leadership, focusing on Ella Baker as an instructive figure for future generations engaged in educational justice and Black childhood liberation. Black women have historically led efforts to protect and uplift Black children, often working outside traditional power structures to combat institutionalized violence and educational inequities. The dissertation examines the Children's Defense Fund (CDF) Freedom Schools as a continuation of Black child advocacy, rooted in Baker's legacy and praxis. By centering her philosophy of empowerment, grassroots leadership, and social change, this research explores how Baker's work provides strategies for strengthening child advocacy in CDF Freedom Schools and similar programs. These connections not only enrich our understanding of Baker's contributions but offer insights into programming that nurtures children's voices, critical consciousness, and intergenerational engagement.
Baker's humanist vision of democracy, honoring the dignity and creative capacities of all people, including children, infuses the CDF Freedom Schools' mission of cultivating agency, leadership, and civic responsibility. These schools serve as critical spaces of hope, affirming the strength within children while equipping them to challenge systemic oppression, despite structural barriers challenging their full potential. Amid state-level attacks on critical race theory and historical truth-telling, this study asserts the urgency of preserving Black women's ethical agency in safeguarding Black children. By engaging Baker's legacy within the CDF Ella Baker Child Policy Training Institute, this dissertation positions her as a contemporary mentor whose philosophy remains instructive for today's advocates.
Drawing from my experience with the CDF Freedom Schools program, this research advances a generalizable and coherent ethics of care framework for childhood advocacy ethics. Through genealogical method, I argue that an ethics of care rooted in public policy, empathetic listening, communicative action, and self and communal care provides a unifying foundation for Freedom Schools pedagogy, leadership development, and advocacy, operationalizing Baker's and Edelman's visions into transformative actions that empower children and improve their life chances and wellbeing, recognizing both the possibilities and the limitations inherent in this work
A Unified Effect Size Index and Its Application to Improve Replicability in Brain-Behavior Association Studies
No full textReporting of effect sizes (ES), such as Cohen's d and odds ratios, alongside their confidence intervals, has gained attention for its ability to convey both the strength and precision of scientific findings simultaneously. However, existing ES indices are model-specific, presenting a challenge for researchers attempting to compare effect sizes across studies addressing similar questions but using different statistical models.
In the first chapter of my dissertation work, I first introduce a robust ES index (RESI) that is not conditional on statistical models to facilitate ES reporting within the cross-sectional study setting. However, the newly proposed ES index hasn’t solved the systematic differences in ESs between cross-sectional and longitudinal study designs yet, thereby complicating comparisons between the two. To resolve this, in the second chapter, I propose a new version of RESI tailored for longitudinal studies, which estimates ES as if the study were conducted cross-sectionally, thereby improving comparability across different study designs. The proposed ES index unifies ES reporting across studies using different models and/or different designs (i.e., cross-sectional or longitudinal), bridging a critical gap in ES communication.
Recently, several studies raised concerns about the low replicability of brain-behavior association studies and showed that thousands of study participants are required for good replicability. However, massive sample sizes are often infeasible in practice. In the last chapter of my dissertation work, I apply the proposed ES index and systematically investigate how we can leverage the modifiable study design features to improve the ESs (and therefore, the replicability) of brain-behavior association studies, using the large-scale data from the Lifespan Brain Chart Consortium. Based on strong empirical evidence and pragmatic statistical theory, concrete and actionable study design and analysis procedures are provided to the neuroscientist to help them improve the replicability of their studies, given their different research objectives and the nature of their target associations
Neural Markers of Cognitive Reappraisal Difficulties in the Detection of Adolescent Depression
No full textDepressed individuals tend to use maladaptive emotion regulation strategies more frequently than non-depressed individuals while using adaptive strategies like reappraisal less frequently. Objective neural markers of cognitive reappraisal could aid in detecting depression and future depression risk. Critically, findings from emotion regulation tasks demonstrate the late positive potential (LPP) component and relative alpha power are both modifiable by cognitive reappraisal efforts. The current study applied principal component analysis (PCA) to EEG data from 201 adolescents (aged 14-17) along with advanced machine learning techniques to clarify the neurophysiological substrates of reappraisal and whether these substrates can discriminate depressed from non-depressed youth concurrently and prospectively. Results revealed a significant group X condition interaction for a late frontal LPP component, such that never-depressed youth showed reappraisal-related LPP reductions while currently depressed youth did not. A pattern for event-related increases in alpha synchronization during reappraisal was observed, but no group differences emerged. Finally, machine learning analyses found the inclusion of both time and time-frequency components optimally discriminated between adolescents with a without a future depressive episode. The findings from this study advance our understanding of complex neurophysiological alterations during cognitive reappraisal and alterations in depressed adolescents’ ability to engage in effective reappraisal
Optimized federated-learning-based techniques for IoT in edge computing
No full textThe Internet of Things (IoT) generates vast amounts of data from billions of interconnected devices, creating unprecedented opportunities for machine learning applications. However, traditional centralized learning approaches face significant limitations in IoT environments, including privacy concerns, bandwidth constraints, and data sovereignty issues. Federated Learning (FL) emerges as a promising solution that enables collaborative model training across distributed edge devices while keeping data locally stored, thus preserving privacy and reducing communication overhead.
This work addresses four critical challenges in federated learning for IoT systems: (1) Communication Efficiency - reducing bandwidth requirements through optimized synchronization strategies in resource-constrained networks; (2) Data Heterogeneity and Multimodality - handling non-identically distributed (non-i.i.d.) data across devices and managing missing modalities in multimodal IoT environments; (3) System Heterogeneity - addressing computational resource variations and unpredictable client participation patterns; and (4) Lifelong Learning - managing concept drift in evolving data distributions and mitigating the straggler problem in dynamic IoT deployments.
The research focuses on optimizing FL strategies to maintain or improve performance under realistic constraints, moving beyond idealized assumptions to address the practical challenges of implementing federated learning in large-scale, heterogeneous IoT networks. By tackling these fundamental issues, this work aims to enable more efficient and robust federated learning systems that can effectively leverage the collective intelligence of distributed IoT devices while respecting privacy and resource constraints
Mechanisms of GPCR signal propagation: from rhodopsin activation to inhibition of synaptic vesicle fusion by Gβγ-SNARE interactions
No full textG-protein-coupled receptor (GPCR) signaling is involved in virtually all physiological processes in vertebrates and contributes to the progression of many diseases. Despite thorough investigation, numerous details regarding the molecular mechanisms of GPCR activation and effector modulation via G-proteins remain unclear. One example is the importance of bulk water in shaping the energy landscape of GPCR activation. To address this, we developed an experimental osmotic stress approach to investigate the relationship between receptor internal hydration and rhodopsin activation. The results indicate that an influx of 80-100 water molecules penetrate the rhodopsin interior upon formation of the active state. Furthermore, dehydration of the rhodopsin interior significantly reduced its affinity for transducin C-terminal peptides. On the basis of these results, we propose that rhodopsin wet/dry cycling is coupled to transducin binding and release.
Another aspect of GPCR signaling that is not well understood is the molecular mechanism by which G-protein βγ heterodimers (Gβγ) inhibit synaptic vesicle fusion via interactions with SNARE proteins. To examine this, we characterized the interaction between Gβγ and the ternary SNARE complex both structurally and biochemically. First, we mapped the binding sites on each human Gβ and Gγ isoform for tSNARE and identified the residues critical for the Gβ1γ2-tSNARE interaction using peptide arrays. Next, we discovered that Gβ1γ2 preferentially interacts with ternary SNARE in the partially zipped conformation, as opposed to the fully zipped conformation. To investigate the interaction further, we stabilized the Gβ1γ2-pre-fusion SNARE complex using crosslinking and structurally characterized the complex using single particle cryo-EM. Our preliminary cryo-EM density map suggests that the N-terminal coiled-coil of Gβγ interacts at the C-terminus of the SNARE complex. We next used Chai-1 and Rosetta docking to predict the structure of the Gβ1γ2 N-coiled-coil domain bound to the C-terminus of tSNARE. This structural prediction suggests that the N-termini of both Gβ1 and Gγ2 form an interface with the C-terminal helix of SNAP-25, and that Gγ2 inserts into the C-terminal SNARE helical bundle. These data provide a roadmap for future experiments to further elucidate the molecular details of the interface. Taken together, these studies elucidate novel aspects of GPCR signal propagation
Exploring Responsibility Attribution in Realistic Scenarios Involving Intelligent Agents
No full textAs artificial intelligence (AI) systems get increasingly involved in decision-making across industries, debates have emerged regarding responsibility attribution in scenarios with AI being the moral actor. This study contributes to the discussion by examining the influence of social perception on moral judgments to scenarios involving AI and other non-human entities, referred to as “agent”. Specifically, we investigate the impact of narrative framing, the human-agent distinction, and general attitudes toward AI on moral judgments and trust in AI-based decision-making, while emphasizing the mediating roles of anthropomorphic attributions and cognitive conflicts. Based on Jaeger’s (2020) anthropomorphism theory, we differentiate between near (surface-level, direct) and far (deep-level, inferred) attributes to analyze their independent impacts.
Participants reported general attitudes toward AI and baseline cognitive conflicts before being randomly assigned to a scenario involving an advisor from a healthcare platform providing incorrect medical advice, resulting in financial loss for a human user. The advisor was either an AI described in humanized narrative framing, an AI described in mechanical narrative framing, or a human advisor. Participants then assessed blame attribution, financial punishment, anthropomorphism attribution, trust in the advisor, and situational cognitive conflict. We initially conducted ANCOVA and mediation analyses collapsing the agent conditions (as no significant differences emerged), followed by an exploratory mediation analysis excluding the human condition and adding non-minimal far-attribution as a mediator.
Results indicated insignificant effect of narrative framing alone and significant effect of human-agent distinction on responsibility attribution and trust. Human advisors were associated with lower blame attributes and higher trust than agent advisors. Positive attitudes towards AI predicted lower advisor blames, while negative attitudes predicted reduced trust only in primary analyses. Induced cognitive conflict directly increased advisor trust. Lastly, non-minimal far-attribution was associated with less platform blame and higher trust in agent advisors. These findings support established literature in algorithm aversion and highlight the roles of deep anthropomorphism attributes, general attitudes, and cognitive conflicts in shaping moral judgments and trust with agents, providing insights for reliable and trustworthy AI practices