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“healing attempt”: a brief, digital music-based mindfulness intervention for Black Americans with elevated race-based anxiety
No full textBackground: Race-based anxiety is a significant health issue for the Black community.
Although mindfulness interventions have demonstrated efficacy for alleviating anxiety, three
central barriers prevent Black Americans from accessing existing mindfulness treatments: high
costs, excessive time commitments, and limited cultural relevance. There is a need for novel
mindfulness interventions for the Black community that can overcome these barriers.
Method: This dissertation used three online multiple-baseline studies to examine the efficacy,
feasibility, and acceptability of a novel digital music-based mindfulness intervention called
“healing attempt” for middle-to-low income Black Americans with elevated race-based anxiety.
The intervention features contributions from Lama Rod Owens (world-renowned meditation
teacher and L.A. Times best-selling author) and Terry Edmonds (former chief speechwriter for
President Bill Clinton). All study visits were conducted on Zoom. I assessed effects of the
intervention on state anxiety (i.e., momentary anxiety), mindfulness, and self-compassion.
Additionally, I also assessed the feasibility and acceptability of “healing attempt” using study
specific scales.
Results: Across all three studies, the intervention led to significant decreases in state anxiety (All
Studies) as well as significant increases in mindfulness and self-compassion (Studies 2 & 3). The
intervention also received high feasibility scores across virtually all major metrics (i.e., the
average likelihood of recommending the intervention was 93 out of 100 across the three studies).
Conclusion: These studies offer preliminary evidence that a digital, music-based mindfulness
intervention can decrease race-based anxiety in Black Americans. Future research should test
whether such interventions elicit lasting changes in race-based anxiety, test mechanisms of
change, and explore the effectiveness of these interventions in real-world contexts.Psycholog
Self-Knowledge in Classical Sanskrit Philosophy
No full textIn this dissertation, I address the epistemology and metaphysics of self-knowledge in Classical Sanskrit philosophy. On the epistemology of self-knowledge, I address a debate between the 6th century philosophers Dharmakīrti and Kumārila on the epistemic sources (pramāṇas) of self-knowledge. On the metaphysics of self-knowledge, I address a debate between the 4th century philosopher Vasubandhu and his Buddhist and Brahmanical interlocutors on the possibility of self-knowledge within the framework of Buddhist “no-self” metaphysics. The dissertation shows that these two seemingly distinct debates can be united under the same interpretive lens because they converge on one key idea: that any adequate account of self-knowledge must not entail that (i) subjects can self-ascribe as many experiences as there are objects and (ii) self-ascribe experiences that are not in fact their own.Philosoph
Design of Peptide-Based Protein Degraders via Contrastive Deep Learning
No full textMost disease-associated proteins are considered “undruggable” by standard small molecule-based pharmaceutical approaches. Ubiquibodies are a promising experimental therapeutic intervention that can effectively treat so-called undruggable targets by hijacking the natural ubiquitin-proteasome pathway.
In this work, we develop a sequenced-based machine learning model to predict protein-peptide binding for the purpose of designing ubiquibodies. By leveraging known experimental
binding proteins as scaffolds, we create a streamlined inference pipeline, termed Cut&CLIP, that
efficiently selects ubiquibody peptide candidates for downstream screening. Unlike existing computational methods, this approach is applicable to the vast majority of human proteins, including disordered and unstable targets. Finally, we test ubiquibodies generated by the Cut\&CLIP pipeline experimentally and demonstrate robust intracellular degradation in human cells of therapeutic targets for pancreatic cancer, COVID-19, Ewing sarcoma, and fatty liver disease, motivating further development of the technology for clinical
translation.Applied Mathematic
Bioethics Crafted by Artificial Intelligence: Review of Organ Transplantation Decision-Making Using AI
No full textArtificial intelligence is increasingly used in healthcare to improve efficiency, consistency, and predictive accuracy. This paper critically examines the ethical and social implications of bias in artificial intelligence systems, particularly in the context of liver transplantation and medical imaging. While artificial intelligence is often promoted as a neutral and objective tool, its outputs are shaped by the data and design choices that inform its development. The paper analyzes how algorithmic bias can emerge from nonrepresentative datasets and design decisions, leading to unequal outcomes for marginalized populations. In liver allocation, artificial intelligence models may reinforce existing disparities by deprioritizing patients based on behavioral or cultural factors. In medical imaging, underdiagnosis and false positives disproportionately affect underserved communities due to limited representation in training data. These findings highlight the need for ethical oversight, inclusive data practices, and interdisciplinary collaboration to ensure that artificial intelligence supports equitable healthcare outcomes. The paper argues that artificial intelligence must be developed and deployed with a solid foundation in bioethical principles such as autonomy, beneficence, nonmaleficence, and justice.Extension Studie
Targeting mTORC1 in the neurons to promote healthy aging in C. elegans
No full textOne of the goals of aging research is to develop drugs and interventions that can move out of the lab and into human health care to promote longer and healthier lives. Currently, many of the interventions that are the most successful at extending lifespan also have serious trade-offs that limit their translational potential. For example, inhibition of the metabolic pathway mTORC1 (mechanistic target of rapamycin complex 1) extends lifespan in many organisms but also results in impaired growth and development. In my thesis research, I show that neuron-specific degradation of RAGA-1, an upstream activator of mTORC1, robustly extends the lifespan of C. elegans without causing the impaired reproduction and growth associated with whole-body mTORC1 inhibition. Neuronal degradation of RAGA-1 upregulates genes involved in stress response and fluorescent imaging revealed that the superoxide dismutase SOD-3 was upregulated in the intestine, demonstrating cell nonautonomous effects of neuron-specific mTORC1 modulation. Knockdown of various genes involved in neurotransmitter biosynthesis, neurotransmitter release, and neuropeptide released did not suppress the longevity mediated by neuronal RAGA-1 degradation, leaving open questions regarding the signaling mechanisms between the neurons and peripheral tissues. Although further research is required to strengthen our knowledge regarding the mechanisms by which neuronal mTORC1 inhibition regulate lifespan, the research presented in this thesis highlights tissue-specific targeting of metabolic pathways as a promising strategy to promote healthy aging.Biological Sciences in Public Healt
Fulfillment for Whom: Investigations of Workplace Social Capital, Psychological Distress, Work-Life Wellbeing, and Injuries in U.S. Fulfillment Centers
No full textWorkplaces can improve mental health and wellbeing by reducing job stressors and enhancing job resources, such as schedule control and social support. There is limited evidence on the role of job resources in fulfillment center work – a growing and evolving workforce. To address this gap, this dissertation investigates the relationships between workplace social capital – a relational job resource – and worker mental health and wellbeing, including psychological distress, turnover, and work-life wellbeing among U.S. fulfillment center workers. Additionally, this dissertation assesses the impact of a worker voice intervention on reported workplace injuries in U.S. fulfillment centers.
This dissertation relies on data from the Fulfillment Center Intervention Study, a study conducted in an e-commerce firm with fulfillment centers across the United States. In chapters 1 and 2, we use observational data from worker surveys and administrative data on job entries and exits. In chapter 3, we leverage the study’s cluster-randomized intervention design to assess the impact of the worker voice intervention on rates of reported injuries at the fulfillment center level using administrative records of injuries.
Chapter 1 investigates the relationships between both individual and supervisor group-level workplace social capital and psychological distress, turnover intent, and actual turnover. Using an open cohort design of all respondents across the 12-month study period, we find that higher individual workplace social capital is cross-sectionally associated with lower psychological distress, lower turnover intent, and lower odds of actual turnover at 3 months across all three survey waves. For group workplace social capital, we find that higher levels are associated with lower levels of psychological distress and turnover intent. In a longitudinal sample with repeat respondents from baseline and six-month follow-up waves, we find that an increase in individual workplace social capital is associated with a decrease in psychological distress and turnover intent. We also find that a within-person increase in group workplace social capital is associated with a decrease in turnover intent and more weakly, psychological distress. However, co-adjusting for both individual and group workplace social capital suggests that, in this sample, associations with group workplace social capital are weaker and no longer significant in the presence of individual workplace social capital.
In chapter 2, we use the 3-wave panel sample with repeated observations at baseline, follow-up 1 (6 months), and follow-up 2 (12 months) waves to assess the direct and indirect relationships between baseline workplace social capital and work-life wellbeing at 12 months of follow-up through perceived schedule control at 6 months of follow-up. We find that both higher workplace social capital and higher perceived schedule control are associated with higher work-life wellbeing. We also find that some of the relationship between workplace social capital and work-life wellbeing is mediated by enhanced perceived schedule control. We find that this mediated relationship is moderated by gender with a stronger association observed among women versus men.
Finally, in chapter 3, we evaluate the impact of a cluster-randomized worker voice intervention at the fulfillment center building level (N = 16) on rates of reported injuries. We find that overall, the intervention group had higher rates of reported injuries in the post-intervention period. To better understand whether this increase is due to higher injury incidence or less underreporting of injuries, we examined the intervention effect by type of injury – i.e. more severe (designated as workers’ compensation and lost time claims) versus less severe (designated as medical-only claims or record-only) injuries – since less severe injuries are more likely to be underreported. We find that the intervention had no impact on more severe injuries but was associated with higher rates of less severe injuries, suggesting that the worker voice intervention may have empowered workers to report injuries that may have been otherwise underreported. Given the small sample size and low numbers of injuries per site, these observed associations are sensitive to the inclusion or exclusion of specific sites and should be treated with caution.
Findings across all three chapters suggest that it is important to preserve and enhance the quality and availability of relational job resources and opportunities for connection and cooperation in fulfillment center work, particularly as incoming technologies reshape the workplace social environment.Population Health Science
Accuracy of Infant Clinical Signs to Predict Young Infant Mortality
No full textABSTRACT 1: Accuracy of Tachypnea to Predict Mortality in Young Infants: A Systematic Review and Meta-analysis
Context: Tachypnea in young infants is commonly used in clinical assessment to identify high-risk infants, however the optimal respiratory rate threshold is unclear.
Objective: To systematically review the evidence on the accuracy of different thresholds of tachypnea to predict mortality in infants 0-59 days.
Data Sources: MEDLINE, Embase, CINAHL, Global Index Medicus, and CENTRAL.
Study Selection: Studies reporting the accuracy of tachypnea (≥60, ≥70, or ≥80 breaths per minute (bpm)) to predict mortality in infants 0-59 days.
Data Extraction: We followed Cochrane methods for study screening, data extraction, and quality assessment using the Newcastle-Ottawa and QUAPAS scales.
Results: Of 7641 studies identified, 8 were included. Tachypnea with threshold of ≥60 bpm had an overall sensitivity of 57% (95% CI: 23%-86%) and specificity of 54% (95% CI: 28%-79%) for predicting future mortality in infants by 59 days of age (6 studies, N = 3819 infants). The pooled odds ratio for the association between tachypnea ≥60 bpm and mortality was 2.00 (95% CI: 1.39-2.87, 7 studies, N = 7122 infants). Tachypnea ≥70 bpm had a 4.6-fold higher odds of mortality (95% CI: 1.60-13.00, 1 study, N = 6924 infants). There was limited data on other thresholds and timing of mortality (early versus late neonatal periods).
Limitations: Heterogeneity and the low number of studies limited the evidence.
Conclusions: Tachypnea was associated with significantly higher odds of mortality in young infants. Overall sensitivity and specificity were low. Further research is needed to determine the optimal threshold and the accuracy for different postnatal ages.
ABSTRACT 2: Accuracy of Infant Clinical Signs to Predict Neonatal Mortality in Rural Bangladesh
Background: Clinical signs provide early warning of illness in neonates at high risk of dying in community settings in low- and middle- income countries (LMICs). There is limited validation of current clinical signs and/or algorithms to predict neonatal mortality in LMICs.
Objectives: 1) To examine the diagnostic accuracy of existing clinical sign algorithms to predict neonatal mortality, and 2) To determine the association of individual clinical signs with neonatal mortality and their diagnostic accuracy in predicting neonatal death.
Methods: We conducted a secondary analysis of a birth cohort in Sylhet, Bangladesh (NCT01572532). Of 7788 live births, 6251 newborns had a clinical examination during a community health worker home visit within 3 days of birth, and 5289 were followed up until 28 days of age with available vital status. We validated existing newborn clinical sign algorithms identified from our prior systematic review (Shafiq 2024), including four Integrated Management of Childhood Illness (IMCI)-like checklist algorithms and the Score of Essential Neonatal Symptoms and Signs (SENSS). We also estimated the odds ratios (ORs) for neonatal mortality associated with individual clinical signs exploring optimal thresholds using univariable logistic regression; and calculated sensitivity and specificity of individual clinical signs for identifying neonatal death.
Results: The WHO Young Infant Study 7-sign modification Z checklist algorithm had the sensitivity of 66.7% (95% CI: 56.6% to 75.7%) and specificity of 68.6% (95% CI: 67.3% to 69.9%) for predicting future mortality. The SENSS algorithm had an Area Under the Curve (AUC) of 75.9% (95% CI: 69.9% to 82.0%), calibration intercept of -1.21 (95% CI: -1.69 to -0.72), and calibration slope of 0.94 (95% CI: 0.78 to 1.11). Among individual clinical signs at birth, fast breathing ≥80 breaths per minute (bpm), fever ≥38.5˚C, and hypothermia .5˚C showed the strongest association with neonatal death, with ORs were 44.4 (95% CI: 13.8 to 142.6) for fast breathing ≥80 bpm, 39.3 (95% CI: 3.5 to 441.3) for fever ≥38.5˚C, and 30.3 (95% CI: 17.1 to 53.9) for hypothermia .5˚C.
Conclusions: Four IMCI-like checklist algorithms had low sensitivity and high specificity to predict neonatal mortality. The Score of Essential Neonatal Symptoms and Signs (SENSS) algorithm reported fair discrimination to identifying neonates at high risk of dying. Further research is needed to optimize clinical sign algorithms to identify high-risk infants in different age groups and settings.Graduate Educatio
Physics, Information and Inference: High-Dimensional Models under Structured Dependencies
No full textThis thesis develops theory and methodology for high-dimensional models that exhibit complex global dependencies. It examines several fundamental problems in statistical physics, compressed sensing, and statistical inference using rotationally invariant random matrix ensembles—frameworks that more accurately capture global dependencies than traditional i.i.d. assumptions.
The work is organized around three main topics:
(i) an in-depth study of the classical Sherrington–Kirkpatrick model under rotationally invariant couplings, including a rigorous proof of the fundamental Thouless–Anderson–Palmer (TAP) equations for low-dimensional marginals of the high-dimensional Gibbs measure;
(ii) the derivation of single-letter formulas characterizing key information-theoretic quantities in compressed sensing with right-rotationally invariant sensing matrices; and
(iii) the development of data-driven corrections to one-step debiasing regularized estimators that model complex global dependencies in the covariates via rotationally invariant matrices.Statistic
Imagined futures of housing policy in Botswana - unveiling spatial narratives through a phenomenological-critical-postcolonial lens
No full textThis mixed methods study examines the citizen’s role in the production of space in the peripheral urban spaces of post-colonial Botswana, aiming to shape collective imagined futures for housing policy. Current research is silent on the citizen’s lived experience in policy formulation. At most, discourse on policy formulation in Botswana analyzes the influence of policy on spatial representations or the socio-economic outcomes of policy. There is a disconcerting silence on the citizen’s role, engagement, and agency in the production of space. In response to this silence, this research embraces African epistemology as a decolonizing approach, recognizing indigenous knowledge systems. This methodology responds to the call for scholars studying African phenomena to dismantle the barriers that obscure local perspectives.
Using an exploratory sequential design, the study identified key variables from citizens’ lived experiences regarding housing and translated qualitative findings into a game. The study sample comprised participants from the peri-urban villages of Mogoditshane, Oodi, and Tlokweng, located at the periphery of Botswana’s capital city, Gaborone. The research methodology was structured in three phases: an initial phase involved narrative interviews with 31 participants, followed by the iterative design of a cooperative strategy game called MOOT City Game, and concluded by testing the game on a sample of 102 participants. An integrated interpretation approach contextualized the qualitative data and determined the nature of the game, while the quantitative data collected during the game were analyzed in relation to the qualitative findings.
The first key finding of the study was its essential invariant structure: the interconnectedness of community through collaboration and cooperation in housing production was a consistent lived experience for the participants. Additionally, the study revealed a significant relationship between collaboration and imagination: participants who collaborated during the game reported higher levels of satisfaction with the game’s outcome than those who did not. Furthermore, male participants who collaborated in a game that featured Molao, or the leader, expressed higher satisfaction with the game’s outcome than their female counterparts. Ultimately, the study found that participants’ agency flourished in an environment that fostered collective imagination rather than individualistic imagination.
This research underscores the value of contextualized local knowledge in crafting innovative solutions for democratizing policy formulation and advancing decolonization efforts in epistemology. Furthermore, the methodology contributes to the literature by linking gamification theory, serious games, and practical applications beyond participant engagement and motivation. The combined approach of the methodology and MOOT City Game serves as a valuable urban planning tool for policymakers, an empowering mechanism for citizens, a new way of thinking about urban planning as collaborative puzzle-solving., and an opportunity to build community beyond formal structures. Future research could involve testing the game in diverse cultural contexts and investigating the potential of the methodology to produce localized urban technologies.
Keywords: Gamification; Serious Game; Imagination; Citizen Agency; Urban Planning Policy; Urban Planning Innovation; Social Production of Space; Postcolonial UrbanizationAdvanced Studies Progra
Learning and Evaluating Algorithmic Policies: Methodology and Applications
No full textAlgorithmic decisions and policies are increasingly used in today's world, including online advertising, public policy, and medicine. While many methods have been developed to efficiently learn a policy from data, there are also practical demands for statistical guarantees on the learned policy, especially in high stake decision making problems. My research aims to develop new methods that can learn a policy and evaluates the learned policy simultaneously, both in Bayesian way (Chapter 1) and frequentist way (Chapter 2, 3).
Chapter 1: Bayesian Safe Policy Learning with Risk Constraints Policy learning with safety guarantee is essential in many high-stake decision making problems. In this chapter, we introduce a Bayesian policy learning framework that maximizes posterior expected utility while controls the Posterior Average Conditional Risk (ACRisk), ensuring that a newly learned policy does not lead to worse outcomes than the existing baseline policy up to a tolerance level. We also demonstrate it by applying it to learn a new policy for assigning military security score during the Vietnam War using historical data.
Chapter 2: The Cram Method for Simultaneous Learning and Evaluation Evaluating a learned policy after training is a critical step before deploying it in practice. To achieve this, sample splitting methods are statistically inefficient, while the resampling based methods are computationally expensive and evaluates an average of multiple policies rather than an exact policy. In this Chapter, we introduce the cram method, a general framework for simultaneous policy learning and evaluation applicable to general policy learning algorithms. We established theoretical guarantees for the crammed evaluation estimator and demonstrate its effectiveness in both simulation studies and a practical application.
Chapter 3: Cramming Contextual Bandits for On-Policy Evaluation As adaptive learning algorithms are increasingly used in practice, evaluating the learned policy is essential for understanding its performance. In this chapter, we extend the cram method to the evaluation of policies learned by multi-armed contextual bandit algorithms, providing an on-policy alternative to off-policy evaluation methods with more efficiency. We prove theoretical guarantees for the crammed estimator under a stability condition, and validate its effectiveness for linear bandit algorithms, including -greedy, Thompson Sampling, and Upper Confidence Bound. Empirical results confirm that cramming significantly reduces evaluation error compared to off-policy evaluation methods.Statistic