DECIPHERING THE CHEMICAL ABUNDANCES OF THE FIRST GALAXIES AT COSMIC DAWN WITH JWST

Astronomers have yet to discover the first generation of stars formed in the universe. These long theorized "Population III" stars would be pristine of heavy elements, or "metals", and likely supermassive, very different from stars we observe today. To find the first stars, we must push our observations as early as possible studying exceptionally small, low mass, metal-poor galaxies. In this thesis, I present an extensive study of MACS0647–JD, the brightest known galaxy within the first 500 million years of cosmic history, magnified by gravitational lensing and observed with cutting-edge JWST imaging and spectroscopy. JWST/NIRCam imaging resolves MACS0647–JD into two distinct components. Through analysis of their physical properties and star formation histories, I find that one clump is younger, while the other appears older, suggesting an early-stage galaxy assembly or merger. Using NIRSpec and MIRI spectroscopy, I measured a direct gas-phase metallicity of 13% solar and a carbon abundance of 60% solar, the first such measurements obtained in the first 500 million years. The elevated carbon abundance may be explained by enrichment from remnants of the first generation of stars. To identify even more primitive galaxies, I conducted a search for extremely metal-poor galaxy (EMPG) candidates using deep, pure-parallel JWST/NIRCam grism spectroscopy. I discovered seven EMPG candidates with metallicities below 2% solar. These galaxies appear to be undergoing starbursts, with high star formation rates (SFR~0.2-2 Msun/yr) and low stellar masses (log(M/Msun)~6.8-7.8). The continued search for distant galaxies, EMPGs, and first star candidates is ongoing, and will undoubtedly transform our understanding of galaxy formation and chemical evolution at cosmic dawn

SIMULATING FUNDAMENTAL FIELDS IN STRONG GRAVITY: ASTROPHYSICAL IMPLICATIONS OF ULTRALIGHT DARK MATTER

The nature of dark matter remains one of the biggest mysteries in modern-day physics. Ultralight dark matter models hypothesize that dark matter is composed of one or possibly several bosonic particles with their mass between approximately $10^{-24}\, \text{eV}$ and $1 \, \text{eV}$. In this thesis, we use numerical relativity to understand the behavior of ultralight dark matter under the influence of strong gravity in the nonlinear regime. We consider three astrophysical scenarios: black hole superradiance, gravitational Magnus effect, and polarized Proca stars. We first study the phenomenon of black hole superradiance, where bosonic particles can extract energy from spinning black holes. While ultralight dark matter can trigger black hole superradiance, we investigate a competing mechanism in which photons in a diffuse plasma gain an effective mass and thus can also trigger superradiance. We carry out relativistic simulations of a Proca field evolving on a Kerr background, with modifications to account for the spatially varying effective mass. We find that a constant asymptotic mass or a shell-like plasma structure is required for superradiant growth. Next, we study a rotating black hole moving at relativistic velocities through scalar dark matter. We simulate the system numerically and extract the Magnus force on the black hole perpendicular to its motion. We confirm that the force scales linearly with the dimensionless spin parameter $a/M$ of the black hole up to $a/M = 0.99$. We also reveal a significant nonlinear correction at relativistic speeds of the black hole up to 0.55 of the speed of light. Lastly, we study the ground state of a spatially localized massive vector field (Proca stars) as a model for ultralight dark matter. We include general relativistic effects and numerically investigate the stability of compact polarized Proca stars. We find that these initial conditions lead to stable configurations. However, they can collapse to black holes at sufficiently large initial compactness. Polarized Proca stars collapse at higher compactness compared to non-polarized states

Hyperparameter Optimization for Neural Machine Translation Systems

Machine translation, a sequence-to-sequence task, involves translating text from one language to another. Currently, transformer-based systems dominate the field of neural machine translation (NMT). To optimize these systems, various critical decisions regarding architecture design and training processes must be made—these decisions are the hyperparameters of the system. Typically, these hyperparameters are set before training begins and remain unchanged until convergence. Traditionally, they are tuned manually based on intuition, heuristics, previous studies, or default settings provided in open-source frameworks. This approach often leads to suboptimal exploration of the hyperparameter space, which can cause exaggerated performance differences and potentially misleading conclusions. Despite the proliferation of hyperparameter optimization (HPO) methods under the umbrella of Automated Machine Learning (AutoML), their effectiveness in NMT has not been thoroughly evaluated, primarily due to the significant computational demands of NMT models and their vast hyperparameter search spaces. This challenge is further complicated by the need to optimize multiple objectives simultaneously, such as translation accuracy and decoding speed. This thesis addresses these challenges by conducting a comprehensive study of HPO specifically within the context of NMT. First, we introduce a benchmark dataset employing a ``table-lookup" based benchmarking procedure, designed to promote reproducible research in HPO for NMT. Second, we propose a novel HPO algorithm using graph-based optimization, which flexibly incorporates prior knowledge about hyperparameters. Third, we develop a post-hoc interpretation framework to better understand the significance and interrelationships of individual hyperparameters. Fourth, we evaluate the efficacy of a multi-fidelity HPO method, successive halving, and propose best practices for its application in NMT and large language models. Finally, this work includes the creation of an HPO toolkit tailored for NMT research, designed to streamline the experimental process and allow researchers to concentrate on innovation instead of the mundane

INTERLEUKIN-2-DRIVEN MODULATION OF THE REGULATORY T CELL TRANSCRIPTOME AND EPIGENOME

The immune system plays a critical role in both health and disease. Regulatory T cells, including their development and maintenance, are a keep component of this role to ensure balance in homeostasis and an effective response in disease. In homeostasis, Tregs’ role in tolerance to self-antigens and by extension components of the intestinal microbiota help prevent autoimmunity. In diseases, including infection and cancer, Tregs regulate the immune response by cytokine-mediated suppression through the secretion of anti-inflammatory cytokines such as IL-10 and TGF-β. Tregs can also deprive immune effector cells such as T effector cells, NK cells and B cells of needed growth signal via the expression of high affinity CD25 and subsequent consumption of Interleukin-2 (IL-2), a key survival cytokine. Upon activation, several chromatin and gene expression changes occur in Tregs to ensure effective immune response. Here we investigated how temporal stimulation with IL-2 affect Treg chromatin landscape and gene expression to induce different cell states for response via single cell RNA and ATAC Sequencing. We uncovered heterogeneity in response in resting Tregs (rTregs) with less signal transduction through Stat5 and the expression of several genes implicated by key transcription factors including Batf. By understanding the transcriptional and epigenetic changes in Tregs, we can better harness their role as key therapeutical targets to maintain immune balance

Finding Their Footing at the Front of the Classroom: The Emerging Professional Identities of Pre-Service Teachers

Pre-service teachers’ resilience is dependent on the development of teacher self-efficacy as part of their emerging teacher identity. This process is complicated by teacher preparation programs that perpetuate systemic inequities and complicate the sociocultural identities of preservice teachers. The study in this dissertation used the Phenomenological Variant of Ecological Systems Theory (PVEST) as a theoretical framework to examine the influences of various social interactions and support systems that nurture a preservice teacher’s identity. The resulting conceptual framework explored how a sense of autonomy, competence, and relatedness within the profession leads to increased teacher self-efficacy, motivation, and persistence as an extension of teacher identity. The convergent parallel mixed-methods study sought to better understand the formation of teacher identity and self-efficacy as an interaction of the identities and experiences of preservice teachers. Additionally, the study examined how various systems embedded in a teacher education program at a small private university on the West Coast could foster adaptive strategies that strengthen preservice teachers’ resilience in the profession. Quantitative data were collected using the Teacher Identity Measurement Scale. Qualitative data were collected through interviews and storyline illustrations. Findings revealed that preservice teachers entered the program with strong motivations towards teaching and a clear teacher identity but initially struggled with low teacher self-efficacy. As they progressed through the program, their teacher self-efficacy increased, and their teacher identity and self-efficacy became more closely aligned. Mentor teachers emerged as the most significant source of confidence and support, offering encouragement and constructive feedback that communicated their belief in the preservice teachers’ abilities to be successful. Despite the difficulties of the program, the preservice teachers in the study developed adaptive coping mechanisms that included leaning into meaningful relationships with each other, focusing on their love for students, and developing a reflective stance towards their practice. The participants’ teacher self-efficacy grew through student teaching experiences, influenced by both their personal and professional identities. These findings suggest that teacher preparation programs must acknowledge the emotional journey of preservice teachers, encourage reflective practices, and provide robust support systems that promote teacher identity formation, self-efficacy development, and ultimately, resilience in the teaching profession

Socially Structured Activity-Time Allocation: Work, Family, and Person Spheres as an Integrated System in Daily Lives

The dissertation explores how social structures in the work and family spheres shape individuals’ activity-time allocation as a system, and how gender manifests in these behavioral patterns. Conceptually, I extend the classic sociological framework of the work-family divide by incorporating a third domain: the person sphere, which includes activities that serve personal well-being and development but are often treated as discretionary. By framing daily life as a socially structured system of interrelated work, family, and person spheres with interpersonal dependence through the family institution, this dissertation offers a more comprehensive understanding of: (1) how gendered work and family institutions reproduce inequality in the ostensibly discretionary person sphere, (2) how labor market and family inequalities interconnect through couple interdependence, and (3) how the three spheres interrelate in individuals’ daily decisions and behaviors. Across three empirical studies, the project bridges sociological theories of labor market, family, and gender inequality with quantitative and computational methods. It provides new insights into the complexities of socially structured, high-dimensional human decisions and behaviors. By uncovering how gendered work and family constraints extend into the person sphere, this dissertation contributes to understanding the stalled and uneven gender revolution, highlighting how institutional change in work and family remains incomplete when opportunities to personal well-being and development are unequally accessible. Additionally, it addresses empirical challenges by expanding analytical possibilities with time-use data, suggesting promising directions for future research on social inequality and the structure of daily lives

Role of s2U tRNA modification and ribosomal protein expression in antibiotic tolerance of Yersinia pseudotuberculosis

Antibiotic treatment of bacterial infections can fail for several reasons. Major focus centers on the problem of antibiotic resistance, which generally occurs when bacteria acquire genetic mutations that allow bacteria to grow in the presence of antibiotics. However, bacteria can also develop phenotypic changes in response to stress, typically involving a slowed growth response, that allow them to survive antibiotic treatment longer than susceptible bacteria. This phenomenon is known as antibiotic tolerance when these changes occur in the entire bacterial population or antibiotic persistence when these changes only occur in a subpopulation. Antibiotic tolerance and persistence have been postulated to contribute to relapsing infections and have been shown to directly enhance the development of antibiotic resistance, making it important to understand what phenotypic changes allow bacteria to survive antibiotic treatment. The following studies aim to understand what phenotypic changes in response to antibiotic stress and stressors in the host environment, are responsible for promoting bacterial survival. In response to doxycycline, we find that Yersinia pseudotuberculosis downregulates tusB, a gene involved in the 2-thiolation on the wobble position of tRNAs (s2U). Whereas tusB overexpression increases susceptibility to doxycycline, tusB deletion renders bacteria tolerant to ribosome and RNA polymerase-targeting antibiotics. Our results indicate that loss of s2U results in less efficient translation of ribosomal proteins. Lower ribosomal protein levels likely globally reduce the translational capacity of the cell, inducing slowed growth and antibiotic tolerance. In a separate study, downregulation of ribosomal proteins also seems to predict bacterial survival after antibiotic treatment. We find that expression of the type-III secretion system (T3SS) attenuates growth and induces tolerance to gentamicin. However, expression of T3SS is not the best indicator of antibiotic survival. Instead, expression of the 30S ribosomal protein S10 was a better predictor of survival wherein cells expressing lower levels of S10 preferentially survived antibiotic treatment. These studies highlight ribosomal protein expression as an indicator of metabolic activity and potentially as a major proponent that influences the survival of bacteria in response to antibiotic treatment

Efficacy Study of Filo in Jefferson County Schools

This quasi-experimental study evaluated the impact of the Filo tutoring program on math, ELA, and science achievement among Grades K-8 students in Jefferson County (AL) Schools (JCS). Using a student-level matched comparison design (N=1,089 treatment, 2,163 comparison), the study examined student performance on ACAP and i-Ready assessments. Results indicated directionally positive impacts on ACAP math, ELA, and science scores, with statistically significant gains in ACAP math observed for male and Black students—a notable outcome given the historical achievement gaps within the district. Although no significant effects emerged for i-Ready scores, a positive association was found between the number of Filo tutoring sessions and ACAP ELA performance

NEURAL CONTROL OF THE TONGUE IN THE COMMON MARMOSET

Flexible control of the tongue is critical across a wide range of human behaviors, including speech, mastication, respiration, and swallowing. Lingual movements exhibit dexterity, precision, and coordinated interactions with other oro-motor systems, making them an intriguing model for studying fine motor control. However, measuring tongue movements is challenging, and there is no established primate model for studying the neural basis of lingual control. Here, we present the common marmoset as the first nonhuman primate model for the study of goal-directed lingual movements across two neuroscientific domains: the neuroeconomics of lingual control and the neurophysiology of lingual control by the cerebellum. Marmosets are capable of remarkable dexterity of their tongue, a skill they use in the wild to feed through extraction of sap from small holes made in trees. In this work, we leverage this ability in a foraging task in which marmosets are trained to produce goal-directed licks for food reward, while we record electrophysiological activity from the cerebellum. In Chapter 2, we examine the effects of economic variables such as reward and effort on foraging-based decision-making and movement vigor of both saccades and licks. We found that when the acquisition of reward became effortful, the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor. This coordinated response suggests that decisions and actions are part of a single control policy that aims to maximize a variable relevant to fitness: the capture rate. In Chapter 3, we shift our focus to the cerebellum, examining the mechanism by which the principal cells of the cerebellar cortex, P-cells, contribute to control of the tongue. We identified lingual regions in lobule VI of the cerebellar vermis and recorded from P-cells as subjects performed goal-directed licks. We found that when P-cell simple spike activity was suppressed following a complex spike, licks exhibited endpoint hypermetria. Further, we found that P-cell simple spike population firing rate responses exquisitely tracked lick deceleration, scaling with lick kinematics. These results suggest that P-cells contribute to the endpoint accuracy of a lick through enabling timely deceleration of movement

TRPV4 MUTATIONS CAUSE BLOOD-CNS BARRIER INTEGRITY BREAKDOWN IN MOTOR NEURON DISEASE

Disruption of blood-brain (BBB) and blood-spinal cord (BSCB) barriers, is an early driver of neurodegenerative disease, yet the mechanisms underlying their impairment remain poorly understood. Neurovascular endothelial cells (NVECs), joined by tight and adherens junctions, form the barrier’s first line of defense. The mechanosensitive ion channel transient receptor vanilloid 4 (TRPV4) is enriched in NVECs and gain-of-function TRPV4 mutations cause motor neuron disease. However, their impact on CNS vascular integrity is unknown. This thesis investigates how TRPV4 mutations, particularly R269C, impair NVEC barrier function. Trpv4R269C/R269C mice developed forelimb weakness, motor neuron degeneration, and early lethality. Cultured NVECs isolated from these mice exhibited increased calcium influx and barrier breakdown following TRPV4 activation. In vivo, focal BSCB leak in the cervical spinal cord preceded motor neuron loss and was reversed by endothelial-specific TRPV4 deletion or pharmacologic inhibition. To characterize the cellular basis of this dysfunction, we modeled the human BBB using isogenic iPSC-derived brain microvascular endothelial-like cells (iBMECs). Mutant TRPV4 iBMECs showed loss of laminin-dependent regulation and exaggerated calcium influx in response to a TRPV4 agonist and shear stress. Flow triggered tight junction disorganization in mutant cells, with morphological ZO-1 changes observed specifically in cells with high calcium influx. These findings suggest that mutant TRPV4 drives focal barrier vulnerability downstream of aberrant calcium signaling. Together, this work identifies TRPV4 as a NVEC mechanosensor and a regulator of CNS barrier stability. It provides insight into how TRPV4 gain-of-function mutations trigger vascular dysfunction and supports therapeutic targeting of TRPV4 in neurodegenerative diseases