UC Irvine

Digital markets are built on data, low-friction access, and user trust. This dissertation studies what happens when each of these pillars is disrupted. Across three essays, it shows that privacy regulation, registration-based access barriers, and public revelations of workplace misconduct can materially alter consumer purchasing, attention allocation, and platform choice. The first essay examines the unintended consequences of the California Consumer Privacy Act (CCPA) for consumer commerce. Using the CCPA as a natural experiment and combining billions of payment transactions with browsing data and firm-level measures of advertising technologies, the essay shows that Californians purchased less, returned more, and spent more time browsing after the law took effect. Firms subject to the regulation also reduced their use of ad-related technologies. These findings suggest that stronger privacy protections may unintentionally weaken personalization and product-consumer matching, reducing commercial activity even as they expand consumers' control over personal data. The second essay studies the New York Times' introduction of a registration wall as a first-party data collection strategy. Using web browsing data together with a regional measure of privacy salience based on cumulative exposure to data breaches, the essay finds that registration walls generate heterogeneous responses across user segments. News-singlehomers reduce engagement after the wall is introduced, whereas news-multihomers slightly increase engagement. Higher privacy salience attenuates the decline among singlehomers and strengthens the positive response among multihomers. When users disengage, their attention shifts primarily toward search engines and video-sharing platforms, revealing broader ecosystem consequences of registration-based access barriers for news consumption. The third essay investigates how public whistleblowing about workplace sexual harassment and discrimination affects consumer demand in platform competition. Exploiting a blog post by a former Uber employee describing sexual harassment, discrimination, and the company’s response—an account that quickly went viral on social media—as an exogenous reputational shock, the essay shows that Uber usage declines while Lyft usage rises among riders most able to switch between the two platforms. These effects are strongest in markets with greater local attention to the event and among riders predicted to be female, highlighting how misconduct toward minorities can translate into meaningful demand-side consequences in consumer markets. Taken together, the three essays demonstrate that consumer behavior in digital markets is shaped not only by prices and product attributes, but also by data governance, access design, and corporate legitimacy. The dissertation contributes to research on privacy regulation, digital media monetization, and platform competition, while offering practical implications for firms and policymakers seeking to balance personalization, access, and accountability in the digital economy.

English academic writing is a critical yet challenging skill for learners in English as a Foreign Language (EFL) contexts. The rapid integration of digital tools, accelerated by the COVID-19 pandemic, has transformed writing instruction; however, evidence of its pedagogical effectiveness remains fragmented and often overlooks teacher perceptions and genre-specific impacts. This three-study dissertation addresses these gaps by investigating the role of digital tools in EFL writing instruction within the EFL higher education context, employing a multi-method approach to triangulate evidence from student outcomes, meta-analytic synthesis, and teacher experiences.Study 1 conducted a classroom experiment with 111 Chinese undergraduates, comparing infographic-based pre-writing to traditional outlining. Results showed that infographic creation significantly improved summary-writing quality, source use, and self-efficacy, but not opinion writing, highlighting the genre-sensitive nature of tool effectiveness.Study 2 synthesized 17 experimental and quasi-experimental studies (N = 1,085) through a meta-analysis. It found a statistically significant, moderate overall effect favoring web-based collaborative writing (WBCW) over non-technological collaboration on writing quality (Hedges’ *g* = 0.51). Moderator analyses indicated that training was a significant factor enhancing outcomes.Study 3 explored the perceptions of seven Chinese university writing instructors through qualitative interviews. Grounded in the TPACK framework, the findings revealed that while digital tools are deeply embedded in instruction, their use is shaped by teachers' knowledge, institutional support, and access. Teachers exhibited cautious and limited integration of emerging Generative Artificial Intelligence (GenAI) tools due to concerns over academic integrity and a lack of institutional guidance.Collectively, the findings demonstrate that technology enhances EFL academic writing most effectively when tools are aligned with genre demands, collaboration is scaffolded by design and training, and implementation is supported by teacher knowledge and institutional context. The dissertation concludes by advocating for a situated, tool-genre-task alignment perspective in future research and practice, moving beyond technocentric adoption to support equitable and effective writing instruction in diverse EFL settings.

Self-renewing hematopoietic stem cells (HSCs) produce billions of blood cells a day to maintain peripheral blood and immune cells in circulation. Inflammation can alter the balance of steady-state hematopoiesis, and disrupted hematopoiesis can lead to blood disorders or cancers. Over a lifetime of cell divisions, HSCs may acquire somatic mutations that provide a competitive advantage over their wild-type counterparts. The clonal expansion of a mutant HSC population is termed “clonal hematopoiesis” (CH). CH is linked to an increased overall risk of mortality due to incidences of cardiovascular disease and transformation into hematological malignancy. Tobacco and nicotine use remain the leading preventable drivers of cancer risk, and both direct and secondhand exposure to combustible cigarettes or electronic nicotine devices perturbs immune function and hematopoiesis. The World Health Organization estimates more than 100 million people across the world are using electronic cigarettes, or e-cigarettes, but the health impacts of these “safer” e-cigarette alternative have yet to be fully elucidated. E-cigarettes have been associated with inflammation and oxidative stress, which can provide selective pressures for the outgrowth of CHIP mutant cells.Here, we evaluate the impact of e-cigarette vapor and combustible cigarette smoke on in vitro cell inflammatory responses and in vivo long-term hematopoietic differentiation. The overarching goal of this project was to understand further how an inflammatory lifestyle stressor, such as smoking, contributes to aberrant hematopoiesis in wild-type normal stem cells, Tet2- deficient cells, and JAK2V617F mutant cells. In cell-based studies, cigarette smoke extract (CSE) and e-cigarette vapor extract (EVE) consistently suppress LPS-induced TNF-α secretion across macrophage/monocyte models, including primary mouse and human cells and complementary cell lines, indicating a reproducible immunosuppressive effect on mature myeloid cells. To assess consequences of smoking behavior in vivo, we used a custom nose-cone inhalation system to deliver controlled exposures to combustible cigarette smoke or e-cigarette aerosol to mice. Chronic exposure increased myeloid proliferation, a hallmark of HSC aging. Taken together, these results support a model in which tobacco exposures blunt innate immune responsiveness while simultaneously driving myeloid expansion conditions that accelerate hematopoietic aging and promote the expansion of mutant hematopoietic cells.

The distributed training of machine learning models via gradient descent is generally conducted by iteratively computing the local gradients of a loss function and aggregating them across all processors. Communicating these gradients during aggregation is often a major cost but sparsification techniques can greatly improve efficiency. One such technique, Top-k gradient compression, ensures that only the k largest components of each local gradient are sent. However, effectively scaling this method can be challenging. The standard ring AllReduce algorithm, which is frequently used to aggregate dense gradients, lacks a counterpart that is optimized for sparse data. Notably, ring algorithms are contention-free, which generally make them easier to scale than other collective communication algorithms. Thus, in practice, the ring AllGather algorithm, which can be trivially adapted for sparse data, may be used instead, even though its bandwidth costs are proportional to the number of utilized processors (unlike ring AllReduce). To provide a more scalable contention-free alternative, we present a variant of ring AllReduce that has been better optimized for sparse data. We compare it to the standard dense ring AllReduce and ring AllGather algorithms, and we evaluate it empirically using gradients sampled from fine-tuning Llama 2 7b.

One-dimensional (1D) and quasi-1D (q-1D) van der Waals (vdW) materials, which have strong bonding along only one crystallographic axis, have emerged as powerful class of solids that hosts novel electronic, optical, and quantum properties useful for next-generation electronics. These materials can, in theory, be thinned to the angstrom scale due to the ideal vdW surfaces along two axes. In practice, achieving atomically precise single chains of these materials poses a significant challenge, as conventional top-down exfoliation and bottom-up growth techniques consistently retain interchain bonding, especially in q-1D materials with anisotropic interchain bonding motifs. The question then arises: Is there a synthetic approach that would enable the suppression of all inter-chain interactions, leaving only intra-chain covalent bonding in one dimension? Using the model q-1D pnictogen chalcogenides (Pn2Ch3; Pn = Sb, Bi; Ch = S, Se, Te), chosen for the highly anisotropic structural complexity and strong inter-chain bonding combined with distinct photophysical properties, we explore encapsulation within ultranarrow nanotube growth templates to precisely define the growth of the material in sub-nanometer length scales. The deployment of these nanotube templates as encapsulants overcomes the inter-chain interactions in these phases to isolate single q-1D chains by affording physical space that directly matches the size of a single chain. More importantly, we can also probe many chains simultaneously in a collective using conventional spectroscopic techniques used in ensemble samples, allowing us to access nanoscale properties through bulk measurements. Herein, not only do we gain insight into structural and electronic properties of these single chains, but we also further our understanding of anisotropic bonding in the bulk structure by understanding how it affects single chain accessibility. As technological devices advance through shrinking and densification, understanding material properties at the atomic limit becomes increasingly vital. Our results demonstrate a powerful tool for synthesizing and studying precisely defined single inorganic chains that approach the atomic limit.

What is the soul, and what can a world do to it? Reading Aristotle through Heidegger, I argue that human being is rhetorical: a moved mover to whom every situation is disclosed, in an attunement, as a field of potentialities for actualization. Part I reconstructs the actualization chain, from perception through phantasia, doxa, and emotion to action, with phantasia at its temporal center, holding past, present, and future in one narrative. The chain spirals: each pass sediments the ground of the next, so that a history of engagement becomes the disposition from which a being next perceives. Rhetoric, then, is the working of an entity—a speaker, a river, a designed world—upon that field; it reshapes not what the soul does but what it perceives, takes-as-true, and desires. Part II develops Rhetorical-Ontological Diachronic-Recursive Analysis (RODRA), which traces that chain across repeated passes through a designed world and reads what each pass leaves behind. Two worlds bring their player to rhetorical incorporation by opposite routes. Gnomes & Goblins, miniaturizing the player within its forest, breaks the world and keeps the player; Red Dead Redemption 2, seizing the player's agency, breaks the player and keeps the world—ontological violence, worked by conscription. What separates them is not medium but route: the discrepancy a world opens between what it shows and what the player brings to it, and the work of phantasia that discrepancy demands. Part III turns to a virtual hospital built for biomedical-engineering students and deployed across three quarters to 241 of them, who reported presence and still preferred the plainer copy beside it—a world holding everything to see and nothing of worth to complete. A laboratory study then sets eye closure and gaze deviation beside a seven-item self-report across thirty-six film-viewing episodes; in nine, the eyes stayed steady while the retrospective verdict said bored: the shape of Heidegger's second form of boredom, in which one is bored with a situation while passing the time inside it, demonstrated rather than measured. A virtual world attunes without determining, and it does so by hacking the soul of those who step into it.

Combustion systems remain essential to modern energy conversion across residential, commercial, industrial, and transportation sectors due to their high power density, flexibility, and compatibility with existing infrastructure. However, conventional combustion of carbon-based fuels such as natural gas, gasoline, and coal produces greenhouse gases and harmful pollutants including CO2, NOx, and particulate matter, necessitating improved combustion strategies as part of broader decarbonization efforts. Alternative fuels, particularly hydrogen and ammonia, offer promising pathways to reduce or eliminate carbon emissions while preserving the practical advantages of combustion-based systems, though their distinct chemical and physical properties present unique challenges related to flame stability, pollutant formation, and system operability. This dissertation investigates two hydrogen-containing fuel strategies: catalytic combustion of hydrogen/methane blends for commercial cooking applications as a low-NOx alternative to conventional flame-based combustion, and hydrogen/ammonia combustion for gas-turbine relevant conditions, where hydrogen addition is explored as a means of enhancing ammonia flame stability while maintaining carbon-free operation.In the catalytic combustion study, a Pd-Al2O3/cordierite monolith was experimentally evaluated for the combustion of H2/CH4 blends containing 0–100% H2 across equivalence ratios of 0.5–1.0 and varying flow rates. Catalyst temperatures reached up to ~1200 °C depending on xxii operating conditions, while complete fuel conversion and zero NOx emissions were achieved within the stable operating window. Preheating the catalyst to 390 °C and sustaining external heating for approximately one minute after reaction initiation enabled self-sustaining combustion without fuel slip. At high H2 concentrations, autoignition and flashback led to irreversible Pd agglomeration and catalyst deactivation, establishing an upper limit for stable operation.To further interpret these experimental results, chemical kinetic simulations were performed using CHEMKIN to examine the catalytic reaction behavior of H2/CH4 blends. H2 reacted rapidly near the monolith inlet, while CH4 oxidation extended farther downstream, giving rise to a distinct two-zone reaction structure. Increasing temperature shifted reaction activity toward the inlet, whereas variations in equivalence ratio and flow rate altered the extent of the reaction zone. The simulations successfully reproduced complete fuel conversion across most experimental conditions and provided insight into surface reaction behavior not directly accessible through experimental measurement alone.In the ammonia combustion study, the stability of premixed H2/NH3/air flames was examined across pressures of 1–11 bar and equivalence ratios of 0.8–1.3. Blends with high NH3 content, with 90% or greater NH3, were unstable across all conditions tested. Increasing pressure reduced stability as laminar flame speed decreased, whereas H2 addition expanded the stable operating envelope by increasing flame speed and resistance to blowoff. Qualitative imaging revealed corresponding changes in flame structure, including increased lifting and a more conical flame shape as stability decreased.Together, these studies define operating limits and combustion strategies for hydrogen containing fuel blends across two distinct applications, demonstrating pathways to reduce carbon intensity while maintaining stable combustion, high thermal performance, and low emissions.

Issues of projectibility are central to the philosophy of induction. The basic problem goes like this. Inductive learning involves projecting regularities in past experience onto unobserved cases. But any body of experience can be described as exhibiting any number of regularities, and depending on which of these we project, induction will yield different beliefs and predictions. A principled procedure for identifying regularities that provide good guidance for belief and prediction—i.e., projectible ones—has proven elusive. Nevertheless, the impressive track record of human and animal learning suggests that nature has found ways to deliver us assumptions of projectibility that track stable, practically relevant regularities in many contexts. This dissertation uses computational models to show how simple natural adaptive processes might accomplish this critical task. Specifically, the goal is to explain how such assumptions might emerge from trial-and-error learning in kinds of problems that arise frequently in nature. Chapter 1 supplies philosophical background and motivation. Chapter 2 considers projectibility in the context of signaling, building on a game theoretic model due to Lewis (1969). Chapter 3 turns to discrimination learning, modeling the emergence of assumptions of projectibility in a setting based on classic primate learning experiments conducted by Harry Harlow. Chapter 4 concerns the special challenges of projectibility posed by task switching problems. Chapter 5 concludes.

Cell-cell communication (CCC) is essential for coordinating important developmental processes, including tissue patterning. Although cells can communicate through well-characterized mechanisms such as endocrine, synaptic, juxtacrine, and paracrine signaling, these are not the only signaling modalities that exist in nature. Cells can also communicate via specialized filopodia, allowing for direct targeting. There are several subclasses of signaling filopodia, including airinemes, which are the focus of this dissertation. Airinemes are a unique class of specialized filopodia that mediate signaling during zebrafish pigment pattern development through the coordinated activities of xanthoblasts, macrophages, and melanophores. Airinemes originate as surface blebs (airineme blebs) on xanthoblasts that are recognized and extracted by migrating macrophages, which transport airineme vesicles and deposit them onto target melanophores. Despite previous work establishing the importance of airinemes in pigment pattern formation, several aspects of airineme-mediated CCC remained unresolved, including the identity of the macrophage population responsible for airineme transport, the molecular mechanisms governing macrophage-airineme interactions, and basic characterization of airineme blebs.This dissertation addresses these questions through three complementary studies. First, I identified at least two morphologically and behaviorally distinct macrophage populations in the zebrafish skin and demonstrated that amoeboid macrophages, which overlap with the previously described ectoderm-derived macrophage population known as metaphocytes, preferentially localize to the hypodermis where airineme-projecting xanthoblasts reside. I further demonstrated that metaphocyte migration into the hypodermis requires MMP9. Second, I identified the cell-surface glycoprotein CD44 as a mediator of interactions between metaphocytes and airineme bleb-bearing xanthoblasts. Cell-specific disruption of the CD44 extracellular domain reduced airineme frequency, demonstrating that CD44-dependent interactions contribute to airineme-mediated signaling. Finally, I established size- and morphology-based criteria for identifying putative airineme blebs and used these criteria to characterize their developmental distribution and potential maturation. Putative airineme blebs increased in abundance prior to peak airineme production, and a subset exhibited CD44a as well as phosphatidylserine (PS), a previously established recognition signal for macrophage-mediated airineme extraction. Together, these findings support a model in which airineme-mediated CCC is assembled through a series of regulated cellular and molecular interactions involving airineme bleb formation, macrophage recognition and transport, and delivery of signaling cargo to target cells. This work expands our understanding of how immune cells can acquire specialized roles in developmental CCC and provides a framework for investigating the formation and maturation of airineme blebs and other membrane-associated signaling structures.

Modern Large Language Models (LLMs) offer exceptional inference accuracy but remain severely restricted by high resource and power requirements. Post-Training Quantization (PTQ) mitigates these memory bottlenecks by compressing weights to sub-4-bit regimes; however, existing Hadamard rotation-based methods that enable nearly lossless 4-bit weight and activation quantization (W4A4) does not push weight quantization below INT4, thereby limiting the memory size reduction potential of Hadamard transform. Furthermore, many GPU-based mixed-precision approaches lack native hardware support for integer to floating point multiplication and custom low-bit multiplication kernels. This requires runtime upcasting of integer weights and fails to leverage the resource saving potentials of low-bit representations during computation, leaving custom FPGA accelerators as the most efficient platform for low-bit mixed precision execution. To address these problems, this paper presents a projection-wise quantization method and a custom FPGA accelerator design that supports this quantization method. The proposed quantization scheme for Llama 2-7B compresses weights to 3.56 bits and achieves 10.87% reduction in weight storage relative to W4A4 QuaRot with a minimal perplexity increase of 2% and obtains a better Compression-to-Degradation Ratio than the current state-of-the-art SliM-LLM. In addition, the mixed precision accelerator design with custom Table Lookup-based matrix multiplication kernels for 3-bit weight operations achieves nearly 50% reduction in LUT usage compared to standard Multiply-and-Accumulate (MAC) units. Finally, to mitigate the additional latency overhead of online Hadamard operation, the proposed accelerator design incorporates a 2-stage pipelined online Hadamard transform unit that reduces the latency of attention projection Hadamard transformations by 13.7%.