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Varieties of Exile: Russian Activism and Emigration during the War in Ukraine
No full textFollowing Russia’s full-scale invasion of Ukraine in February 2022, hundreds of thousands of Russians, possibly over a million, left their country. While many left for economic reasons—to avoid doing business under sanctions or because the companies they worked for relocated abroad—many others left due to ideological opposition to the war, increased persecution by the state, or fear of persecution. I ask how Russian political migrants during the full-scale invasion of Ukraine coped with their often-sudden exit from Russia, how and why they remain engaged in activist practices abroad, and how their collective identity is shaped by the context of their host countries. I argue that due to the conditions of wartime Russia, the precarity of migration, as well as the domestic political context of host countries, we cannot expect, as some scholars and analysts do, Russian political migrants to organize in the form of a classical social movement, or even in the ways that political migrants have previously been theorized about. Russian political migrants do not believe they have the capacity to overthrow Putin or end the war, nor do they lobby their host countries to apply pressure on Russia. Instead, continued political engagement is a matter of personal fulfilment, community building, and satisfying the compulsion to do something, even on a very small scale. Elucidating the subjectivity of these migrant Russians sheds light on how they understand themselves in relation to the state, how they resist its authoritarianism, and envision the political future for Russia. Doing so reveals how ordinary citizens dealt with the intense repression of the Russian regime prior to emigrating, as well as how they understand themselves and their own political agency in the post-Soviet periphery, spaces still grappling with the impacts of Russian and Soviet imperialism. This project is based on nine months of fieldwork in Armenia, Georgia, and Latvia from September 2022 to May 2023, during which I conducted in-depth interviews with 36 Russians from a variety of political and socio-economic backgrounds who emigrated from regions across Russia, as well as ethnographic observation of various protests and other political events in these field sites.Doctor of Philosophy (Ph.D.)2026-09-0
A Numerical Investigation of the Effect of Micro Vortex Generators on Film Cooling
No full textFilm cooling is a key technology for protecting turbine blades from high thermal loads, directly influencing component durability, efficiency, and operational safety. Micro vortex generators (MVGs) offer a passive approach to enhance near-wall mixing, suppress cooling jet lift-off, and stabilize the coolant film, enabling more uniform and effective surface cooling. Despite their widespread use, the detailed influence of MVG geometry, axial placement, and tip angle on aerodynamic and thermal performance remains insufficiently understood. This study examines MVG height (1.5 to 5.5 mm), axial location (–7.5 to +7.5 mm relative to the cooling hole), and tip angle (tested within ±5° of the baseline design) on film cooling over a stator blade. Aerodynamic and thermal effects are evaluated using turbulence kinetic energy (TKE), surface pressure distributions, temperature, film cooling effectiveness, enthalpy, stagnation density, cfRe (friction Reynolds number), Nusselt number, adiabatic film cooling effectiveness, and pressure-loss
measurements. Heights below 1.5 mm fail to generate coherent vortices, acting mainly as surface roughness, while heights above 5.5 mm induce local separation, vortex breakdown, and increased pressure loss, reducing overall cooling. Heights of 2.5–4.0 mm produce stable streamwise vortices that enhance near-wall mixing and extend the cooling film's effectiveness. Axial placement strongly influences vortex–jet interactions: upstream positions allow premature vortex dissipation, while positions too close to the jet disrupt the core flow and induce instabilities. Our study shows that the best cooling occurs for MVGs placed 5–7.5 mm upstream, where vortices remain coherent,
jet lift-off is suppressed, and lateral spreading is promoted. Tip angle variations within the tested range have minimal impact. Compared to the baseline case, the best MVG configuration improves surface cooling by 33–46% along the blade, with maximum gains near the root and mid-chord (X/D ≈ 0.05–0.2), highlighting the importance of well-designed MVGs for sustaining effective film cooling across critical blade regions
Agricultural Context and Bee Health: Assessing Pesticide Exposure and Interactive Effects with Pathogens and Drought
No full textPollinators are vital for sustaining biodiversity and global food production, providing essential ecosystem services valued at billions of dollars annually. Yet, their populations have been declining for decades due to multiple, overlapping stressors, including parasites and pathogens, land-use intensification, pesticide use, and climate change. While each of these threats has been extensively studied, their interactive effects on pollinator health remain less understood. In Chapter I explored honey bee pesticide exposure in relation to landscape composition by analyzing honey samples from local beekeepers across the state of Massachusetts, as well as pesticide contamination from commercial wax foundation—a widespread beekeeping practice that mimic natural honeycomb. We found that pesticide concentrations in honey increased with agricultural land cover and declined in areas with more wetlands. Although overall toxicity remained below established thresholds of concern— we used the U.S. Environmental Protection Agency’s acute contact Level of Concern (LOC = 0.4) as a reference (U.S EPA, 2015)—the synergist piperonyl butoxide showed a strong landscape response, with concentration and toxicity both increasing in agricultural landscapes and declining in wetland-rich areas. Additionally, we found that wax foundation itself exposes bees to a wide array of pesticides, representing an often-overlooked contamination pathway. These findings highlight the importance of integrated land management that protects ecosystems such as wetlands to mitigate pesticide exposure, while also rising awareness of the need for wax foundation standards that better support pollinator health.
Chapter II examined the effects of chronic, sublethal exposure to three field-realistic concentrations of the neonicotinoid thiamethoxam (0.5, 1, and 1.5 ppb) on Crithidia bombi infection dynamics in lab reared common eastern bumblebee (Bombus impatiens), using both isolated individuals and microcolonies. Elevated mortality occurred only in the individual trials at the highest concentration (1.5 ppb), resulting in its exclusion in the microcolony experiment. While thiamethoxam unexpectedly reduced parasite infection and transmission, it also led to reduced sucrose consumption, impaired mobility, and diminished brood care. These behavioral disruptions may offset any potential benefits of parasite suppression, illustrating the complex and sometimes counterintuitive consequences of interacting stressors on pollinators.
Chapter III investigated how drought stress influences neonicotinoid accumulation in the pollen of three major seed-treated crops in the United States: sunflower (Helianthus annuus), squash (Cucurbita pepo), and cotton (Gossypium hirsutum). We also monitored plant performance through weekly growth metrics such as flower production, corolla diameter, plant height, and vine length. Drought consistently reduced floral resource availability but mostly did not affect pesticide concentrations in pollen. Drought did increase detections of thiamethoxam in sunflower, but from a baseline of zero detections in well-watered and moderate stress plants to three very low detections—under the level of quantification—under drought conditions. Across all crops, we detected diverse pesticide mixtures—including insecticides, fungicides, and herbicides—raising concerns about additive or synergistic effects on pollinators. Importantly, patterns of pesticide accumulation differed substantially among crops, emphasizing the roles of crop physiology, pesticide formulations, and environmental context in shaping pollinator exposure risk.
Overall, my research demonstrates that pollinator health is shaped by the combined pressures of pesticides, pathogens, and environmental context. These findings underscore the urgent need for integrated conservation and land management strategies that reduce pesticide contamination at its sources, conserve natural habitats such as wetlands, and build resilience in both managed and wild pollinator populations.Mass Department of Agriculture (MDAR)
Sustainable Agriculture Research and Education (NE SARE)
American Association University Women (AAUW)
Howard E. Bigelow and Margaret E. Barr Bigelow
Natural History Collections Research Grant, UMass
Lotta Crabtree
Predissertation Research Grant, UMass
Spaulding Smith, UMassDoctor of Philosophy (Ph.D.)2026-03-0
Advancing the Knowledge of Social and Behavioral Determinants of Health Using AI in Health Outcome Research
No full textSocial and behavioral determinants of health (SBDH) are critical to understanding health outcomes and designing effective clinical decision support systems (CDSS). Despite their importance, SBDH information is often embedded in unstructured clinical data, limiting its utility in research and practice. With the widespread adoption of electronic health record (EHR) systems in the United States, natural language processing (NLP) has emerged as a promising approach to harness this relatively untapped resource. This dissertation explores the potential of NLP to extract, analyze, and apply SBDH information to advance clinical insights. We address three key research areas: (1) advancing SBDH data extraction, (2) elucidating the role of SBDH in critical health outcomes, and (3) integrating SBDH information into CDSS.
To address the need for high-quality public datasets to support the development of effective SBDH extraction systems, we propose Synth-SBDH, a novel synthetic dataset designed to improve NLP-based extraction of SBDH from clinical text. Generated using a large language model (LLM) via in-context learning, Synth-SBDH offers detailed annotations across 15 SBDH categories, including status, temporal information, and rationale. Our work demonstrates the dataset's utility in real-world applications, showcasing improvements in extracting rare SBDH categories with impressive performance gains in macro-F scores. Furthermore, we highlight Synth-SBDH’s potential to enhance model generalizability and support the training of models under constrained scenarios, paving the way for broader applications in clinical contexts. We propose to make our framework more generalizable and performant across different domains by incorporating LLMs and leveraging a larger and more diverse synthetic clinical SBDH dataset.
Next, we developed robust multi-task learning frameworks to extract SBDH from unseen EHR text and leveraged them to explore the association between SBDH and critical health outcomes. We started by investigating the role of NLP-extracted SBDH in understanding risk factors for opioid overdose (OOD) and suicide. Using clinical data from intensive care unit admissions, we identified significant associations between several SBDH factors — such as illicit drug use and insurance status - and nonfatal OOD. Our findings also underscore the substantial disparity between ICD-coded SBDH information and the wealth of data extractable through NLP, highlighting the transformative potential of NLP in filling gaps in observational research and clinical care. Similarly, using veteran health data, we found significant associations between NLP-enriched social determinants of health (SDOH) and critical health outcomes such as suicide and fatal OOD. These studies validate the utility of NLP in uncovering nuanced SBDH details that are often overlooked, thereby enhancing our understanding of the role of SBDHs in critical health outcomes.
Finally, we demonstrated the integration of NLP-extracted SBDH into predictive modeling frameworks to support CDSSs. Our research shows that incorporating SBDH into suicide prediction models significantly improves performance metrics across multiple prediction time windows. Similarly, in the context of fatal OOD prediction, both traditional machine learning and deep learning models—including those based on LLMs—benefited from the inclusion of SDOH predictors. These results highlight the utility of NLP-enriched SBDH to inform healthcare practitioners and policymakers, driving data-informed interventions.Doctor of Philosophy (Ph.D.)2026-09-0
REGULATING THE FIRM: THREE ESSAYS ON THE INTENDED AND UNINTENDED CONSEQUENCES OF HEALTH, SAFETY, AND ENVIRONMENTAL REGULATION
No full textThis dissertation studies how firms respond to health, safety, and environmental regulations, and how those responses reshape competitive dynamics, labor conditions, and distributional outcomes. Using case studies focused on poultry slaughter line speed and decarbonization policy, I analyze how regulation functions not only as a technical constraint but as a site of contestation over who bears the costs and benefits of shifting production practices.
The first chapter uses synthetic control methodology to estimate the effects of three federal regulatory changes that increased maximum slaughter line speeds in poultry plants from 1990 to 2020. Drawing on firm-level financial data, I estimate how these changes affected profits, profit rates, and profit margins for five poultry firms. I find that deregulation did not consistently improve short-run profitability and, in one case, reduced it. The results, combined with information from firm annual reports, suggest that higher line speeds led to overproduction, falling prices, and decreased firm profitability. This challenges the dominant industry narrative that line speed limits act as a bottleneck to firm performance.
The second chapter continues the analysis of line speed regulation and turns to its implications for labor. I argue that labor productivity can serve as a proxy both for rising physical demands on labor and an indicator of firm-level cost advantages. I construct a novel dataset linking the same regulatory events to changes in output per worker for the same five companies. The findings show that productivity gains were concentrated in specific firms and periods, reflecting variation in how companies respond to regulatory changes. The chapter highlights how line speed regulation makes the poultry industry an important case for studying the political economy of workplace risk.
The third chapter (co-authored with Michael Ash and Jim Boyce) analyzes the distributional effects of decarbonization in the U.S. electricity sector. We model three climate policy objectives – carbon reduction alone, carbon plus air quality, and carbon plus equity – and find that a carbon-only approach can increase local air pollution and worsen exposure disparities. Adding air quality and equity goals reduces both overall exposure and exposure gaps at relatively low cost.Doctor of Philosophy (Ph.D.)2026-09-0
Investigating the Mechanisms Determining RNA Stasis During Kaposi's Sarcoma Associated Herpesvirus Infection
No full textGene expression during Kaposi’s Sarcoma-associated Herpesvirus (KSHV) infection is shaped not only by transcription but also by post-transcriptional mechanisms that govern RNA stability, localization, and export. This work tests the hypothesis that the m6A reader protein IGF2BP1 modulates the fate of human IL-6 (hIL-6) mRNA through distinct mechanisms in latency and lytic infection, influencing expression, stress granule recruitment, and CRM1-dependent export. IGF2BP1 associates with hIL-6 during latency, although an m6A site in the 3′ UTR arises specifically during lytic infection, indicating context-dependent recognition through multiple modes of regulation. Phosphorylation by mTORC2 and SRC kinases may further influence IGF2BP1’s interaction with hIL-6 and its redistribution to stress granules, linking cellular signaling to viral control of RNA metabolism. KSHV-induced hyperadenylation during host shutoff alters export of transcripts such as hIL-6 and the Notch modulator Lunatic Fringe (LNFG). Although hyperadenylation often marks RNAs for decay, here it enhances CRM1-dependent export, establishing CRM1 as a determinant of transcript escape from nuclear retention. Finally, viral IL-6 (vIL-6) and hIL-6 cooperatively remodel host and viral transcriptomes during latency and reactivation, amplifying STAT3 signaling that intensifies during lytic infection. Collectively, these findings identify IGF2BP1 and CRM1-mediated export as central checkpoints in RNA fate during KSHV infection, while highlighting hyperadenylation as layered regulatory inputs. More broadly, this work demonstrates how human IL-6, viral IL-6, and Notch modulators are incorporated into KSHV’s post-transcriptional strategy to promote persistence, immune evasion, and oncogenic signaling.R35 GM138043/GM/NIGMS NIH HHS/United StatesDoctor of Philosophy (Ph.D.
Interfacial and Foaming Properties of a Soluble Fraction Derived from Pea Protein Sidestreams
No full textPlant-based protein ingredients must deliver functional properties comparable to dairy proteins and pea protein fractions shows promises. In this study, the interfacial and foaming properties of two pea protein fractions, a globulin-rich fraction (GRF) and an albumin-rich fraction (ARF), were investigated with mixtures at various ratios. Techniques like interfacial rheology and interfacial tension measurements at air–water and oil–water interfaces, as well as dynamic foam analysis to assess foam capacity and stability. The albumin-rich fraction (ARF) demonstrated superior foaming performance, producing a higher foam expansion (overrun) and more stable foams than the globulin fraction across all conditions. In contrast, the globulin-rich fraction (GRF) formed more viscoelastic interfacial films, contributing greater structural elasticity and rigidity at interfaces, but showed lower foamability. Notably, blending ARF and GRF led to synergistic effects like intermediate ARF:GRF mixtures achieved the lowest equilibrium interfacial tensions (dropping oil–water tension to the one digit mN/m range) and balanced interfacial rheology which outperforms the individual fractions alone. These findings highlight that ARF’s fast adsorption and foam generating ability, combined with GRF’s network forming strength can be leveraged to tailor interface and foam properties. The insights from this work broaden the understanding of pea protein fraction functionality and suggest that optimized ARF:GRF blends could be used to design improved plant-based foams and emulsions in foods by enhancing the applicability of pea proteins in sustainable food formulations.Master of Science (M.S.
Preparing for the Worst but Hoping for the Best: Censorship, Academic Libraries, and Reconsideration Policies
No full textLibraries in the United States have received the highest number of book challenges on record in recent years. Although the vast majority of these challenges happened at school or public libraries, we sought to assess how academic libraries are prepared to face such challenges, especially with the rise of state laws seeking to limit what subjects can be taught. To answer this question, we analyzed American members of the Association of Research Libraries’ reconsideration policies. Our analysis found that a minority of these libraries had a reconsideration policy
Investigating Contributors to Pancreatic Toxicity Following Developmental Perfluorooctanesulfonic Acid (PFOS) Exposure in Zebrafish (Danio rerio)
No full textPerfluorooctanesulfonic acid (PFOS), a legacy member of the per- and polyfluoroalkyl (PFAS) chemical class, has been shown to broadly affect organ systems using unknown or multifaceted mechanisms. Epidemiological studies demonstrate that PFOS crosses the placenta and is found in breast milk, underscoring the importance of studying developmental exposure. Developmental PFOS exposure in zebrafish has been shown to affect both the endocrine and exocrine pancreas. This dissertation identifies and investigates factors that contribute to pancreatic toxicity with developmental PFOS exposure. The endocrine pancreas is composed of islets of Langerhans containing insulin producing β-cells that regulate blood glucose while the exocrine pancreas secretes digestive enzymes to break down nutrients for intestinal absorption. Due to its structure, PFOS has been identified as a fatty acid mimic, aiding in its ability to enter cells through nutrient transporters. Here we compare the effects of PFOS and a non-fluorinated structural analog alpha lipoic acid (ALA). We demonstrate that, like PFOS, ALA reduce islet area and alters lipid parameters, indicating that that fatty acid mimicry plays a role in islet toxicity with exposure to ALA and PFOS during development. We also investigate how PFOS exposure affects the development and function of the exocrine pancreas across multiple larval stages. We show that increased yolk utilization can lead to an insurmountable nutrient deficit, leading to reduced exocrine pancreas size in post-yolk feeding larvae. Additionally, we show that even at a timepoint and PFOS concentration where there are no detectable changes to the size of the exocrine pancreas, there is reduced exocrine pancreas function. Finally, we study how PFOS exposure affects islet vascularization where we saw a decrease in contact between the vasculature and the β-cells, likely driven by changes to cell-cell adhesion, namely reduced integrin gene expression with PFOS exposure. This dissertation advances the understanding of contributors to pancreatic toxicity with PFOS exposure. Future studies should be focused on studying the mechanisms underlying the processes described in this work to better understand the contribution of PFOS exposure during development on the risk of chronic diseases later in life.Doctor of Philosophy (Ph.D.)2026-02-0
Learning-Augmented Online Algorithms for Energy Optimization
No full textFor competitive analysis of online algorithms, an online algorithm only knows current and past inputs and must make decisions sequentially as inputs arrive. The primary performance metric of competitive ratio - the maximum ratio of the online algorithm’s performance against the optimal offline algorithm’s performance with full knowledge of future inputs - is calculated over all possible problem instances. This framework is highly suited for energy optimization problems that face uncertainties in future variables such as price fluctuation, electricity demand, and renewable energy generation. Online algorithms are then able to provide theoretical performance guarantees on the cost of procuring energy, even when facing worst-case outcomes for future inputs.
In this thesis, we present optimal online algorithms for energy optimization in the competitive analysis setting. First, we consider online linear optimization with inventory management constraints where the clearing price of electricity is determined by bids submitted by market participants. We propose algorithms where the competitive ratio approaches those of optimal online algorithms in the basic setting without bids.
Competitive analysis fares well when future inputs are adversarial, but in practice, predictions on future inputs are available through machine learning or other predictive models. This has sparked a growing area of research on learning-augmented online algorithms that are able to use predictions while maintaining provable performance guarantees. This setting is well suited to the nature of predictions in energy optimization problems, where data-driven models for price and demand are able to leverage some degree of seasonality. However, underlying factors such as weather variation still cause unpredictable spikes in price and demand.
We then present energy optimization problems in the learning-augmented setting. First, we analyze the peak-aware energy scheduling problem and propose Pareto-optimal algorithms that can utilize a trust parameter of the predictions. Second, we consider the k-min search problem with predictions. We design our algorithm with both robustness (when prediction error is arbitrary) and consistency (when predictions are accurate) guarantees such that our algorithm achieves the Pareto-optimal tradeoff of robustness and consistency.Doctor of Philosophy (Ph.D.