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Assessing the Community Impact of Two Overdose Prevention Centers in New York City
No full textHarm reduction programs focus on improving public health outcomes and service delivery for people who use drugs (PWUD). Examples of such programs include overdose prevention centers (OPCs), which allow PWUD to consume substances under medical supervision. A large body of research has highlighted the positive public health effects of OPCs, which include safer drug-using practices, increased treatment enrollment, and a reduced likelihood of overdose among program clients. However, there has been limited research evaluating the impact of these programs on the larger community. This research gap is particularly salient in urban neighborhoods that have struggled to overcome a history of drug epidemics, exposure to violence, and increasing quality of life concerns.
This dissertation uses a mixed-methods approach to examine the community impact of the first sanctioned OPCs implemented in the United States in two New York City neighborhoods: East Harlem and Washington Heights. The research draws on NYC311 data and administrative police data to evaluate the impact of OPCs on local neighborhoods, while controlling for a variety of contextual factors such as socioeconomic and land use variables. To complement this analysis, qualitative interviews were conducted to examine stakeholder perspectives on the implementation of OPCs in their communities. Data were gathered through semi-structured interviews with residents, business owners, and social service providers, supplemented with a historical analysis of the two neighborhoods. Findings aim to better understand how public health programs impact the larger surrounding community and to assess the factors that drive underlying community resistance/support towards OPCs. Policy implications emphasize the importance of collaboration between public health and criminal justice sectors to create policies that address the varied health and safety needs of local urban communities
Online Visual Query System for Real-Time Large-Scale Spatio-Temporal Data Explorations with Error Bounds
No full textModern datasets continue to grow in size, dimensionality, and heterogeneity, creating increasing tension between the need for responsive, interactive analysis and the computational cost of accessing, aggregating, and visualizing large volumes of data. Traditional database engines and visualization tools often assume that full data retrieval is feasible or that exact computation is necessary for meaningful insight. In practice, however, analysts frequently benefit from timely, uncertainty-aware approximations than from delayed and exact results. This thesis investigates how data summarization techniques, specifically mergeable sketches can be combined with progressive, out-of-core visualization methods to support interactive exploration of datasets that exceed main memory. The first contribution of this thesis is the design and theoretical analysis of a mergeable data sketch tailored for scalable visual analytics. Unlike sketches that are either not composable or lose accuracy when merged repeatedly, the proposed construction preserves formal approximation guarantees under merging while maintaining a compact memory footprint. This formulation allows sketch summaries to be built independently across chunks and then combined efficiently without compromising error bounds. Together with an out-of-core index mechanism, the sketch supports multi-resolution summarization, enabling rapid coarse estimates that can be refined progressively.
Building on this foundation, the second contribution is an out-of-core visualization engine that uses the sketches within a spatial–temporal Two-level indexing framework. The engine streams data from disk, incrementally constructs and merges summaries at multiple granularities, and serves the approximate results to the front end. As a result, users can interact with datasets far larger than memory, receiving immediate but approximate feedback that is refined over time. The system explicitly encodes approximation uncertainty and communicates the approximations back to users.
To examine the practical utility of this combined approach, we present a proof-of-concept application demonstrating sketch-based interaction for exploratory querying. The prototype integrates the multi-resolution index with the front end that supports real-time spatio-temporal querying, approximate matching, and real-time refinement. Through a staged evaluation including performance measurements, qualitative system behavior analysis, and scenario-based demonstrations, the PoC system illustrates how mergeability, progressive refinement, and uncertainty-aware visualization jointly support fluid exploration when the underlying dataset is very large.
Experiments show that the sketch construction scales nearly linearly with data size, preserves accuracy under deep merging, and enables query latencies compatible with interactive use. The indexing scheme further reduces disk access overhead by prioritizing partitions most relevant to the user’s current view. Collectively, these results demonstrate that principled sketch design and system-level engineering can substantially narrow the gap between large-scale data processing and interactive analytics.
The thesis concludes by discussing the remaining challenges, including adaptive parameter selection, refinement prioritization, support for heterogeneous data types, and the need for formal user studies on progressive and uncertainty-aware interfaces. The work establishes a foundation for future research at the intersection of approximate computation, out-of-core analytics, and human-centered visualization. By integrating mergeable sketches with a progressive visualization engine, this thesis provides both theoretical and practical contributions toward scalable, interpretable, and interactive data exploration
Comparing ChatGPT, SciSpace, Copilot, and Human Brainstorming
No full textThis assignment introduces students to collaborative inquiry, AI-assisted research, and critical source evaluation within the context of contemporary social issues. Working in groups, students brainstorm a course-related social problem, develop an exploratory research question, and generate preliminary explanations grounded in personal experience, common sense, and course concepts. They then use SciSpace Copilot to gather scholarly sources and apply the CRAAP test to evaluate credibility and relevance. Students subsequently compare these findings with responses generated by ChatGPT, analyzing similarities and differences in scope, reasoning, and evidence. Through structured reflection, students critically assess how AI shapes the research and information-gathering process. As the first stage of a scaffolded project, this assignment builds toward the creation of an infographic that disseminates researched insights and potential solutions, strengthening students’ information literacy, critical thinking, and ethical AI use
“We Cannot Live Without Our Lives”: Art-Activism and the Struggle for Black Women’s Lives in Boston, 1979
No full textThe Combahee River Collective’s political work in response to the murders of twelve Black women in Boston in 1979 is a crucial chapter in the history of Black queer socialist feminism, with important political lessons about resistance for activists and scholars today. This essay explores this history, with a focus on two benefit readings organized at the height of the movement in July 1979: one advertised as “An Evening of Poetry by Black Women” featuring Audre Lorde, Donna Kate Rushin, and Barbara Smith, among others, and a second reading featuring Lorde and Adrienne Rich. These readings created space for public mourning and resistance, affirmed life amid death, and committed participants to bearing witness and imagining freedom. I contend that they also played a critical role in the creation of a woman of color lesbian canon that is foundational to lesbian studies now. I contextualize Lorde’s “Need: A Choral of Black Women’s Voices,” which was inspired by the Combahee River Collective’s organizing and read at both events, within this social movement history, underscoring its use as a political organizing tool while modeling an intersectional activist aesthetics that remains deeply influential to contemporary artists, activists, and theorists. Then as now, art and politics were central to challenging the invisibility and silence around the lives and deaths of Black women and to developing an antiracist, anti-sexist, anti-homophobic political analysis and movement
Latino Immigrant and Second-Generation Small Food Business Owners\u27 Survival Strategies in New York City in a Time of Crisis
No full textWhen the Covid-19 pandemic hit, there was an influx of news stories about restaurant closures as stay at home orders kept many consumers eating at home. This was especially the case in New York, an early epicenter. However, there were also businesses that were resilient and beat the odds to remain open. These businesses are the topic of my dissertation. Immigrants and their children are key small business owners in the city. I specifically look at Latino-run businesses because Latinos were among the hardest hit in Covid-19 cases and because they are one of the largest growing immigrant groups in New York City. This dissertation looks at how Latino small food business owners were able to survive the Covid-19 crisis. Specifically, I ask: what resources did Latino small food business owners rely on to survive the pandemic? To answer this question, in 2022 and 2023, I interviewed 39 Latino small food business owners, 1 prospective Latino small food business owner, and 1 Latino restaurant manager in New York. I found that Latino small business founders had no single strategy for staying open, but rather chose a strategy based on the resources they have at their disposal. To this end, I constructed three typologies (traditionalist, enterpriser, aficionado) based on the primary capital the Latino small food business owners I interviewed relied on to survive the pandemic: social, economic, and cultural. I found that traditionalists relied on their social networks; enterprisers relied on their access to, and facility with, economic capital; and aficionados on their social networks from their education and previous employment and access to economic capital to get started, then later on their cultural knowledge and facility of social media to get through the pandemic. I argue that Latino small food business owners’ do not have a single set of motivations to run a business, but rather a diverse set of motivations which shape the strategies they employ to survive the major economic crisis of the Covid-19 pandemic. This dissertation helps reveal how small business owners make decisions in times of crisis. Overall, this study contributes to the sociology of immigration, specifically the ethnic entrepreneurship literature by showcasing a modern-day study of ethnic entrepreneurship. This study also contributes to the sociology of consumption, especially the literature on food and consumer tastes by highlighting the role of the makers of food
Computational and Functional Characterization of Terebrid Venom Peptides
No full textVenom peptides represent a natural repertoire of bioactive compounds that are potent modulators of ion channels. Teretoxins, venom peptides from terebrid snails, remain underexplored despite their structural and functional diversity. This thesis is the first to comprehensively explore the structure and function of teretoxins using a comparative computational sequence and structure model followed by experimental assays. The majority of the functional focus has been on identifying the ion channel molecular targets of uncharacterized teretoxins. Teretoxins, like other venom peptides, are hypothesized to manipulate the cellular function of a diversity of ion channels. Dysregulation of ion channels leads to several human diseases and disorders, including pain and cancer. In this work I introduce how venom peptides from marine snails have a demonstrated potential for drug discovery and development, and how uncharacterized venom peptides, like teretoxins, can be used to target dysregulated ion channels in the most common form of liver cancer, hepatocellular carcinoma (Chapter 1).
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths and disproportionately affects Black and Asian populations. HCC is often diagnosed at advanced stages where treatment options are limited, and sorafenib offers only modest benefits. As a channelopathy, HCC is characterized by the overexpression of transient receptor potential (TRP) ion channels. Teretoxins that target dysregulated ion channels represent a promising source of anti-HCC therapies. One of the focus areas of this dissertation investigates the structural, and functional characteristics of teretoxins with the goal of identifying candidates with antitumor and ion-channel-modulatory potential (Chapter 2). This chapter examines the feasibility of that effort by reviewing the current literature and practices and outlining the inherent advantages of venom peptides in the HCC use case scenario.
The central hypothesis guiding this dissertation research is that teretoxins sharing structural features with functionally characterized venom peptides would exhibit similar functional activity. To test this, I performed a large-scale computational screening of approximately 4,000 venom peptides (1,558 teretoxins and 2,322 conotoxins) using AlphaFold2 for structure prediction—the first application of this tool to a comprehensive terebrid peptide dataset (Chapter 3). Structural clustering with TM-align identified seven peptide co-clusters with various predicted activities including neurotoxin, ion-channel targeting and Kunitz-type protease inhibitors with potential ion-channel-modulatory functions. Predicted function activity of the Kunitz-type peptide cluster was orthogonally validated through molecular docking and molecular-dynamics simulations, further strengthening analysis to deorphanize teretoxin Tar2.9 and cone snail venom peptide, conotoxin P07849. In an effort to apply sequence and structural predictions to large datasets of uncharacterized venom peptides, I co-developed MARC (Molecular Arms Race Classifier), a machine-learning model that classifies venom peptides by their ion-channel molecular targets –potassium (K⁺), sodium (Na⁺), calcium (Ca²⁺), or non-ion channel acting (Chapter 4). MARC predicted that 28 unique teretoxin peptides were primarily K+ channel modulators. This prediction was validated by the overall stability of a docked complex of the highest rated teretoxin Cje1.9, with the K+ channel, MthK (pdb_00004hyo).
To bridge the computational predictions with experimental efforts, I optimized peptide synthesis and oxidative folding workflows for cysteine-rich teretoxins and characterized the bioactivity of the teretoxin Tv1 in human HCC cell lines (Chapter 5). Tv1 demonstrated selective antiproliferative effects in SNU475 and HUH7 cells compared to normal liver cells, suggesting anti-cancer specificity. Although the precise mechanism of Tv1’s anti-cancer activity remains unresolved, experimental evidence indicates that Tv1’s activity is not mediated by manipulation of TRPC3 channels but may involve other calcium-channel interactions.
Overall, this work integrates computational modeling, machine learning, and molecular experimentation to establish a framework for studying teretoxins as potential ion-channel modulators and anticancer agents. It expands the structural and functional understanding of terebrid venom peptides and lays the foundation for future mechanistic and therapeutic development
Nanoscale Probing of Electrochemical Processes on Photocatalytic Materials by Tunneling Mode Photo-Scanning Electrochemical Microscopy
No full textScanning electrochemical microscopy (SECM) has become an indispensable technique for resolving charge-transfer processes with micro- to nanometer spatial precision. This dissertation advances SECM and its photoactive variants as powerful tools for interrogating electrochemical and photoelectrochemical phenomena across catalytic nanostructures, semiconductor interfaces, and complex photocatalytic architectures. The work begins with fundamental principles of nanoelectrode behavior and SECM operation (Chapter 1), establishing the theoretical and instrumental foundation required for high-resolution electrochemical imaging. Building on these fundamentals, Chapter 2 examines heterogeneous photocatalysts using advanced electrochemical scanning probe microscopies, integrating amperometric and potentiometric modalities to map activity, charge separation, and interfacial energetics.
In Chapter 3, we extend the tunneling mode of SECM to characterize charge-transfer kinetics at single metallic, pseudo-metallic, and semiconducting nanoparticles. Using ultrathin carbon nanoelectrodes, this work enables single-nanoflake voltammetry without ohmic contact, revealing intrinsic catalytic behavior in MoS2 phases, porous carbon catalysts, and MXene nanosheets. Chapter 4 applies both diffusion-based and tunneling-based photo-SECM to nanoscale photocatalysts, achieving spatial resolutions down to 1–2 nm. These measurements reveal local photocurrent generation, hydrogen evolution, and active-site heterogeneity in mixed-phase 2D materials and CVD-grown semiconductors.
Chapter 5 demonstrates the combined amperometric and potentiometric photo-SECM analysis of Pt/TiO2 photocatalysts, capturing nanoscale variations in photogenerated charge carriers, catalytic turnover, and interfacial potentials under operando conditions. Finally, Chapter 6 integrates SECM imaging with simplified band-structure modeling to construct an electrochemical geometry-programmed photocatalyst of GaInP/GaN photocathodes functionalized with spatially separated IrOx and Rh–CrOx cocatalysts. Using Substrate Generation/Tip Collection and tunneling-distance potentiometric SECM, we directly probe and map local oxygen and hydrogen evolution, catalyst-dependent band bending, and interfacial Fermi-level shifts with nanometer precision.
Together, this dissertation establishes a unified framework for probing, understanding, and engineering charge-transfer processes at the nanoscale. The integrated use of tunneling SECM, photo-SECM, and digital-twin modeling provides mechanistic insight essential for the rational design of next-generation catalysts and semiconductor materials for solar energy conversion, providing a general operando framework for designing and diagnosing next-generation photocatalysts
Dumatree: Free Online Education in Bridging the Migrant Digital Divide
No full textResearch Questions: This capstone project addresses two primary research questions. First, it examines the digital divide within the immigrant-serving non-profit sector, specifically investigating the lack of computer and digital skills training in organizations that serve refugees and migrants. Second, it asks: is it possible to build a scalable, professional-grade tech curriculum using only free, Wi-Fi-accessible online tools? To address the first question, 50 non-profit organizations serving refugees and migrants were reviewed to assess their technical education offerings. To address the second question, Dumatree.com was developed using universally accessible digital resources. A website was the most appropriate format for this project, as it demonstrates that migrants with limited funds and time can access vital information at no cost from any location, provided they have an internet connection.
Findings and Observations: Of the 50 non-profits reviewed, 18 (36%) provided English language programs, while only 8 (16%) offered computer technical programs. While legal advocacy, resettlement services, and basic human needs are undeniably essential, technical education—alongside language proficiency—is a critical component of successful integration into new communities and labor markets. Despite this importance, there is a notable lack of technology-focused programming among these organizations. To bridge this gap, Dumatree was built using exclusively free, publicly available resources. Over 50 websites, Massive Open Online Courses (MOOCs), platforms, and YouTube channels were reviewed and selected based on their user-friendly formats, comprehensible content, and learner recommendations.
Project Contribution: The project’s contribution is twofold. On an individual level, Dumatree provides a cost-free entry point for any learner interested in acquiring technical skills. The website is designed to encourage those new to technology to embark on a digital journey, directing learners who may not know where to start toward high-quality, free resources. While these resources exist elsewhere, Dumatree organizes and simplifies the selection process to remove barriers to entry. Secondly, this project adds value to non-profit organizations that currently lack computer technical programming. These organizations can implement Dumatree as a foundational tool to launch their own tech education initiatives, providing learners with the digital literacy essential in the modern job market