Research

(a) Access to CareLanguage access in healthand social servicesLanguage-concordance desertsState policy and provider supplyDisaggregated provider data(b) Help-SeekingDigital technology and AIin help-seeking and supportGenerative AI adoptionOnline peer supportChatbots for public services(c) AI in ServicesAI adoption in health andsocial service organizationsWorkforce AI adoptionDocumentation burdenSocial work roles and trainingImmigrant and Minority Wellbeingin the age of Artificial Intelligence

RESEARCH INTERESTS

I am a social work scholar and public interest technologist. Combining years of work with immigrant and refugee organizations with training in social work and computational social science, I study mental health, well-being, and service use among immigrants and racial, ethnic, and linguistic minorities, with a focus on language and technology. Specifically, my research examines (a) access to care and support among immigrants and linguistic minorities, with attention to language access and the policies that govern it; (b) the use of digital technology and AI in immigrants’ help-seeking and support; and (c) the adoption of AI in health and social service organizations and in social work practice and education.

CURRENT PROJECTS

Language access as a structural determinant of immigrant wellbeing

Map of county-level spatial access to Spanish-language public mental health care across the United States, with service deserts shaded orange
Spatial access to Spanish-language public mental health care, by county, from the Immigrant Support Atlas.

More than 25 million people in the United States, roughly 9 percent of the population over age five, speak English less than “very well,” and that population has grown substantially since 1990[1]. Language barriers are a documented obstacle to health and social services, including mental health care, for immigrant and refugee populations, alongside cost, insurance, stigma, and the limited availability of language-concordant services[2][3]. Title VI of the Civil Rights Act of 1964 and Section 1557 of the Affordable Care Act require federally funded health programs to take reasonable steps to provide meaningful access to individuals with limited English proficiency, and the National CLAS Standards set expectations for language assistance in health care[4]. The obligation is settled. What is not well described is the distribution of the workforce that would have to satisfy it.

Digital technology and AI use among immigrant and minority populations

A woman in a hijab using a laptop and a phone at home

For immigrants and minority communities, digital technology and AI carry two possibilities at once. They can lower barriers that were fixed before, reaching people across distance, cost, and sometimes language in ways an in-person system never did. They can also reproduce exclusion, when a tool is built around a default user and quietly works less well for everyone else[6]. Both are live at the same time; which one prevails in a given case is an empirical question, and the answer turns on how a tool is built and deployed and on who was kept in mind while it was.

AI literacy among practitioners

Hands arranging a stack of labels reading Workflow, Human Review, Prompting, and AI Literacy

Practitioners are increasingly expected to work alongside AI in assessment, documentation, and decision-making[10], and that expectation is arriving ahead of the training that would let them use these tools well. Practitioners who serve immigrant and minority communities carry an added weight, because a tool that works less well for those communities does its harm precisely where the stakes are already high. Most available ethics guidance assumes an organization with the resources to interpret and apply it[11], which does not describe many of the community settings where this work happens.

Computational social science methods for social work

A tablet showing the python.org homepage with a Fibonacci function in the code sample

Social work has always relied on data, but the questions it cares about have outrun the methods it conventionally uses. Whole populations leave traces in provider directories, administrative records, and public text, and reaching them calls for tools that social work training rarely covers: natural language processing, machine learning, geospatial analysis, data visualization, and the collection of large digital datasets[13][14]. Running through my projects is a methodological thread that develops and adapts these tools for social work questions, and asks what changes when the profession takes them up.

The Immigrant Wellbeing and Technology Lab meets biweekly to work through research in progress. If you are working on these questions as a student, practitioner, or community partner, see Get Involved for how to join.