PROGRAM OF RESEARCH
My research aims to advance mental health equity for immigrant and ethnic minority communities and promote well-being in digital contexts through data-driven and computational methods.
Mental health challenges disproportionately impact racial/ethnic minorities and immigrant communities due to an interplay of social determinants operating at multiple levels—from individual experiences of discrimination to community-level stressors and structural barriers to care. Despite being a long-standing question in social work and behavioral health research, mental health services often fail to reach or effectively serve minority and immigrant populations.
To tackle these long-standing inequities among immigrants and racial/ethnic minorities, my research aims to:
- Apply computational methods such as natural language processing, machine learning, and geospatial analysis to uncover large-scale patterns of mental health inequity across administrative and digital data.
- Adopt a systems-level perspective to examine how policies, care infrastructures, community resources, and interpersonal discrimination converge to shape mental health outcomes.
- Integrate digital innovation into mental health equity research by analyzing how telehealth, mHealth tools, and online platforms shape patterns of inclusion and exclusion in mental health care.
- Bridge research and practice by ensuring that empirical findings lead to social change—through evidence-informed policies, culturally and linguistically responsive provider training, and equity-centered digital health design.
Across projects, I harness quantitative, geospatial, and computational social science methods—especially natural language processing—to capture these multi-level determinants of minority mental health and wellbeing. The ultimate goal is to inform the development and implementation of more targeted, responsive, and scalable interventions to eliminate disparities and ensure all individuals and communities can thrive.
Furthermore, I explore how data science and computational methods can improve social work research without causing harm. This includes examining the application and implications of data science in the field and education. How can large volumes of text data, such as clinical or case notes, social media, or administrative data, along with large language models, be applied to enhance social work research and practice? This line of inquiry emphasizes the potential for technology to contribute to, rather than detract from, the ethical advancement of social work.
Research Areas
1. Understanding Mental Health & Service Utilization among Immigrants and Ethnic Minorities: My research examines what macro- and meso-level factors (e.g., discrimination, cultural identity, social determinants, stressors) contribute to mental health outcomes and disparities among immigrant and ethnic minority populations, and how these manifest in specific health behaviors or experiences.
2. Leveraging Technology for Mental Health among Immigrants and Ethnic Minorities: I study how digital experiences, online community participation, and the digital divide impact the well-being and social capital of underserved populations, while also exploring the potential and challenges of using telemental health and digital interventions to improve service delivery.
3. Applying Data Science Methodologies to Social Work and Mental Health Research: I apply data science techniques—particularly Natural Language Processing and Large Language Models—to examine mental health narratives, help-seeking behaviors, and service access patterns, especially among immigrants and racial/ethnic minorities. My work also explores how computational methods can inform the design, evaluation, and ethical deployment of mental health interventions in social work and mental health services.
PUBLICATIONS
DISSERTATION PROJECT
My dissertation project, ‘Unraveling Ethnic Disparities: Spatial and Virtual Access to Mental Health Services among Immigrants with Language Barriers,’ focuses on the linguistic and cultural barriers that immigrants with Limited English Proficiency (LEP) face in accessing mental health services and the role of technology in overcoming these barriers. By identifying service gaps arising from structural racism, this study aims to drive structural reforms in the educational and training systems for multilingual and multicultural social workers to close the health gaps across ethnic minorities. The anticipated findings could pave the way for tailored mental health services for Hispanic and Asian immigrants with language barriers, striving towards a more inclusively equipped workforce. My dissertation uses a multi-method approach to assess linguistic accessibility in the real world at the 1) system, 2) provider, and 3) client level.
Throughout the dissertation project, I employ the public health critical race praxis (PHCRP) with a focus on 1) contemporary patterns of racial relations, 2) knowledge production, 3) conceptualization and measurement, and 4) action. In addition to the qualitative part of the study, I collaborate with the Community Advisory Board to ensure voice representation and utilize dissemination strategies such as data visualization and mapping.
Funded Grant/Award for Dissertation Research
- APA Dissertation Research Award ($5,000), American Psychological Association
- Grand Challenges for Social Work Doctoral Award ($3,000), Grand Challenges for Social Work (Funding: New York Community Trust)
- Diane Greenstein Memorial Fellowship ($5,000), NYU Silver School of Social Work
- C.V. Starr Fund for A/P/A Research ($1,000), Asian/Pacific/American Institute at NYU
- PhD Program Dissertation Research Fund ($1,000), NYU Silver School of Social Work
- Graduate Student Award for Summer Research on Migration ($1,500), NYU Migration Network
