Faculty & Staff
Assistant Professor; Core Faculty, Global Health Institute at Rutgers
Ph.D., UC Berkeley; MSW, Washington University in St. Louis; MPP, Harvard University
Dr. Jung's research focuses on development aid policy as an instrument for alleviating global poverty. She applies data science techniques to examine the relationship between poverty and aid, particularly in fragile states. Her research uses three lenses to evaluate the extent to which aid activity is suited to the needs of aid recipients:
- a nuanced poverty assessment at the global level;
- a case analysis of project design at the country level; and
- a granular evaluation at the community level.
In her recent work, she combines artificial intelligence with satellite imagery to measure poverty and highlight latent features relating to aid distribution. Her work has been advanced through a Ph.D. in Social Welfare with designated emphasis on Development Engineering at UC Berkeley with the support of the National Science Foundation Innovations at the Nexus of Food, Water, and Energy Systems Fellowship. She has published in interdisciplinary development journals, and served on the editorial board of and ad-hoc reviewers for peer-reviewed journals. Her experience with multi-/bilateral development agencies (UNICEF NY, KOICA), research institutes (Harvard Malcolm Wiener Center for Social Policy), and grassroots organizations (Nari Gunjan) in Asia and Sub-Saharan Africa informs her research and teaching. In March 2021, Dr. Jung received an Azure grant for AI for Humanitarian Action from Microsoft for which she will serve as PI.
Advanced Statistic Methods II: Generalized Linear Modeling and Other Advanced Methods
Research Methods I
Research Methods II
Selected Recent Publications:
Jung, W. (2022) The Discrepancy Between Two Approaches to Global Poverty: What Does it Reveal?. Social Indicators Research. https://doi.org/10.1007/s11205-021-02866-6
Jung, W. (2021). Becoming One: Religion, Development, and Environmentalism in a Japanese NGO in Myanmar. By Chika Watanabe. Pacific Affairs, 94(4), 781-783.
Jung, W. (2020). Two Models of Community-centered Development in Myanmar. World Development, 136. https://doi.org/10.1016/j.worlddev.2020.105081
Jung, W. (2020). Using data science to strengthen the social safety net: Predicting risk for Medicare and Medicaid insurers. Journal of Governmental Studies. 26(2) https://dx.doi.org/10.19067/jgs.2020.26.2.29
Organista, K. C., Jung, W., & Neilands, T. (2020). A structural-environmental model of alcohol and substance-related sexual HIV risk in Latino migrant day laborers. AIDS and Behavior. https://doi.org/10.1007/s10461-020-02876-4
Wu. C-F, Chang, Y-L., Rhodes, E., Musaad, S. & & Jung, W. (2020), Work-Hour trajectories and associated socioeconomic characteristics among single-mother families. Social Work Research. 44(1), 47-57. https://doi.org/10.1093/swr/svz029
Organista, K., Jung, W., & Neilands, T. (2019). Working and living conditions and psychological distress in Latino Migrant Day Laborers. Health Education & Behavior, 46(4), 637-647. https://doi.org/10.1177/1090198119831753
Kim, P. H., & Jung, W. (2018). Ownership and planning capacity in the Asian-style development cooperation: South Korean Knowledge Sharing Program to Vietnam. Korea Observer, 49(2), 349-368. https://doi.org/10.29152/KOIKS.2018.49.2.349
Kharas, H., Makino, K., & Jung, W. (Eds.). (2011). Catalyzing development: a new vision for aid. Brookings Institution Press.
In the News:
"Development Engineering Scholar Woojin Jung Finds Significant Discrepancies in Global Poverty Measures" in Blum Center for Developing Economies. https://blumcenter.berkeley.edu/news-posts/development-engineering-scholar-woojin-jung-finds-significant-discrepancies-in-global-poverty-measures/
Exploring Scalable Multimodal Approaches to Identify Vulnerable Populations in the Congo
Woojin Jung, PhD, MPP, MSW, Rutgers School of Social Work
Grant Category: Research
Collaborative Partners: Microsoft, World Food Programme
This project will use artificial intelligence technologies to more accurately and rapidly identify areas of extreme poverty in the Republic of the Congo, informing humanitarian responses to the country’s surging food insecurity in the wake of COVID-19. The research will incorporate daytime satellite imagery, nighttime luminosity, and social media data to create algorithms that estimate the wealth and livelihood of geographic regions. The robust and objective information that is produced will allow for more precise targeting of social safety net programs.