Woojin Jung

Woojin Jung is an assistant professor at Rutgers School of Social Work. Her research lies at the intersection of global poverty, social welfare policy, and data science. She develops multimodal, high-resolution models to predict poverty, food insecurity, and hazard vulnerability, equipping development organizations to more effectively target social transfers in data-sparse regions. Drawing on satellite imagery, social media, and geographic attributes, her work produces granular maps that improve needs assessment in some of the world's least studied settings.

Her scholarship appears in leading interdisciplinary journals and has been recognized by the Association for Computing Machinery, the Korean-American Scientists and Engineers Association (Excellence Award), the Society for Social Work and Research (Outstanding Dissertation Award), and Rutgers University (Social Innovation Award). Her research is supported by the National Science Foundation, the National Oceanic and Atmospheric Administration, Microsoft, and the Korea International Cooperation Agency. She presents regularly at major international conferences and universities worldwide.

AI/ML/Spatial Data Science in Global Development

Social Welfare Policy and Services I

Advanced Statistic Methods II: Generalized Linear Modeling and Other Advanced Methods

Research Methods I

Research Methods II

Jung, W., Kim, A. H., Sinha, A., Stoeffler., Q., Ghadimi, S., Shah, V., Garg, K., & Ammari, T. (In Press) 
Multimodal poverty mapping and geographic transfer allocation. 
Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2026.107248

Jung, W., Kim, A. H., Hung, Y., Chear, C., Shah, V., & Ammari, T. (2025, In press). Digital pulse of development: Leveraging social media discourse for poverty analysis. Information Processing & Management. https://doi.org/10.1016/j.ipm.2025.104579

Jung, W., Kim, A. H., Stoeffler, Q., Goudarzi, S., Benotsmane, R., & Shah, V. (2025). Targeting urban poverty and food insecurity: A community-informed spatial analysis and machine learning approach. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2025.106799 

Jung, W., Benotsmane, R., Stoeffler., Q., Kim, A. H., Ghadimi, S., Ntarlagiannis, D., Hosseini, M., Ammari, T., Lu, Y., & Steiner, J. (2025). Contextualized poverty targeting through multimodal spatial data and machine learning in Brazzaville, Congo. Cities. https://doi.org/10.1016/j.cities.2025.106429

Jung, W., Hung, Y., Kim, A. H., Chear, C., Shah, V., & Ammari, T. (In Press). Digital pulse of development: Generating poverty metrics from social media discourse. In Proceedings of the 5th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '25). https://doi.org/10.1145/3757887.3767680

Jung, W., Sinha, A., Kim, A. H., Shah, V., Lu, Y., Ammari, T., & Lee, L. (2025). The last mile in remote sensing poverty prediction. Association for Computing Machinery (ACM) Journal on Computing and Sustainable Societies. http://dx.doi.org/10.1145/3724422 

Jung, W., Ghadimi, S., Ntarlagiannis, D., & Kim, A. H. (2025). Using Artificial Intelligence/machine learning to evaluate the distribution of community development aid across Myanmar. Socio-Economic Planning Sciences, https://doi.org/10.1016/j.seps.2024.102139.

Jung, W., Kim, A., & Chear, C. (2024, July 17). Data Science and Social Work. Encyclopedia of Social Work. https://doi.org/10.1093/acrefore/9780199975839.013.1649

Bukhari, M. H., Shad, M. Y., Nguyen, U. D. T., Trevino, J. A. Jung, W. C., Haque, U, (2023). A Bayesian spatiotemporal approach to modelling arboviral diseases in Mexico, Trans R Soc Trop Med Hyg 2023; 0: 1–9, https://doi.org/10.1093/trstmh/trad064

Jung, W. (2023). Mapping community development aid: Spatial analysis in Myanmar. World Development, 164  https://doi.org/10.1016/j.worlddev.2022.106124
50 day-free access

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.

Algorithm-Based Strategy Shows Promise in Reducing Urban Poverty

Discovering a New Way to Measure Poverty: An approach developed by Rutgers researchers could transform how international aid and development work operate

2025 Center for Effective Global Action, Africa Evidence Summit https://cega.berkeley.edu/event/2025-africa-evidence-summit-2/

• YouTube – Day1 : https://www.youtube.com/watch?v=Le17Mju9KwU

Rutgers Research Feature: Focus on Faculty with Assistant Professor Woojin Jung

Assistant Professor Woojin Jung Receives Research Council Award

Assistant Professor Woojin Jung Receives Microsoft Azure Grant

"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.

Grants

2025-2026 (Grant Amount: $10,000) Leveraging AI/ML in Poverty Targeting & Social Safety Net Programs. Received 2,000 GPU hours (equivalent to $10,000) from NSF ACCESS to support our research projects., Woojin Jung, Agency: National Science Foundation (NSF), Role: PI

2025-2026 (Grant Amount: $20,000) Department Seed Grant, “Robust Spatial Poverty Prediction and Data-Driven Resource Allocation”, School of Social Work., Woojin Jung, Agency: School of Social Work, Role: PI

2024-2025 (Grant Amount: $10,000) “Detecting Human-Interpretable Features from Satellite Imagery to Support Poverty Mapping,” The Office for the Vice Provost for Research’s AI/ML Pilot Seed Grant., Woojin Jung, Agency: Office for the Vice Provost for Research (OVPR), Role: PI

09/2024-09/2024 (Contract) Provided expert consultation on the Korean government's strategy to promote artificial intelligence (AI) for climate action initiatives in UNFCCC in Bonn, Germany. Korea International Cooperation Agency, Role: PI/Consultant

2023-2024 (Grant Amount: $100,600, ₩136,000,000) Assessing the Vulnerability of Climate- Induced IDP Communities and Developing Climate Resilience Models in the Philippines. a total grant of $100,600 (₩136,000,000), Min-Ah Kang, Woojin Jung, and Younsung Kim, Agency: Korea International Cooperation Agency, Role: co-PI

2023-2024 (Grant Amount: $50,000), “Detecting Human-Interpretable Features from Satellite Imagery to Support Poverty Mapping,” The Office for the Vice Provost for Research’s ML/AI Pilot Seed Grant, PI: Woojin Jung https://newbrunswick.rutgers.edu/chancellor/research/seed-funding-projects

2023-2024 (Grant Amount: $25,000), “Improving Distribution of Food Aid via Trustworthy Multimodal Poverty Prediction,” Cyberinfrastructure & AI for Science and Society Seed Grant, PI: Woojin Jung

Awards

• 2026 Excellence Awardee for the Prof. Chin Ok Lee & Ms. Kwanghee Kim Award by Korean-American Scientists and Engineers Association (KSEA)

• 2025 Social Innovation Award

• 2025 Association for Computing Machinery (ACM) Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) Honorary Mention

• Scholarly Affiliate, The Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory: https://radcollaboratory.rutgers.edu/profile/woojin-jung/
• Faculty Affiliate, Rutgers Climate & Energy Institute: https://rcei.rutgers.edu/affiliate-search/
• Core Faculty, Global Health Institute at Rutgers: https://globalhealth.rutgers.edu/directory/woojin-jung/
• Faculty Affiliate, Rutgers Urban & Civic Informatics Lab: https://rucilab.rutgers.edu/woojin-jung/

February 2026

• Jung, W., Benotsmane, R., Stoeffler, Q., Kim, A. H., Ghadimi, S., Hosseini, M., Ntarlagiannis, D., Ammari, T., Lu, Y., & Steiner, J. (2026, February 19). Contextualized poverty targeting with multimodal spatial data and machine learning in Brazzaville, Congo [Paper presentation]. Data Infrastructure for Development Summit, Center for Effective Global Action (CEGA), Lomé, Togo. https://cega.berkeley.edu/event/data-infrastructure-for-development-did-2026-linking-geodata-to-decision-making-in-africa-and-beyond/

• Jung, W. (2026, February 10–12). Workshop on engaging diverse knowledge systems and workshop on methods of assessment [Invited workshop presentation]. Intergovernmental Panel on Climate Change (IPCC), University of Reading, Reading, United Kingdom. https://www.ipcc.ch/event/ipcc-workshop-on-engaging-diverse-knowledge-systems-and-ipcc-workshop-on-methods-of-assessment/

January 2026

• Society for Social Work and Research (SSWR) Pre workshop and symposium “Spatial Data Science: Visualizing and Analyzing Community Needs”

November 2025

• Association for Public Policy Management and Analysis (APPAM), Paper, “The last mile in remote sensing poverty prediction” accepted for a Panel Presentation
• Association for Computing Machinery (ACM), Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), Honorable Mention, “Digital Pulse of Development: Constructing Poverty Metrics from Social Media Discourse” (selected from over 100 submissions) 

September 2025

• Seoul National University, hosted by Institute of Social Welfare, "Targeting Urban Poverty and Food Insecurity: A Community- informed Spatial Analysis and Machine Learning Approach”

August 2025

• Invited Speaker, "Targeting Urban Poverty and Food Insecurity: A Community- informed Spatial Analysis and Machine Learning Approach," hosted by the Global Policy Lab, Stanford Doerr School of Sustainability, Aug 28th, 2025. 
• Invited Speaker, "Targeting Urban Poverty and Food Insecurity: A Community- informed Spatial Analysis and Machine Learning Approach," Wednesday lunch seminar at the Institute for Research on Labor and Employment, co-sponsored by the School of Social Welfare at UC Berkeley, Aug 27th, 2025.

July 2025

• “The Last Mile in Remote Sensing Poverty Prediction” Association for Computing Machinery (ACM) Conference on Computing and Sustainable Societies (COMPASS) 2025, at the University of Toronto, Toronto, Canada, July 22–25, 2025.

June 2025

• Contextualized poverty targeting through multimodal spatial data and machine learning in Brazzaville, Congo” The 2025 Africa Evidence Summit in Nairobi, Kenya, hosted by the Center for Effective Global Action at University of California Berkeley, June 23rd, 2025.

May 2025

• “The last mile in remote sensing poverty prediction” The 2025 Symposium on Spatiotemporal Data Science, hosted by Harvard Center for Geographic Analysis, Cambridge, MA May 24th
• “Geographic Targeting of Food Security in Urban Areas Informed by Community-Driven Insights” Conference on Integrated Health and Social Work, National Taiwan University, Taipei, Taiwan, May 29, 2025

April 2025

• Jung W. “Detecting human-interpretable features from satellite imagery to support poverty mapping” Climate-Energy-and AI Convergence Café, Rutgers University, New Brunswick, NJ, April 3rd, 2025 (lightning  talk slides)
• Jung W. “The last mile in remote sensing poverty prediction” Rutgers Artificial Intelligence/Machine Learning Collaboratory, Rutgers University, New Brunswick, NJ, April 8, 2025

January 2025

• Jung, W., Stoeffler, Q., Kim, A. H., Goudarzi, S., Benotsmane, R., & Shah, V. Geographic Targeting in Urban Areas Informed by Community-Driven Insights, 2025 Conference of the Society for Social Work and Research (SSWR). Paper presentation

• Jung, W. Contextualized Poverty Targeting through Multimodal Spatial1 Data and Machine Learning in Congo, 2025 Conference of the Society for Social Work and Research (SSWR). Poster presentation