Scalable Multimodal Approaches

Identifying Vulnerable Populations in

The Republic of Congo

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Artificial intelligence (AI) and machine learning (ML) techniques can generate unbiased geographical data and identify areas of extreme poverty, which policymakers can adopt to prioritize development aid allocations. This innovative method can help develop scalable algorithms to inform humanitarian responses to Africa's food crisis. In the Republic of the Congo (RoC), one out of two people live below poverty, and the pre-COVID level of food insecurity of 10% has surged to 30% during the pandemic. Thus far, governments have responded to this crisis by doubling social protection programs. However, the current targeting methods do not provide enough information on eligible communities and are too slow to mitigate transitory shocks. This issue highlights the need for a systemic, fine-grained, and rapid geographic targeting technique. 

This current project aims to develop a multimodal approach that combines daytime satellite imagery, nighttime luminosity, and social media to estimate the wealth and livelihood of regions and shape aid distribution.

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FOOD INSECURITY IN CONGO PRE-COVID

FOOD INSECURITY IN CONGO DURING COVID

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FOOD

SECURITY

90%

FOOD

INSECURITY

10%

30%.png

FOOD

INSECURITY

30%

FOOD

SECURITY

70%

Republic of Congo Food Insecurity.jpg

Research

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Dr. Jung's latest research project aims to develop a multimodal approach that combines daytime satellite imagery and social media to estimate the wealth and livelihood of regions and shape aid distribution.

              PARTNERS             ....................................................

Global Health Institute.png

Recipient of the Global Health Institute's Global Health Seed Grant

              PARTNERS             ....................................................

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Recipient of Microsoft's
AI for Humanitarian Action Project Initiatives'  $25,000 Azure Grant 

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PARTNERS
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Image by Annie Spratt

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Dr. Jung’s research is expected to have high global impact by offering enhanced data for key development actors to reach people and regions with extreme poverty. This project's outputs, such as high-resolution poverty maps, will be shared with the WFP and the Ministry of Social Affairs to scale up social protection programs. These maps can be updated as new data becomes available to decision-makers in near real time. This work could potentially address misinformation and current criticisms of AI that miss out on the bottom billion, thereby addressing diversity, equity, and inclusion.

            RESEARCH TEAM       .................................................................​​

The team consists of computer/data scientists

and social scientists:

  • Woojin Jung (PI) - Rutgers, School of Social Work

  • Jordan Steiner - Rutgers, School of Social Work

  • Alisha Cupid - Rutgers, School of Social Work

  • Melanie Yu - Rutgers, School of Social Work

  • Amanda Starcev - Rutgers, School of Social Work

  • Charles Chear - Rutgers, School of Social Work

  • Eunice Park - Rutgers, School of Social Work

            RESEARCH TEAM       .................................................................​​

  • Matthew Purri - Rutgers, School of Engineering

  • FangZheng Wu - Rutgers, School of Art & Sciences

  • Javier Velasquez - Rutgers, School of Communication & Info.

  • Luan Tran - Rutgers, School of Engineering

  • Lily Lin - Microsoft

  • Koubouana Felix - Marien Ngouabi University

      3 STAGES        ..........................................................

  1. Assess the current machine learning model

  2. Develop new algorithms with a wider application

  3. Share this new field-tested and evaluated approach with policymakers

Research Duration
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July 2021 - June 2022

The study is slated to conclude in June 2022 with the dissemination of findings to policymakers.