Stanford
King Center on Global Development Global Development

2025–2026 Summer Full-Time Undergraduate RFs

Projects

A Two-Pronged Approach to Dengue Prevention in Guatemala: Bayesian Modeling of Vector Control Interventions and Community Education

Health
Environment and Climate Change
Gender and Equity

This research investigates the effectiveness of vector control interventions on dengue incidence in Guatemala through Bayesian hierarchical modeling. In collaboration with the Guatemalan Ministry of Health, we analyze municipality-level dengue data (2012–2024) to assess the impact of insecticide spraying and fumigation programs while accounting for socio-environmental factors including climatic conditions and agricultural activities that serve as proxies for mosquito breeding habitats, human-vector contact, and human mobility. Environmental data will be sourced from Google Earth Engine, with temporal and spatial patterns analyzed using geospatial methods and visualized in R. This modeling approach aims to provide evidence-based insights into which vector control strategies are most effective under varying environmental conditions. In parallel, we will develop an animated, language-independent community education video on dengue transmission and prevention strategies. Recognizing that Guatemala has over 30 spoken languages and that scientific literature rarely reaches the communities most affected by dengue, this video uses visual storytelling without words to communicate prevention methods inclusively and accessibly. Together, these efforts represent complementary approaches to dengue prevention: rigorous statistical assessment of existing control measures and direct community engagement through culturally appropriate education materials.

Faculty supervisor: Erin Mordecai, School of Medicine - Biology 
Focus country(ies): Guatemala 
Research fellow: Julieta Lamm-Perez, '27, Human Biology

Analysis of Stillbirth in South Asia

Health
Gender and Equity

There are an estimated 1.9 million stillbirths globally each year, nearly as many as the number of deaths in the neonatal period, i.e., the first 28 days after birth. Despite the large burden and the fact that many stillbirths are preventable through quality antenatal and intrapartum care, stillbirths receive little attention in global policies, programs, research, and funding. In collaboration with local partners, we conducted a population-based study in five sites across Bangladesh, India, and Pakistan, where we identified and followed women of reproductive age to identify pregnancies, pregnancy outcomes, and signs of illness and risk factors for mortality among young infants. We enrolled 71,302 pregnancies and identified 2,320 stillbirths. We conducted a verbal autopsy (VA), i.e., a structured interview with the family to ascertain the context and potential contributory factors, for 1,669 of the stillbirths. Furthermore, a physician review of VAs in Pakistan has provided causes of stillbirths in these data, and we have identified additional studies that contain such information and plan to integrate them into our database. In this project, the student will apply machine learning methods to these datasets with the goal to investigate causes and factors associated with stillbirth. This will provide a novel framework for study of stillbirth that goes beyond current approaches and that can more broadly investigate stillbirth and provide insights into its causes and thus inform approaches to stillbirth prevention.

Faculty supervisor: Ivana Maric, School of Medicine - Pediatrics 
Focus country(ies): Not country-specific/regional focus only 
Research fellow: Zareef Shafquat, '28, Computer Science

Cross-Border Migration and Health: Mapping Migration, Labor Conditions, and Emergent Health Needs Among Migrant Farmworker Families

Trade and Migration
Health

Migrant agricultural worker families traveling from Mexico to California experience intersecting vulnerabilities related to labor practices, legal status, environmental exposures, and healthcare access. Migrant workers may be exposed to hazardous labor conditions, labor exploitation, climate-related illness, unmet health needs and uncertain healthcare access. Further, children in these families may experience disrupted schooling, child labor, and unique environmental health risks based on their developmental stage. This study will characterize labor exploitation dynamics, emergency healthcare needs, and the health impacts of agricultural labor among migrant worker families originating in Mexico to work in California's agricultural sector. This project employs a mixed-methods, community-engaged research design. We will first use structured literature reviews to synthesize evidence on migration pathways, and map migration pathways between Mexico and workers in Central Northern California. We will also create geospatial resource maps of agricultural labor sites, community-based support organizations, and emergency healthcare services in Mexico, California's Central Valley, and Northern California. We will pursue secondary analysis of the National Agricultural Workers Survey to assess labor violations, child presence in agricultural work, and health indicators. We will then create community partnerships with organizations working to support migrant worker families in Mexico, Central Valley and Northern California. With these organizations, we will co-design multilingual WhatsApp-based surveys to capture real-time information on child labor, schooling, emergency health needs, and environmental and climate-related health impacts. Findings will be reviewed with community partners to ensure contextual accuracy and relevance, with the goal of informing future interventions, policy, and healthcare delivery strategies for migrant agricultural worker families.

Faculty supervisor: Preeti Panda, School of Medicine 
Focus country(ies): Mexico 
Research fellow: Ashley Galeana Dominguez, '27, Earth Systems

Drones and Dengue Indonesia

Innovations in Methods and Data

The incidence and distribution of dengue, chikungunya, and Zika viruses has increased exponentially in recent decades and this trend is projected to continue, driven by climate change and plastic pollution. Aedes aegypti mosquitoes are the primary vector for these three viruses and identification of Aedes aegypti breeding sites in trash and water holding containers are key for reducing vector transmission. The student project will work on our Drones and Dengue project using machine learning algorithms to help develop a model to identify trash and breeding sites from drone imagery in our Indonesia sites.

Faculty supervisor: Joelle Rosser, School of Medicine - Medicine Department 
Focus country(ies): Indonesia 
Research fellow: Ruikuan Zhu, '29, Mathematics

Economic Development in the MENA Region

Governance and Institutions
Urbanization and Infrastructure
Trade and Migration

What explains economic development trajectories for countries in the Middle East and North Africa (MENA)? In this project, we will be exploring patterns of economic development across and within MENA countries. Student researchers will be responsible for collecting and cleaning data; running analysis; and writing literature reviews on related topics. Arabic language skills preferred but not required.

Faculty supervisor: Lisa Blaydes, H&S - Political Science Department 
Focus country(ies): Morocco 
Research fellow: Jana Alsolamy, '29, Undeclared

Forest Fragmentation, Biodiversity Loss, and Zoonotic Disease Risk in the Amazon

Health
Environment and Climate Change
Urbanization and Infrastructure

Rapid forest loss and habitat fragmentation across the Amazon are reshaping ecosystems that directly influence human health in the region's low- and middle-income countries. Many infectious diseases affecting Amazonian populations are zoonotic, with transmission driven by non-human host species whose distributions are sensitive to environmental change. Despite growing concern about biodiversity loss and emerging disease risk, no Amazon-wide analysis has systematically examined how forest fragmentation alters the distributions of known disease host species across the Amazon. This project will provide the undergraduate summer research fellow with hands-on experience addressing a core global development challenge at the intersection of environmental change and public health. The student will compile and harmonize publicly available data on known zoonotic disease host species using existing host–pathogen databases and biodiversity repositories; develop species distribution models to characterize how forest fragmentation and land-use change shape host habitat suitability across the Amazon; conduct counterfactual analyses that simulate alternative development and forest-fragmentation pathways to estimate their impacts on host species distributions; and where data permit, overlay modeled host distributions with human disease case data for pathogens such as Oropouche virus, leishmaniasis, and yellow fever. Together, these analyses will generate policy-relevant insights for conservation planning, disease prevention, and health equity in Amazonian regions while providing the student with rigorous training in spatial analysis, ecological modeling, and global health research.

Faculty supervisor: Erin Mordecai, H&S - Biology Department 
Focus country(ies): Brazil 
Research fellow: Harper Baer, '27, Mathematics

From Contacts to Contracts: Rebuilding Social Capital for Refugee Entrepreneurs — A Triadic Chat Design Testing Behavioral Mechanisms of Tie Formation

Work, Entrepreneurship, and Finance
Education and Skills
Gender and Equity

This project examines how refugee entrepreneurs can rebuild social networks after displacement through a randomized field experiment in Uganda. We will test whether tie initiation (helping entrepreneurs make first contact) or tie maintenance (sustaining follow-up) is more effective for forming lasting business relationships. During summer 2026, approximately 450 refugee entrepreneurs in Palabek and Adjumani will participate in a 3-day bootcamp with rotating mentors, followed by 4 weeks of digital support via WhatsApp. Participants will be randomly assigned to receive either tie initiation prompts, tie maintenance prompts, or neutral messages through a triadic chat system (mentor-mentee-bot). The research fellow will help implement the intervention, monitor WhatsApp chat data in real-time, conduct surveys with participants, and analyze outcomes including new ties formed, conversation continuity, and entrepreneurial progress. This project contributes to understanding how to design effective entrepreneurship programs for displaced populations who start with minimal existing networks, with implications for refugee support programs globally.

Faculty supervisor: Charles Eesley, SoE - Management Science and Engineering Department 
Focus country(ies): Uganda 
Research fellows: Anika Turcotte, '28, Management Science and Engineering; Beyza Kaya, '29, Undeclared

Infectious Diseases, Environment, Global Health, and Climate Change

Health
Environment and Climate Change

The research assistant can contribute to different ongoing activities within the lab based on their interests. Our mission is to improve population health by creating high quality evidence about what health interventions work in whom and where, when, and how to implement them. Most of our research is focused on infectious diseases, including malaria, diarrhea, soil-transmitted helminths, and influenza. We use a variety of epidemiologic, computational, and statistical methods, including causal inference and machine learning methods, in pursuit of our mission. Ongoing research projects include a randomized trial testing the effect of replacing soil floors with concrete on maternal and child health in Bangladesh; quantifying the relationship between different housing features in low-income countries and health outcomes; using causal inference models to determine where preventive malaria interventions are most effective; and developing novel metrics for climate adaptation and resilience at the household level in low- and middle-income countries.

Faculty supervisor: Jade Benjamin-Chung, School of Medicine - Epidemiology and Population Health 
Focus country(ies): Not country-specific/regional focus only 
Research fellow: Cara Sowa, '28, Management Science and Engineering

Interventions to Counter Extreme Heat: Development, Implementation, Evaluation, and Creating a Roadmap for Sustainability in Climate-Vulnerable Informal Settlement Communities

Environment and Climate Change
Urbanization and Infrastructure
Health

Due to climate change, extreme heat is becoming widespread. The effects of heat on human health are not well understood and informal settlement regions often lack the resources to adapt to extreme heat. This study aims to quantify the health effects of extreme heat exposure for individuals living in coastal informal settlements in Makassar, Indonesia using data collected by wearable devices, environmental sensors, and survey instruments. Survey, sensors, and wearable data collection will occur within enrolled households. A selection of households will receive a reflective roof paint intervention which aims to cool indoor temperatures. Data will be analyzed to better understand the effects of extreme heat on work, sleep, and daily life, and to gauge the effectiveness of the reflective roof intervention. The results, alongside community partnerships in Makassar, will be used to further develop sustainable and scalable interventions to counter extreme heat.

Faculty supervisor: John Openshaw, School of Medicine - Medicine Department 
Focus country(ies): Indonesia 
Research fellow: Rachael Gold, '28, Biomedical Computation

Land Use Decision-Making in the Bolivian Amazon: The Case of Ranchers

Environment and Climate Change

This research employs a case study approach in Eastern Bolivia to better understand the social-ecological factors — including climate change, land succession, migration, and globalization — that are impacting and possibly restructuring patterns of land use and land control in the Amazon Basin. Through semi-structured interviews with ranchers and the development of a regional survey, this project aims to better understand how this understudied and diverse group of actors is making decisions about natural resource management and the implications of these decisions for the social-ecological future of the region. Cattle ranchers are an overlooked group in the field of critical geography, yet these actors manage vast territories across the Amazon Basin and thus have potential to mobilize land use transformations as they seek to respond to diverse social-ecological stressors, including climate change and land succession. This project will examine the factors driving the intensification and expansion of commodity agricultural production in the Amazon Basin and how ranchers are responding to this trend, including through the diversification of their production systems. This research will provide key insights regarding land managers' decision-making and social-ecological trade-offs in agricultural commodity frontiers to inform pathways and policies that galvanize and support the social-ecological future of the Amazon Basin.

Faculty supervisor: Nicole Ardoin, Doerr - Social Science Division 
Focus country(ies): Bolivia 
Research fellow: Alexis Nunez, '27, Sociology

Octopi: AI-Powered Diagnostic Platform for Detecting Malaria and Other Diseases in Low-Resource Settings

Health
Innovations in Methods and Data
Environment and Climate Change

Nearly every minute, a child dies of malaria. With cases rising annually and growing resistance to existing diagnostics and treatments, there is an urgent need for better detection tools. Current diagnostics face critical limitations: microscopy requires skilled personnel and misses low-density infections; rapid diagnostic tests cannot quantify parasite load or detect emerging variants; and molecular methods like qPCR are too expensive for field use. To address these gaps, the Prakash Lab developed Octopi, an AI-powered automated microscopy platform. In collaboration with clinical partners in Africa, Asia, and the US, we have tested the platform for detecting malaria, sickle cell disease, and tuberculosis. In preliminary clinical validation studies conducted in Uganda and the US, we demonstrated that our novel fluorescence microscopy technique can analyze roughly half a million cells per minute, achieving patient-level sensitivity and specificity exceeding 97% for detecting P. falciparum, the deadliest malaria species. This internship will contribute directly to expanding Octopi's diagnostic capabilities to develop machine learning models, annotate clinical images, and collaborate with international partners. There may be opportunities to participate in field validation at clinical sites in Africa, Asia or Latin America.

Faculty supervisor: Manu Prakash, H&S - Biology Department 
Focus country(ies): Tanzania 
Research fellow: Emily Chu, '27, Biology

Reconciling Gaps in Typhoid Surveillance: Integrating Serologic and Environmental Data to Map True Disease Burden in Sub-Saharan Africa

Health
Innovations in Methods and Data
Urbanization and Infrastructure

Typhoid fever remains a major public health challenge in Côte d'Ivoire, where limited access to safe water and sanitation sustains transmission and conventional surveillance systems substantially underestimate disease burden. National surveillance relies primarily on blood culture–based case detection, which is difficult to implement outside urban areas and has limited sensitivity among children and individuals with prior antibiotic exposure. Recently, novel surveillance approaches have emerged to address these gaps. Community-based seroepidemiologic methods using antibodies to Salmonella Typhi hemolysin E (HlyE) enable estimation of recent infection, while bacteriophage detection in wastewater provides large-scale, non-invasive environmental surveillance of S. Typhi circulation. However, early application of these methods in southern Côte d'Ivoire has yielded conflicting results of typhoid transmission, complicating burden estimation. This project leverages spatiotemporal analysis to reconcile these data streams and contextualize transmission dynamics. Using georeferenced seroincidence estimates and environmental phage sampling locations, this project will apply spatial clustering, hotspot detection, and other geospatial methods to identify areas of concordance and divergence between human infection markers and environmental reservoirs. These patterns will be further integrated with population density, seasonal rural–urban mobility, and hydrological data to create an integrated spatiotemporal model of typhoid transmission. This work aims to better estimate true typhoid burden in areas where clinical surveillance alone is insufficient, and will inform more equitable targeting of typhoid conjugate vaccines, guide water and sanitation interventions, and provide a scalable model for integrating heterogeneous surveillance data to improve infectious disease burden estimation in resource-limited settings.

Faculty supervisor: Jason Andrews, School of Medicine - Medicine Department 
Focus country(ies): Côte d'Ivoire 
Research fellow: Max Yang, '27, Human Biology

Risk for Hospitalization and Mortality of Young Infants Based on Machine Learning Analysis of Demographic and Clinical Data from South Asia and Sub-Saharan Africa

Innovations in Methods and Data
Health
Gender and Equity

Community health workers (CHWs) play a critical role in low- and middle-income countries in identifying and managing sick young infants. The World Health Organization (WHO) has developed clinical algorithms — known as Integrated Management of Childhood Illness — to guide CHW clinical assessments and management. We are collaborating with WHO and have assembled and harmonized global data from multiple sites in South Asia and sub-Saharan Africa for analysis of risks for hospitalization and mortality associated with various clinical signs identified in young infants under 2 months of age. We have conducted initial, novel machine learning analysis and will complete machine learning and time-varying Cox regression analyses of the data to predict risk for hospitalization and mortality. Results will be used to inform WHO global recommendations for the identification and management of sick young infants. This is the first study to apply machine learning analytical approaches to global data on risks for hospitalization and mortality of young infants.

Faculty supervisor: Gary Darmstadt, School of Medicine - Pediatrics Department 
Focus country(ies): Not country-specific/regional focus only 
Research fellow: Lucas Pu, '29, Biomedical Computation