2026-27 AY PT Research Fellowship: Machine Learning Analysis of Clinical Signs with Mortality of Young Infants in South Asia and Sub-Saharan Africa
The Stanford King Center on Global Development’s Academic Year Part-Time Research Fellowship Program connects King Center faculty affiliates and affiliated researchers with undergraduate students committed to providing research support during autumn, winter, and spring quarters.
Students have the opportunity to engage in world-class research that has real-world impact. Undergraduate student research fellows are paid $19/hour*. Students must be enrolled full-time to participate and must be able to commit to research 8-10 hours per week.
*Students must attend orientation and submit an I-9 form to verify employment and receive payment. Students who cannot accept pay may be allowed to receive academic credit for this research.
Research Project Description:
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 global data from studies across multiple countries in Asia and Africa on the risks for 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. Results will be used to inform modifications of WHO global recommendations for the identification and management of sick young infants. This is the first study to assemble global data on clinical signs and their association with mortality of young infants, and to apply advanced machine learning analytical approaches to gain further insights into how to most effectively recognize and management sick young infants in low- and middle-income countries. We are looking for a highly motivated student with experience in data analysis to work on this exciting and impactful project. Our aim is to produce two peer-reviewed publications of our findings.
Primary Research Mentor: Ivana Maric
Co-Research Mentor: Gary Darmstadt and Sue Alvarez
Stanford undergraduate students in good academic standing and enrolled full-time are eligible to apply. Co-term students must have undergraduate student status; if co-terms are in graduate billing status (after 12 quarters) they are ineligible to participate.
All majors are welcome!
Students Responsibilities:
- Data Cleaning
- Work in R and Python
- Participation in and presentation of progress at regular (weekly) team meetings with Stanford mentors and WHO staff.
- Data analysis, including Cox regression and machine learning.
- Participation in preparation of manuscript(s).
Students qualifications:
- Experience in data management and analysis, including proficiency in R and preferably experience with regression and machine learning methods.
Time Commitment:
The time commitment is 8-10 hours per week (equivalent to a 3-unit course) each academic quarter. The expectation is that students will work the full academic year with their mentor (Autumn, Winter, and Spring quarters). Students planning on studying abroad may not be eligible.
To Apply:
Along with the application, applicants are asked to submit:
- a cover letter
- resume or CV
- unofficial Stanford transcript (first quarter frosh do not need to submit transcripts for autumn quarter applications)
Research Mentor Questions for Applicants:
