2025 Data Science for Social Good

The Data Science for Social Good summer program trains aspiring researchers to use data science for social impact. In partnership with governments and nonprofits, participants address real-world challenges in areas such as education, health, energy, and economic development. A diverse cohort of students spends the summer on campus working closely with the program.

DSSG Leadership Team

Balasubramanian "Naras" Narasimhan is a senior advisor and research data scientist in Stanford Data Science, Statistics, and Biomedical Data Science Departments. 

Haojie Wang is a Postdoctoral Fellow at Stanford Data Science. His research focuses on the development of intelligent earth observation approaches for global population health monitoring in low-resource regions. He is also interested in applying data science techniques to address research challenges related to labor trafficking, natural hazard forecasting, risk assessment, and climate change. Outside of work, he is passionate about cooking, hiking, and exploring new places through travel. At Stanford DSSG, Haojie served as the head organizer in the Summer 2025 and as a technical mentor in Summer 2023.

Noah Cowan is a PhD candidate in Statistics at Stanford University. His research focuses on developing robust and reliable machine learning algorithms, with a particular emphasis on enhancing our understanding of the brain. He graduated from Brown University with a degree in Chemical Physics, worked in cybersecurity with Darktrace, and now focuses on using optimization for good. Outside of work, he loves to cook, play tennis, and learn random facts

Predicting Global Health Using Satellite Imagery

One-page Summary: DSSG One-page Summary

Final Presentation Video: 2025 Stanford Data Science for Social Good

Final Report: DSSG__Project_Final_Report.pdf

Slides: Predicting Global Health Using Satellite Imagery Final Slides.pdf

Traditional data on maternal and child health (MCH) relies on nationally representative household surveys, which are often costly and infrequently conducted in low and middle-income countries. Our project explores the use of machine learning with satellite imagery and other remotely sensed variables to estimate these key MCH indicators, such as women’s mean BMI, skilled birth attendant rate, under-5 child mortality rate, and more. We integrate this MCH information from Demographic and Health Surveys (DHS) with geospatial information from Google Earth Engine and imagery from Landsat and Sentinel satellites. In order to predict MCH information, we build complementary pipelines: a tabular pipeline that combines DHS data with geospatial features from Google Earth Engine, and a satellite pipeline that applies convolutional neural networks and vision transformers to raw Landsat imagery. Beyond building accurate models, our goal is to evaluate where and when these approaches generalize best across countries, years, and local contexts. By assessing both performance and limitations, this work advances the potential of satellite-informed prediction as a tool for global health monitoring and equity.

Technical Mentor: Haojie Wang 

Fellows

Esha Thapa is graduating with her B.A. degree in Data Science & Social Systems and is planning to pursue her coterminal Master’s degree in Computer Science. She is interested in critically understanding how information is collected and used, particularly at the intersection of human rights and technology, where she hopes to explore how data systems can advance equity. Outside of school, Esha enjoys dance, film photography, and scrapbooking.

Ness Arikan is a senior studying Data Science and Social Systems, concentrating in Brains and Behavior. She is interested in using her skills to better understand human behavior and affect to have a positive impact within the social sciences. She has a diverse academic palate from neuroscience to policy, but is particularly interested in rigorous causal studies, science communication, and finding ways to bridge the gap between scientific research and public understanding to inform better decision-making.

In her free time, she improvises with the Stanford Improvisors, organizes events for her residents in East FloMo, and takes care of her many plants.

Allana Moore is a junior studying Data Science and Social Systems with a focus on using data science for social impact. She is interested in applying data science to communications and investigative journalism, using evidence-driven insights to uncover truths, shape public understanding, and inform decision-making that addresses socioeconomic inequality. Beyond her academic and professional interests, she is passionate about health, fitness, and wellness, and enjoys design and reading.

Data Science On The Road: Car Collisions and Life Expectancy

Motor vehicle collisions pose a significant public health burden, yet their long-term effects on individuals remain understudied. To better quantify this impact, we analyzed Medicaid and MarketScan insurance claims data to examine the populations involved in motor vehicle collisions and estimate their health consequences. We applied the World Health Organization’s Disability-Adjusted Life Years (DALY) framework, focusing specifically on Years Lived with Disability, to capture the non-fatal effects of injuries. Our analysis highlights both the scope of individuals affected across datasets and the extent of disability experienced following collisions. While DALY provides a useful lens for measuring the burden of motor vehicle collisions, our findings suggest opportunities to refine this metric and to explore complementary approaches for assessing the full impact of collisions on health and quality of life.

Technical Mentor

Yi-Ting Tsai is a PhD candidate in Computational and Mathematical Engineering at Stanford University. Her research focuses on developing mathematical and probabilistic models in the field of phylogenetics. Outside of research, she enjoys spending time with friends and finds joy in traveling, especially to national parks.

Fellows

Xander Russell is a Senior at Stanford studying Data Science and Mathematical & Computational Science. He is passionate about applying statistical modeling and optimization to transform complex data into actionable insights.

Pankhuri Dayal is a senior from Hong Kong studying data science, psychology, and German. Her work focuses on the applications of data science to healthcare and psychology, specifically in building tools to enhance diagnostic capabilities and therapy accessibility. At Stanford, she has been a part of the Language and Cognition Lab, Behaviour Design Lab, the Bridge (a peer counseling service), Undergrad Psych Society, and Undergrad Research Association. In her free time, you can find her weekend tripping to another city, watercolour painting in the sun, wooing a cat, experimenting with Indian fusion dishes, or plotting her move to Berlin.