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DSSG Student Fellows

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Teams of student fellows will spend eight to ten weeks (usually late June through late August) working full-time on a data science project with technical mentorship from Stanford researchers and advanced graduate students. The 2024 program will be on campus, and is available only for Stanford undergraduates. To participate, students must not have conferred their undergraduate degree (even if they are a coterm student) by the summer 2024 quarter. 

Stanford Data Science (SDS) has solicited applications from project partners with social good oriented problems who need data science help. Each team will work closely with their partner, meeting weekly, and their work will culminate in a final project handoff and presentation.  During the summer, participants will have daily check-ins and mentorship meetings on their projects by faculty, research scientists, and advanced graduate students or post-docs. The program will also include technical training and discussions on project-related and data science topics. Read about last year's program here.

The 2024 program comprises two projects. The first involves statistical analysis of parentally reported food reactivity to better understand the challenges and experiences of parents with food-reactive children (see  The second involves using machine learning to augment labor exploitation prediction. Students will be assigned to projects based on their application details and skill sets. 

The fellowship’s primary goal is to create a unique, world-class data science learning experience for student fellows while making progress on real-world problems with social impact. Example projects included the following:

  • Reducing Platelet Wastage at the Stanford Blood Center
    Platelets are an expensive and limited resource with a short shelf life, leading to high wastage. Student fellows partnered with the Stanford Blood Center, and built models to predict platelet use from patient-level data.
  • Safely Prescribing Opioids in the VA Population
    Partnering with the Department of Veterans Affairs, student fellows explored trends in opioid prescriptions and adverse events for minority and underrepresented veterans in the Veterans Affairs health system.
  • Improving predictions for targeted human trafficking investigations in Brazil
    In collaboration with the Human Trafficking Data Lab at Stanford and the Brazilian Federal Labor Prosecution Office, student fellows used data science tools to build the Intuition Engine – an ensemble predictive model combining regression models, spatial data science, natural language processing, deep learning and network analyses to better detect the risk of trafficking.
  • Building a network of land ownership in Kenya
    Student fellows partnered with Code for Africa to build a database of networks of corporations and persons involved in land transfer and ownership to help journalists in Kenya in fighting corruption and promote good governance of land resources.
  • Identifying CAFO characteristics using satellite imagery
    With help from Stanford Law School’s Regulation, Evaluation, and Governance Lab, student fellows leveraged state-of-the-art advances in machine learning, artificial intelligence, and causal inference to design and evaluate programs, policies and technologies that modernize government regulations of wastewater polluters.
  • Measuring spatio-temporal change of physical conditions in neighborhoods with street view imagery
    Partnering with Changing Cities Research Lab, student fellows built a deep learning pipeline to systematically and automatically identify the physical conditions of neighborhood environments at a large scale, across multiple cities and over 10 years, using innovative street view images and crowd-sourcing data.
  • Operationalizing Equity Tiebreaker in San Francisco Student School Assignment
    Student fellows worked with The San Francisco Unified School District (SFUSD)  to develop a policy recommendation for how students are assigned to public elementary schools in SFUSD.

To be eligible for the program, fellows must be:

  • Hungry to learn and grow in the following areas: team data science, working with a project partner, statistics, reproducibility, and programming for data science
  • A current undergraduate (currently enrolled seniors/juniors) at Stanford. Students must not have conferred their undergraduate degree (even if they are a coterm student) by the summer 2024 quarter. 
  • Proficient in a programming language such as Python or R.
  • Committed to being present at all meetings during the entire duration of the summer program (40 hrs/week).

Fellows from all disciplines are encouraged to apply! Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law. Students will receive a stipend for participating in the program. See the FAQ for additional details.

For summer 2024 applicants: We will interview select applicants and make final decisions by late-March. Applicants should provide the contact information of a faculty member who could comment on their data science skills and knowledge.

Please contact with any questions. If you’d like to receive updates about the program, please subscribe to our mailing list.