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. 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 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.
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) or graduate student (Masters) at an accredited US based university or college
- 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). The 2022 program is virtual (via Zoom) due to COVID-19 travel restrictions.
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.
Apply here for the 2022 summer program June 20th through August 12th, 2022. Applications are due Feb 14, 2022.
For summer 2022 applicants: We will interview select applicants and make final decisions by mid-April. Applicants should provide the contact information of a faculty member who could comment on their data science skills and knowledge. We may request recommendation letters for selected applicants.