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The Data Science for Social Good summer program trains aspiring researchers to work on data science projects with social impact.  Working closely with governments and nonprofits, participants take on real-world problems in education, health, energy, public safety, transportation, economic development, international development and more. Participants include a diverse and inclusive cohort of students who spend the summer on campus working with the program.

CALL FOR APPLICATIONS

The Data Science Initiative is excited to invite applications for our inaugural Stanford Data Science for Social Good Summer Fellowship program!  The program is a new and unique opportunity to help improve pressing societal challenges using cutting-edge tools and methods. 
 
This opportunity will bring together teams of student fellows (Stanford undergraduate and graduate students) with technical mentors (advanced Stanford graduate students) to work on a challenge with significant social impact under the guidance of Stanford faculty and senior researchers.
 
The fellowship’s main goal is to create a unique, world-class data science learning experience for student fellows while helping to provide positive social impact by working on real-world problems. Potential projects include the following:
 
California Wildfires
Wildfires have taken lives and razed homes in California and other US States, but we know little about the health burden of wildfire smoke exposure on public health. Student fellows will work with scientists, policy makers, and public health officials to canvas the wildfire problem, using a data-driven approach, and use these findings to coordinate a more proactive and unified response to wildfire risk across different California counties.
 
Safely Prescribing Opioids
Modifying current opioid prescribing practices is an important strategy for improving population level health outcomes.  However, a recent NIH expert panel concluded that providers have insufficient evidence for the use of opioids. Partnering with the Department of Veterans Affairs, student fellows will use data science tools and methods to support safe and effective prescription practices.
 
The fellowship will run from Monday, July 15th to Friday, September 13th at Stanford University. Participants must be present at Stanford during those dates. Fellows and mentors will both be compensated as detailed below.

Student Fellows

Applications are due on 4/1 at 5:00pm
 
Fellows collaborate on interdisciplinary teams to tackle a data science problem. Fellows will also be selected based on demonstrated commitment to data science with social impact and overall enthusiasm for engaging deeply with the program. Fellows must be a undergraduate, a graduate student, or a postdoc at Stanford. Fellows from all disciplines are encouraged to apply. Fellows will receive a stipend for participating in the program.
 
In order to apply, please use the form linked above to submit your CV and a single page Letter of Intent that describes your interest in becoming a Stanford Data Science for Social Good Summer Fellow and how you might use the opportunity.  We strongly encourage you to provide links to any publicly accessible code and visualizations that you have developed and/or articles or websites that describe efforts that you have led.

Technical mentors

Applications for this opportunity are closed.
 
Each team will have a technical mentor that supports the project and fosters a meaningful learning experience for the fellows. Being a technical mentor requires the unique ability to rapidly switch between technical educator, project manager, and team facilitator. Technical mentors will work full-time throughout the summer spending their time on activities such as side-by-side coding or running interactive training sessions on fundamental skills like using git. Accordingly, technical mentors need to be deeply committed to the program and view it as their full-time job throughout the summer. Technical mentors will receive a 50% RA during the spring quarter and a 100% RA during the summer.
 
Please contact datascience@stanford.edu with any questions.