For the past four years, the Data Science for Social Good summer fellowship has brought over 160 fellows from around the world to Chicago to work on data-driven projects with real world significance. They've used machine learning and predictive analytics to improve graduation rates, anticipate urban blight and water infrastructure issues, direct hazardous waste inspections, deliver targeted social services, and much more. Now, the program, run by the CI's Center for Data Science and Public Policy, is looking for fellows, mentors, project managers, partners, and funders for 2017, with applications due at the end of January.
Social science and public policy have always been connected, to varying degrees of success. In an ideal world, understanding the mechanisms behind social processes -- both good and bad -- would directly inform the creation of policies that promote social benefits and reduce consequences. But language, whether academic or political, often separates the two disciplines.
Using the University of Chicago Medicine data warehouse, a team led by CI faculty and fellow Samuel Volchenboum detected a dangerous ripple effect in hospitals: when one patient becomes critically ill, chances of a similar setback increase for others in the same unit.