Doing Practical Data Science for Social Good and Public Policy
Event on 2016-09-13 17:00:00
UCL Computer Science, UCL STEaPP, and UCL Public Policy present its first lecture in UCL's Distinguished Lecture Series on Data Science and Public Policy: Doing Practical Data Science for Social Good and Public Policy Rayid Ghani, Director of the Center for Data Science & Public Policy and a Senior Fellow at the Harris School of Public Policy and the Computation Institute at the University of Chicago Abstract: Can data science help reduce police violence and misconduct? Can it help prevent children from getting lead poisoning? Can it help cities better target limited resources to improve lives of citizens? We're all aware of the data science hype right now but turning this hype into any social impact takes effort. In this talk, I'll discuss lessons learned from our work at University of Chicago while working on dozens of data science projects over the past few years with non-profits and governments on high-impact public policy and social challenges. These lessons span from challenges these organizations face when trying to apply data science, to understanding how to effectively train and build cross-disciplinary teams to do practical data science, as well as what data science and social science research challenges need to be tackled, and what tools and techniques need to be developed in order to have a social and policy impact with data science. Bio: Rayid is a reformed computer scientist and wanna-be social scientist, but mostly just wants to increase the use of data-driven approaches in solving large public policy and social challenges. Rayid is also passionate about teaching practical data science and started the Eric & Wendy Schmidt Data Science for Social Good Fellowship at UChicago that trains computer scientists, statisticians, and social scientists from around the world to work on data science problems with social impact. Before joining the University of Chicago, Rayid was the Chief Scientist of the Obama 2012 Election Campaign where he focused on data, analytics, and technology to target and influence voters, donors, and volunteers. Previously, Rayid was a Research Scientist and led the Machine Learning group at Accenture Labs. Rayid did his graduate work in Machine Learning at Carnegie Mellon University and is actively involved in organizing Data Science related conferences and workshops. In his ample free time, Rayid works with governments and non-profits to help them with their data, analytics and digital efforts and strategy.
at Roberts 106LT
Roberts Building , UCL
London, United Kingdom