Matthew Gee

CEO, Sr Researcher
BrightHive/University Of Chicago

Matt Gee is a Senior Research Scientist at the University of Chicago’s Center for Data Science and Public Policy and a Research Fellow at the Urban Center for Computation and Data. He is the co-founder of the Eric and Wendy Schmidt Data Science for Social Good fellowship, which over the last 5 years has paired over 200 fellows with over 75 national, state, and local government organizations and NGOs to build data-driven solutions to social problems. He is also co-founder and CEO at BrightHive Data, a social enterprise developing open source data integration platforms powering smarter government and more effective social service delivery.

Matt's applied work focuses on combining traditional methods and problems from the social sciences with new data-intensive approaches used in computer science and machine learning to drive operational efficiency and individual behavior change, and to implement adaptive policy interventions, with a focus on social service system, workforce and education, energy use, and sustainable development. He has been principal investigator on major data science initiatives funded by the National Science Foundation, the Sloan Foundation, JP Morgan Chase Foundation, the Lumina Foundation and others, developed new models for public-private data partnerships with large nonprofit, private, and public sector networks, including international development banks, national governments and agencies, as well as state and city governments. Most recently, he has led the Workforce Data Initiative, a public-private applied research initiative combining massive amounts of labor market data with new techniques in machine learning and artificial intelligence to provide new insight into rapidly evolving labor market dynamics worldwide. He has previously worked at US Treasury and has founded several companies focused on analytics, energy, and finance. He serves as an advisor to Code for America, DataKind, and the World Bank.

[Programming descriptions are generated by participants and do not necessarily reflect the opinions of SXSW.]

Programming descriptions are generated by participants and do not necessarily reflect the opinions of SXSW.