Presented By O'Reilly and Cloudera
Make Data Work
Sept 29–Oct 1, 2015 • New York, NY
Matthew Gee

Matthew Gee
Principal/Senior Research Scientist, Impact Lab/University of Chicago

Website

Matthew Gee is cofounder and principal at the Impact Lab, a data-analytics company focused exclusively on developing scalable data science solutions to social-sector problems. He is also 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. Matt is the cofounder of the Eric and Wendy Schmidt Data Science for Social Good fellowship, which in its first three years has paired 126 fellows with over 40 national, state, and local government organizations and NGOs to build data-driven solutions to social problems.

Matt’s applied work focuses on combining methods and problems from the social sciences with machine-learning methods and new data sources to drive operational efficiency and individual behavior change and to implement adaptive policy interventions, with a focus on energy use, sustainable development, urban systems, and local labor market dynamics. He has lead major data science initiatives with large public-sector clients, including the World Bank, national governments and agencies (Mexico, USA), state governments (California, Illinois), and cities (San Francisco, Chicago, Memphis), as well as large nonprofit organizations and for-profit companies. Matt serves as an advisor to Code for America, DataKind, and the Chicago School of Data and is a member of the World Bank’s Partnership for Open Data. He has previously worked at the US Treasury’s Office of Energy and Environment and has founded several companies focused on analytics, energy, and finance.

Sessions

9:00am–12:30pm Tuesday, 09/29/2015
Business & Innovation
Location: 3D 05/08
Marie Beaugureau (O'Reilly Media, Inc. ), Paco Nathan (derwen.ai), Tim Berglund (Confluent), Edd Wilder-James (Google), Matthew Gee (Impact Lab/University of Chicago ), Yael Garten (LinkedIn), Katie Kent (Galvanize)
Average rating: ***..
(3.78, 9 ratings)
Whether starting a data science program, reaching the breaking point with your current data technology, or figuring out what the competition is up to, these sessions will give you a bird's-eye view of data technologies, techniques, and data-driven organizations. Read more.
11:00am–11:30am Tuesday, 09/29/2015
Data 101
Location: 3D 05 / 08 Level: Non-technical
Matthew Gee (Impact Lab/University of Chicago )
Average rating: ***..
(3.56, 18 ratings)
Machine learning algorithms are the workhorses of the data economy, but often seem like one part math and two parts magic. In this session, we'll demystify core concepts in machine learning, give practical examples of applications, and walk through some basic rules for deciding if your organization’s key questions and data sources are a good fit for a machine learning solution. Read more.