Thursday, February 14, 4:00 p.m.
Where: Rashid Auditorium
4401 Gates and Hillman Centers
Obama for America 2012
Machine Learning/Google Distinguished Lecture
The Role of Data, Technology, and Analytics in the Presidential Elections
If you're still recovering from the barrage of ads, news, emails, facebook posts, and newspaper articles that were giving you the latest poll numbers, asking you to volunteer, donate money, and vote, this talk will give you a look behind the scenes on why you were seeing what you were seeing. I will talk about how the Obama Campaign used analytics to improve decision making in virtually every function within the organization and describe how data from a variety of sources was used to improve fundraising, volunteer activities, and voter contacts. We will cover what kind of data was available to the campaign, what technologies were developed and/or used, and how the resulting products were adopted by the campaign in order to help win the presidential elections. Although the focus will be on the elections and politics, we'll also talk about lessons learned during the campaign and how some of the same techniques can be used by other organizations to make them more successful through smarter use of data and analytics.
Rayid Ghani was the Chief Scientist at Obama for America 2012 campaign focusing on analytics, technology, and data. His work in the campaign focused on improving different functions of the campaign including fundraising, volunteer, and voter mobilization using analytics, social media, and machine learning. Before joining the campaign, Rayid was a Senior Research Scientist and Director of Analytics research at Accenture Labs where he led a technology research team focused on applied R&D in analytics, machine learning, and data mining for large-scale & emerging business problems in various industries including healthcare, retail & CPG, manufacturing, intelligence, and financial services. In addition, Rayid serves as an adviser to several start-ups in Analytics, is an active organizer of and participant in academic and industry analytics conferences, and publishes regularly in machine learning and data mining conferences and journals.
Faculty Host: Tom Mitchell
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