Jennifer Neville
"Are we too smart for our own good? How large-scale machine learning systems can vastly exceed human level decision-making abilities"
Loeb Playhouse: 1 - 1:45 PM
The ability to harness big data with smart algorithms and massive computing power has recently produced significant technological gains in many areas of business and society in general. This talk will give a brief history of machine learning and its relationship to the field of artificial intelligence, while providing an overview of the mathematical, computational, and algorithmic abstractions that underlie machine-learning methods. These abstractions, combined with advances in other areas of computer science, are driving the development of very large-scale machine learning systems that can vastly exceed human level decision-making abilities.
Bio: Jennifer Neville is an associate professor at Purdue University with a joint appointment in the Departments of Computer Science and Statistics. In 2012, she was awarded an NSF Career Award, in 2008 she was chosen by IEEE as one of 'AI's 10 to watch', and in 2007 was selected as a member of the DARPA Computer Science Study Group. Her research focuses on developing data mining and machine learning techniques for relational and network domains, including citation analysis, fraud detection, and social network analysis.