Lise Getoor is a professor in the Computer Science Department at the University of California, Santa Cruz and director of the UCSC Data Science Research Center. Her research areas include machine learning, data integration and reasoning under uncertainty, with an emphasis on graph and network data. She has over 250 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, serves on the board of the Computing Research Association (CRA), and was co-chair for ICML 2011. She is a recipient of an NSF Career Award and twelve best paper and best student paper awards. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a professor in the Computer Science Department at the University of Maryland, College Park from 2001-2013.
Much of today's data is noisy, incomplete, heterogeneous in nature, and interlinked in a myriad of complex ways. In this talk, I will describe AI methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. I will describe both the benefit of utilizing structure and the inherent risk of ignoring structure.