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Big Graph Data Science
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14 August 2014

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One of the challenges in big data analytics lies in being able to reason collectively about extremely large, heterogeneous, incomplete, noisy interlinked data.  We need data science techniques which an represent and reason effectively with this form of rich and multi-relational graph data.  In this presentation, Dr. Lise Getoor will describe some common collective inference patterns needed for graph data including: collective classification (predicting missing labels for nodes in a network), link prediction (predicting potential edges), and entity resolution (determining when two nodes refer to the same underlying entity).  Dr. Getoor will describe two key capabilities required, relational feature construction and collective inference, and briefly describe some of the cutting edge analytic tools being developed within the machine learning, AI, and database communities.


Lise Getoor is Professor in the Computer Science Department at the University of California, Santa Cruz and currently leads their Data Science Initiative.    Her research areas include machine learning and reasoning under uncertainty with a focus on structured data; in addition she works in data management, data integration, visual analytics and social network analysis. She is a recipient of an NSF Career Award and eight best paper and best student paper awards.   She was co-chair for ICML 2011, and has served on the PC of many conferences including the senior PC of AAAI, ICML, KDD, UAI, WSDM and the PC of SIGMOD, VLDB, and WWW.   She is an elected Fellow of the Association for Artificial Intelligence and a board member of the International Machine Learning Society.     She was a professor in the Computer Science Department, University of Maryland, College Park (2001 to 2013) and received her PhD from Stanford University, her Master’s degree from University of California, Berkeley, and her undergraduate degree from the University of California, Santa Barbara.


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