IC Colloquium : Big Graph Data Science
Event details
| Date | 03.11.2014 |
| Hour | 16:15 › 17:30 |
| Location | |
| Category | Conferences - Seminars |
By : Lise Getoor - University of Maryland
Video of her talk
Abstract :
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 talk, I 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 which nodes refer to the same underlying entity). I will describe three key capabilities required: relational feature construction, collective inference, and lifted reasoning. Finally, I will describe some of the cutting edge analytic tools being developed within the machine learning, AI, and database communities to address these challenges. In particular, I will describe work by my group on Probabilistic Soft Logic (http://psl.umiacs.umd.edu/), a highly scalable declarative language for collective inference problems.
Bio :
Lise Getoor is a professor in the Computer Science Department at UC Santa Cruz. Her research areas include machine learning and reasoning under uncertainty; in addition she works in data management, visual analytics and social network analysis. She has over 200 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, has served as Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, JAIR Associate Editor, and she has served on the AAAI Council. 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 a recipient of an NSF Career Award and eight best paper and best student paper awards. She was recently recognized as one of the top ten emerging researchers leaders in data mining and data science based on citation and impact, according to KDD Nuggets. 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 at the University of Maryland, College Park from 2001-2013.
More information
Video of her talk
Abstract :
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 talk, I 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 which nodes refer to the same underlying entity). I will describe three key capabilities required: relational feature construction, collective inference, and lifted reasoning. Finally, I will describe some of the cutting edge analytic tools being developed within the machine learning, AI, and database communities to address these challenges. In particular, I will describe work by my group on Probabilistic Soft Logic (http://psl.umiacs.umd.edu/), a highly scalable declarative language for collective inference problems.
Bio :
Lise Getoor is a professor in the Computer Science Department at UC Santa Cruz. Her research areas include machine learning and reasoning under uncertainty; in addition she works in data management, visual analytics and social network analysis. She has over 200 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, has served as Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, JAIR Associate Editor, and she has served on the AAAI Council. 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 a recipient of an NSF Career Award and eight best paper and best student paper awards. She was recently recognized as one of the top ten emerging researchers leaders in data mining and data science based on citation and impact, according to KDD Nuggets. 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 at the University of Maryland, College Park from 2001-2013.
More information
Practical information
- General public
- Free
- This event is internal
Contact
- Host : Christoph Koch