Presenter: Lise Getoor, Professor of Computer Science, University of MD
When: Sep 13, 2013, 12:10 pm – 01:15 pm
Where: B001 Sparks (the 'Databasement")
"Entity-based Data Science"
Entity-Based Data Science
There is a growing interest in integrating, analyzing, visualizing and making
sense of large collections structured, semi-structured and unstructured data.
In the world of big data, data science provides tools to help with this process
– tools for cleaning the data, tools for integrating and aligning the data,
tools for finding patterns in the data and making predictions, and tools for
visualizing and interacting with the data. In this talk, I will focus on
entity-based data science, data science techniques which support network
analysis for computational social science. I will focus on the tasks of entity
resolution (determining when two references refer to the same entity),
collective classification (predicting missing entity labels in the network),
and link prediction (predicting relationships) in a holistic manner which takes
into account both entity attributes and relationships among the entities. I
will overview of our recent work on probabilistic soft logic (PSL), a framework
for collective, probabilistic reasoning in relational domains. Our recent
results show that by using state-of-the-art optimization methods in a
distributed implementation, we can solve large-scale problems with millions of
random variables orders of magnitude more quickly than existing approaches.
Lise Getoor a professor in the Computer Science Department at the University of
Maryland, College Park. Her primary research interests are in machine learning
and reasoning with uncertainty, applied to graphs and structured data. She also
works in data integration, social network analysis and visual analytics. She
has six best paper awards, an NSF Career Award, and is an Association for the
Advancement of Artificial Intelligence (AAAI) Fellow. She has served as action
editor for the Machine Learning Journal, JAIR associate editor, and TKDD
associate editor. She is a board member of the International Machine Learning
Society, has been a member of AAAI Executive council, was PC co-chair of ICML
2011, and has served as senior PC member for conferences including AAAI, ICML,
IJCAI, ISWC, KDD, SIGMOD, UAI, VLDB, WSDM and WWW. She received her Ph.D. from
Stanford University, her M.S. from UC Berkeley, and her B.S. from UC Santa
Barbara. For more information, see http://www.cs.umd.edu/~getoor
This event is open to all interested members of the Penn State community.
