The Keynote Speaker on Thursday, May 29th will be given by Volker Tresp.

Short Bio:

Lise Getoor is a Professor in the Computer Science Department at the University of California, Santa Cruz.  Prior to that, she was a Professor in the Computer Science Department at the University of Maryland, College Park (2001-2013). Her primary research interests are in machine learning and reasoning with uncertainty, applied to graphs and semi-structured data. She also works in data integration, social network analysis and visual analytics. She has over 200 publications, eight best paper awards, an NSF Career Award, was PC co-chair for the 2011 International Machine Learning Conference (ICML), and is an Association for the Advancement of Artificial Intelligence (AAAI) Fellow.  She received her Ph.D. from Stanford University in 2001, her M.S. from UC Berkeley, and her B.S. from UC Santa Barbara. 

Website: http://www.cs.umd.edu/~getoor

Combining Statistics and Semantics to Turn Data into Knowledge

Addressing inherent uncertainty and exploiting structure are fundamental to turning data into knowledge. Statistical relational learning (SRL) builds on principles from probability theory and statistics to address uncertainty while incorporating tools from logic to represent structure. In this talk I will overview our recent work on probabilistic soft logic (PSL), a SRL framework for collective, probabilistic reasoning in relational domains. PSL is able to reason holistically about both entity attributes and relationships among the entities, along with ontological constraints. The underlying mathematical framework supports extremely efficient inference. Our recent results show that by building on state-of-the-art optimization methods in a distributed implementation, we can solve large-scale knowledge graph extraction problems with millions of random variables orders of magnitude faster than existing approaches.