Please join us for a talk on Friday, March 8, 2019, where Lisa Getoor, professor and director of the data, discovery and decisions research center, University of California Santa Cruz, will present a seminar titled, “The unreasonable effectiveness of structure.”

The unreasonable effectiveness of structure

The unreasonable effectiveness of structure
Presented by Lisa Getoor, professor and director of the data, discovery and decisions research center, University of California Santa Cruz

Friday, March 8, 2019 
11 a.m.
Brickyard (BYENG) 210, Tempe campus [map]

About the talk

Our ability to collect, manipulate, analyze and act on vast amounts of data is having a profound impact on all aspects of society. Much of this data is heterogeneous in nature and interlinked in a myriad of complex ways. From information integration to scientific discovery to computational social science, we need machine learning methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. Statistical relational learning (SRL) is a subfield that builds on principles from probability theory and statistics to address uncertainty while incorporating tools from knowledge representation and logic to represent structure. In this talk, Getoor will give a brief introduction to SRL, present templates for common structured prediction problems, and describe modeling approaches that mix logic, probabilistic inference and latent variables. She will overview her recent work on probabilistic soft logic (PSL), an SRL framework for large-scale collective, probabilistic reasoning in relational domains. The lecture will close with highlighting emerging opportunities (and challenges) in realizing the effectiveness of data and structure for knowledge discovery.

About the speaker

Lise Getoor is a professor in the computer science department at the University of California, Santa Cruz and director of the data, discovery and decisions research center at UC Santa Cruz.  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 doctorate from Stanford University in 2001, her master’s from UC Berkeley, and her bachelor’s from UC Santa Barbara, and was a professor in the computer science department at the University of Maryland, College Park from 2001-2013.

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