Distinguished Lecture: Towards Relational AI -- The Good, The Bad, and The Ugly of Learning Over Networks
Associate Professor, Purdue University
Friday, March 29, 2019
11:15am - 12:30pm
100 Hancock Hall
In the last 20 years, there has been a great deal of research on machine learning methods for graphs, networks, and other types of relational data. By moving beyond the independence assumptions of more traditional ML methods, relational models are now able to successfully exploit the additional information that is often observed in relationships among entities. Specifically, network models are able to use relational information to improve predictions about user interests, behavior, and interactions, particularly when individual data is sparse. The tradeoff however, is that the heterogeneity, partial-observability, and interdependence of large-scale network data can make it difficult to develop efficient and unbiased methods, due to several algorithmic and statistical challenges. In this talk, I will discuss these issues while surveying several general approaches used for relational learning in large-scale social and information networks. In addition, to reflect on the movement toward pervasive use of the models in personalized online systems, I will discuss potential implications for privacy, polarization of communities, and spread of misinformation.
Jennifer Neville is the Miller Family Chair Associate Professor of Computer Science and Statistics at Purdue University. She received her PhD from the University of Massachusetts Amherst in 2006. She is currently PC chair of the 19th SIAM International Conference on Data Mining. She was an elected member of the AAAI Executive Council from 2015-2018. In 2016 she was PC chair of the 9th ACM International Conference on Web Search and Data. In 2012, she was awarded an NSF Career Award, in 2008 she was chosen by IEEE as one of "AI's 10 to watch", and in 2007 was selected as a member of the DARPA Computer Science Study Group. Her work, which includes 100+ peer-reviewed publications with more than 7500 citations, focuses on developing machine learning and AI methods for complex relational domains, including social, information, and physical networks.