Andrea L. Bertozzi

Prof. of Mathematics and Mechanical and Aerospace Engineering
Betsy Wood Knapp Chair for Innovation and Creativity
University of California Los Angeles
USA

The Mathematics of Crime

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Robert D. Nowak

Nosbusch Professor in Engineering
University of Wisconsin-Madison
USA

Active Learning

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Bin Yu

Chancellor's Professor
Statistics, Electrical Engineering and Computer Science
University of California, Berkeley
USA

Three Principles of Data Science: Predictability, Computability and Stability (PCS)

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Rong Jin

Principal Engineer
Alibaba Group
China

Optimization in Alibaba: Beyond Convexity

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Xiao-Li Meng

Whipple V. N. Jones Professor of Statistics
Harvard University
USA

Is it a Computing Algorithm or a Statistical Procedure: Can you tell or do you care?

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Jennifer Neville

Prof. of Computer Science and Statistics
Purdue University
USA

Towards Relational AI – The good, the bad, and the ugly of learning over networks

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.

Biography
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.