Faculty Search Colloquium: Data-Driven Behavioral Analytics with Networks

Date and time: 
Thursday, March 2, 2017 - 17:30
Location: 
220 Deschutes
Author(s):
Meng Jiang
University of Illinois at Urbana-Champaign

Abstract

Thanks to Information Technologies, online user behaviors are broadly recorded at an unprecedented level. This gives us an opportunity for getting insights into human behaviors and our societies from real data of large scale enough that makes manual analysis and inspection completely impractical in building intelligent and trustworthy systems. In this talk I will discuss about research problems, challenges, principles, and methodologies of developing network-based computational models for behavioral analysis. Specifically, I will present recent approaches on (1) modeling user behavioral intentions with knowledge from social and behavioral sciences for behavior prediction, recommendation, and suspicious behavior detection, (2) modeling social and spatiotemporal information for knowledge from behavioral contexts, (3) structuring behavioral content into information networks of entities and attributes, and (4) integrating structured and unstructured behavior data to support decision-making and information systems. Two results, CatchSync and MetaPAD, will be presented in details. I will conclude with my thoughts on future directions.

Biography

Dr. Meng Jiang is a postdoctoral researcher in University of Illinois at Urbana-Champaign. He received his Ph.D. from the Department of Computer Science at Tsinghua University in 2015. He obtained his bachelor degree from the same department in 2010. He visited Carnegie Mellon University in 2013 and visited University of Maryland, College Park in 2016. Find more about him here: http://www.meng-jiang.com.

His research lies in the field of data mining, focusing on user behavior modeling. He has delivered two book chapters and two conference tutorials on this topic. His Ph.D. thesis won the Dissertation Award at Tsinghua. His work on "Suspicious Behavior Detection" was selected as one of the Best Paper Finalists in KDD'14. His work on "Social Contextual Recommendation" has been deployed in the Tencent social network. The package of his work on "Attribute Discovery from Text Corpora" is transferring to U.S. Army Research Lab. He has published 20 papers with 590+ citations, including 15 papers with 380+ citations as the first author.

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