What's New

Currently I am on the job market for a faculty position (since Fall 2017) in U.S. or Canada. [C.V.]
October 2016: A tutorial on Data-Driven Behavioral Analytics: Observations, Representations and Models with Dr. Peng Cui and Professor Jiawei Han at CIKM 2016. See you in Indy! [Schedule and Slides are available!]
September 2016: Two short papers were accepted by ICDM 2016.
September 2016: A workshop paper was accepted by CyberSafety Workshop at CIKM 2016.
June-July 2016: I visited Professor V.S. Subrahmanian in University of Maryland Institute for Advanced Computer Studies, College Park.
May 2016: A paper CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors was accepted by ACM SIGKDD 2016 as a full paper with oral presentation!
January 2016: Survey paper Suspicious Behavior Detection: Current Trends and Future Directions on the Special Issue on Online Behavioral Analysis and Modeling, IEEE Intelligent Systems!

Latest Publications

CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors, KDD, 2016.
Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms, TKDE, 2016.
Little is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds, AAAI, 2016.
Suspicious Behavior Detection: Current Trends and Future Directions, Special Issue on Online Behavioral Analysis and Modeling, IEEE Intelligent Systems Magazine, 2016.

Short Bio

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, Beijing in 2015. He received his bachelor from the same department in 2010. He visited Carnegie Mellon University in 2013 and University of Maryland, College Park in 2016. You can 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 gives two tutorials in major conferences. His Ph.D. thesis won the Dissertation Award at Tsinghua University. He was the recipient of National Scholarship of China. 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 by the Tencent social network since 2012. The package of his work on "Automatic Attribute Discovery" is now transferring to U.S. Army Research Lab.

Topic of my recent talk: Data-Driven Behavioral Analytics with Networks.

Talk Abstract

We interact not just with each other but also with the world around us. Can we build intelligent and trustworthy user-oriented systems by analyzing massive behavior data of the interactions? In principle, data-driven approaches can extend the methodology of observation, representation and models beyond our eyes and hands. However, we lack means to fully exploit the structured contextual information and unstructured content information. In this talk, I will present three lines of work towards this goal: (1) Mining behavior network with social spatiotemporal context. (i) How can we recommend items in social network mechanism? What are the key factors of users' decisions? (ii) How can we catch suspicious behaviors made by smart bad actors? Instead of what they can do, what do they have to do? Two computational models were developed and deployed for social recommender systems and anti-fraud/spam systems; (2) Structuring behavioral content into attributed information network. Can we automatically discover attributes and facts from text corpus? A novel methodology, Meta Pattern mining, will be introduced for open-domain Information Extraction from the data-driven perspective; (3) Integrating behavior network with rich information network. I will uncover the potential of integrating structured and unstructured data for better understanding human behaviors, and conclude by sharing my thoughts for future directions.