I'm an assistant professor in the Department of Computer Science and Engineering, College of Engineering at the University of Notre Dame. I joined the faculty in August 2017, after completing my Ph.D. in Computer Science and Technology at Tsinghua University in 2015 and two-year postdoctoral training in Computer Science at the University of Illinois at Urbana-Champaign. [C.V.]

My research focuses on developing data-driven approaches of online behavior analysis for applications such as future behavior prediction, social recommendation, fraud detection, and event summarization, called Data-Driven Behavioral Analytics. It is at the intersection of social media analysis, spatiotemporal data mining, text mining, information extraction, machine learning, and artificial intelligence.

I am recruiting Ph.D. students and visiting researchers. Send me an e-mail (mjiang2@nd.edu)!

What's New

• August 2017: I feel excited to join Notre Dame. I am going to teach Notre Dame CSE 40647/60647: Data Science from August 22 to December 7.
• May 30 - August 3, 2017: I taught summer course UIUC CS412: Introduction to Data Warehousing and Data Mining as a sole instructor.
• July 2017: One paper was accepted to Transactions on Big Data on causal analysis of human behaviors.
• July 2017: One paper was accepted to Transactions on Knowledge and Data Engineering on large network representation learning.
• May 2017: Two papers were accepted to KDD 2017 on (1) "meta pattern" information extraction and (2) causal analysis of human behaviors.
• April 2017: One paper was accepted to IJCAI 2017 on disease-symptom network representation learning.
• April 2017: Two tutorials were accepted to KDD 2017: (1) Data-Driven Approaches towards Malicious Behavior Modeling and (2) Mining Entity-Relation-Attribute Structures from Massive Text Data. See you this August in Halifax, Nova Scotia, Canada!

Latest Publications

• Effective Promotional Strategies Selection in Social Media: A Data-Driven Approach, TBD, 2017. (to appear)
• Embedding Learning with Events in Heterogeneous Information Networks, TKDE, 2017. (to appear)
• MetaPAD: Meta Patten Discovery from Massive Text Corpora, KDD, 2017.
• Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing, KDD, 2017.
• ContextCare: Incorporating Contextual Information Networks to Representation Learning in Medical Forum Data, IJCAI, 2017.
• Treatment Effect Estimation with Data-Driven Variable Decomposition, AAAI, 2017.
• Steering Social Media Promotions with Effective Strategies, ICDM, 2016.
• Large-Scale Embedding Learning in Heterogeneous Event Data, ICDM, 2016.
• 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.
• Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach, Special Issue on the Best of SIGKDD 2014, TKDD, 2016.

Last updated on August 22, 2017.