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 Computational Behavior Modeling for applications such as behavior prediction, recommendation, fraud detection, event detection, planning, and decision making. It is at the intersection of network science, text mining, information extraction, machine learning, and artificial intelligence.

Call for Journal Article Participation: I am a co-Topic Editor for the Research Topic "Computational Behavioral Modeling for Big User Data" (click me!) in Big Data Networks, Frontiers in Big Data. Keywords: Recommender Systems, Misbehavior Detection, Social Network Analysis, Graph Learning, Deep Learning. Please submit your abstract if your research falls in the topic!

I am directing the Data Mining towards Decision Making (DM2) Laboratory. My research has been supported by National Science Foundation, Condé Nast Inc., and Global Gateway.

• NSF CRII: III: Complementarity Learning for Contextual Behavior Modeling. (10/01/2019 - 09/30/2021)
• NSF SHF: Medium: Collaborative Research: Semantically-Enhanced Software Traceability for Supporting Human-Centric Tasks. (08/15/2019 - 08/14/2023)
• Global Gateway: Digital Empires: Structured Biographical and Social Network Analysis of Early Chinese Empires. (05/01/2019 - 08/31/2020)
• Condé Nast: Representation Learning for Modeling and Predicting User Engagement and Propensity for Conversion Through the Engagement Funnel. (01/21/2019 - 05/20/2020)

I am recruiting PhD students and visiting researchers. Drop me an e-mail (mjiang2 [at] nd.edu) if you are interested! Stay healthy, safe, and happy!

What's New

• June 2020: One paper was accepted to Knowledge and Information Systems, Springer on temporal fact extraction.
• June 2020: One paper was accepted to Nature Humanities and Social Sciences Communications on text mining for suicidal prevention.
• May 2020: One paper on behavior modeling and two tutorials (Scientific KG and Multi-modal Learning) were accepted to KDD 2020.
• April 2020: I presented our work on knowledge graph learning in SPIE DCS 2020.
• April 2020: One paper was accepted to ACL 2020 on text representation and answer retrieval.
• March 2020: One paper was accepted to TCBB on scientific text mining.
• February 2020: I have been recognized as ACM WSDM 2020 Outstanding PC.
• January 2020: Two full papers with oral presentation were accepted to TheWebConf 2020. Congratulations to Wenhao!
• November 2019: Three papers were accepted to AAAI 2020 on representation learning.
• October 2019: One regular paper was accepted to IEEE BIBM 2019 on scientific literature search, with acceptance rate of 18%.
• August 2019: One paper was accepted to EMNLP 2019 on scientific text mining.

Latest Publications

• Precise Temporal Slot Filling via Truth Finding with Data-Driven Commonsense, Knowledge and Information Systems, 2020. (In press)
• Phrase-level Pairwise Topic Modeling to Uncover Helpful Peer Responses to Online Suicidal Crises, Nature Humanities and Social Sciences Communications, 2020. (In press)
• Scientific Text Mining and Knowledge Graphs, KDD, 2020. (Tutorial)
• Multi-modal Network Representation Learning: Methods and Applications, KDD, 2020. (Tutorial)
• Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors, KDD, 2020.
• Crossing Variational Autoencoders for Answer Retrieval, ACL, 2020.
• Improving Situational Awareness with Collective Artificial Intelligence over Knowledge Graphs, SPIE DCS, 2020.
• Identifying Referential Intention with Heterogeneous Contexts, TheWebConf, 2020.
• Experimental Evidence Extraction in Data Science with Hybrid Table Features and Ensemble Learning, TheWebConf, 2020.
• Multi-label Patent Categorization with Non-local Attention-based Graph Convolutional Network, AAAI, 2020.
• Few-Shot Knowledge Base Completion, AAAI, 2020.
• Graph Few-shot Learning via Knowledge Transfer, AAAI, 2020.

Last updated on June 29, 2020.