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!

Our KDD 2020 Tutorial on Scientific Text Mining and Knowledge Graphs is released. Please click to find slides and videos!

I am directing the Data Mining towards Decision Making (DM2) Laboratory. My research has been supported by National Science Foundation, Condé Nast Inc., Snap Inc. Research, and Notre Dame 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 - 08/31/2021)

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

• September 2020: Three papers were accepted to EMNLP 2020 on information extraction, relation reasoning, and text generation.
• August 2020: Congratulations to Daheng Wang and co-authors for winning the Best Paper Award in the International Workshop on Deep Learning on Graphs: Methods and Applications (KDD-DLG'20), entitled Learning Attribute-Structure Co-Evolutions in Dynamic Graphs.
• August 2020: Congratulations to Daheng Wang for passing proposal defense entitled Learning Complementarity and Dynamics for Contextual Behavior Modeling.
• July 2020: Two papers were accepted to CIKM 2020 on graph learning.
• July 2020: Three papers were accepted to KDD-DLG 2020 workshop on graph neural network.
• July 2020: Delighted to receive an unrestricted gift from Snap Research! Thank you Snap!
• June 2020: One paper was accepted to Knowledge and Information Systems 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!

Latest Publications

• Biomedical Knowledge Graphs Construction from Conditional Statements, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020.
• Precise Temporal Slot Filling via Truth Finding with Data-Driven Commonsense, Knowledge and Information Systems, 2020.
• Phrase-level Pairwise Topic Modeling to Uncover Helpful Peer Responses to Online Suicidal Crises, Nature Humanities and Social Sciences Communications, 2020.
• A Technical Question Answering System with Transfer Learning, EMNLP, 2020. (demo)
• Tri-Train: Automatic Pre-fine Tuning between Pre-training and Fine-tune Training for SciNER, EMNLP-Findings, 2020.
• Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases, EMNLP-Findings, 2020.
• Error-bounded Graph Anomaly Loss for GNNs, CIKM, 2020.
• GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction, CIKM, 2020.
• Learning Attribute-Structure Co-Evolutions in Dynamic Graphs, KDD-DLG, 2020. (best paper award)
• Early Fraud Detection with Augmented Graph Learning, KDD-DLG, 2020.
• GNN-based Graph Anomaly Detection with Graph Anomaly Loss, KDD-DLG, 2020.
• 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.
• 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.
• Improving Situational Awareness with Collective Artificial Intelligence over Knowledge Graphs, SPIE DCS, 2020.

Completed Projects

• Condé Nast: Representation Learning for Modeling and Predicting User Engagement and Propensity for Conversion Through the Engagement Funnel. (01/21/2019 - 05/20/2020)

Last updated on September 15, 2020.