I'm an assistant professor in the Department of Computer Science and Engineering at the University of Notre Dame. My research fields are data mining, machine learning, and natural language processing. My research focuses on Computational Behavior Modeling for applications such as intelligent assistance, recommender system, and question answering. It is at the intersection of user modeling, graph learning, information extraction, text mining, and text generation. [C.V.]

I am directing the Data Mining towards Decision Making (DM2) Laboratory. I am recruiting PhD students. Drop me an e-mail (mjiang2 [at] nd.edu) if you are interested! Stay healthy, safe, and happy!

What's New

• Check our Github repositories: DM2 @ Github --
• January 2022: One survey paper was accepted to ACM Computing Surveys on knowledge-Enhanced Text Generation.
• December 2021: Tutorial on Knowledge-Augmented Methods for Natural Language Processing was accepted for delivery at ACL 2022!
• November 2021: One paper was accepted to ACM TIST on graph neural networks and federated learning.
• November 2021: I am thrilled to receive the Best Reviewers Award from CIKM 2021.
• November 2021: Tutorial on Knowledge-Enriched Natural Language Generation (led by DM2 PhD student Wenhao Yu) was successfully delivered at EMNLP 2021!
• October 2021: One paper led by an undergraduate researcher Joseph Kuebler was accepted to ICBK 2021 on knowledge extraction.
• September 2021: Awarded a new grant from NSF for advancing online STEM learning with AI. I look forward to working with amazing collaborators from UIUC and Gallaudet University!

Latest Publications

• A Survey of Knowledge-Enhanced Text Generation, ACM Computing Surveys, 2022. (In press)
• A Survey on Data-Driven Network Intrusion Detection, ACM Computing Surveys, 2022. (In press)
• Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance, ACM Transactions on Intelligent Systems and Technology, 2022. (In press)
• A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning, IEEE Transactions on Neural Networks and Learning Systems, 2021. (In press)
• Modeling Co-evolution of Attributed and Structural Information in Graph Sequence, IEEE Transactions on Knowledge and Data Engineering, 2021.
• Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding, ACM Transactions on Intelligent Systems and Technology, 2021.
• Inductive Contextual Relation Learning for Personalization, ACM Transactions on Information Systems, 2021.
• Knowledge-Augmented Methods for Natural Language Processing, ACL, 2022. (tutorial)
• Dynamic Attributed Graph Prediction with Conditional Normalizing Flows, ICDM, 2021.
• Knowledge-Enriched Natural Language Generation, EMNLP, 2021. (tutorial)
• Sentence-Permuted Paragraph Generation, EMNLP, 2021.
• Injecting Entity Types into Entity-Guided News Generation, EMNLP, 2021.
• Action Sequence Augmentation for Early Graph-based Anomaly Detection, CIKM, 2021.
• Evolutionary Graph Normalizing Flows, KDD-DLG, 2021.
• Data Efficient Learning on Graphs, KDD, 2021. (tutorial)
• Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations, KDD, 2021.
• Cross-Network Learning with Partially Aligned Graph Convolutional Networks, KDD, 2021.
• Technical Question Answering across Tasks and Domains, NAACL-HLT, 2021.
• Enhancing Factual Consistency of Abstractive Summarization, NAACL-HLT, 2021.
• TCN: Table Convolutional Network for Web Table Interpretation, TheWebConf, 2021.
• Few-Shot Graph Learning for Molecular Property Prediction, TheWebConf, 2021.
• Traceability Transformed: Generating More Accurate Links with Pre-Trained BERT Models, ICSE, 2021 (selected for ACM SIGSOFT Distinguished Paper Award).
• Data Augmentation for Graph Neural Networks, AAAI, 2021.

Current Projects

• NSF SHF: Medium: Collaborative Research: Semantically-Enhanced Software Traceability for Supporting Human-Centric Tasks. (08/15/2019 - 08/14/2023)
• NSF RETTL: Collaborative Research: Advancing STEM Online Learning by Augmenting Accessibility with Explanatory Captions and AI (09/15/2021 - 08/31/2024)

Completed Projects

• 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)
• NSF CRII: III: Complementarity Learning for Contextual Behavior Modeling. (10/01/2019 - 09/30/2021)

Last updated on January 24, 2021.