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 (CBM) with graph and text data for applications such as intelligent assistance, recommender system, question answering, scientific discovery, and mental healthcare. It is at the intersection of user modeling, graph machine learning, information extraction, text mining, and natural language generation. I created the CBM course at Notre Dame. [C.V.]

I am directing the Data Mining towards Decision Making (DM2) Laboratory, supported by National Science Foundation (NSF), Amazon.com, Inc., Snap Inc., Condé Nast Inc., and Notre Dame International. I received NSF CRII award in 2019 and CAREER award in 2022. I am recruiting two 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 --
• May 2022: BigData-IT Workshop at IEEE BigData 2022 is calling for papers: Knowledge Discovery and Data Mining in IT Operations! Deadline: October 1, 2022.
• May 2022: Mis2-TrueFact Workshop at KDD 2022 is calling for papers: Joint International Workshop on Misinformation and Misbehavior Mining on the Web and Making a Credible Web for Tomorrow! Deadline: May 26, 2022.
• May 2022: Two paper were accepted to KDD!
• May 2022: One paper was accepted to ICML on graph data augmentation!
• April 2022: Check our lab page. DM2 is welcoming five new PhD students! We look forward to having you at Notre Dame!
• April 2022: One paper on complementarity learning was accepted to IEEE TNNLS!
• March 2022: Received NSF CAREER award for advancing the knowledge and methods for mental health with data mining and natural language processing! Thank you NSF!
• February 2022: One paper was accepted to Bioinformatics!
• February 2022: Awarded a new grant from NSF for advancing the technologies of graph data augmentation! Thank you NSF!
• February 2022: Two papers on knowledge-enhanced natural language generation were accepted to ACL 2022!
• 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!

Latest Publications

• Deep Multimodal Complementarity Learning, IEEE Transactions on Neural Networks and Learning Systems, 2022.
• A Survey of Knowledge-Enhanced Text Generation, ACM Computing Surveys, 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.
• Modeling Co-evolution of Attributed and Structural Information in Graph Sequence, IEEE Transactions on Knowledge and Data Engineering, 2021.
• Graph Rationalization with Environment-based Augmentations, KDD, 2022.
• Automatic Controllable Product Copywriting for E-Commerce, KDD, 2022. (Applied Data Science)
• Learning from Counterfactual Graph for Link Prediction, ICML, 2022.
• Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts, Findings of ACL, 2022.
• Dict-BERT: Enhancing Language Model Pre-training with Dictionary, Findings of ACL, 2022.
• 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.

Current Projects

• NSF SHF: Medium: Collaborative Research: Semantically-Enhanced Software Traceability for Supporting Human-Centric Tasks (08/15/2019 - 08/14/2023) [NSF page]
• NSF RETTL: Collaborative Research: Advancing STEM Online Learning by Augmenting Accessibility with Explanatory Captions and AI (09/15/2021 - 08/31/2024) [NSF page]
• NSF III: Small: Comprehensive Methods to Learn to Augment Graph Data (03/01/2022 - 02/28/2025) [NSF page]
• NSF CAREER: Synergistic Approaches for Specialized Intelligent Assistance (03/15/2022 - 02/28/2027) [NSF page]

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)
• Global Gateway: Digital Empires: Structured Biographical and Social Network Analysis of Early Chinese Empires (05/01/2019 - 08/31/2020)
• NSF CRII: III: Complementarity Learning for Contextual Behavior Modeling (10/01/2019 - 09/30/2021) [NSF page]

Last updated on May 15, 2022.