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 --
- September 2022: BigData-IT Workshop at IEEE BigData 2022 is calling for papers: Knowledge Discovery and Data Mining in IT Operations! Deadline: October 1, 2022.
- September 2022: KnowledgeNLP-AAAI Workshop at AAAI 2023 is calling for papers: Knowledge Augmented Methods for NLP! Deadline: November 4, 2022.
- June 2022: Congratulations to Wenhao Yu who has received the 2022 NAACL Student Travel Award and the 2022-2023 Bloomberg Data Science Fellowship!
- May 2022: Two paper were accepted to KDD on graph data augmentation and text generation!
- May 2022: One paper was accepted to ICML on graph data augmentation!
- May 2022: Tutorial on Knowledge-Augmented Methods for Natural Language Processing was successfully delivered at ACL 2022!
- 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 using data mining and natural language processing! Thanks NSF!
- February 2022: Two papers on knowledge-enhanced text generation were accepted to 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.
- 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 Links for Link Prediction, ICML, 2022.
- Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts, ACL, 2022.
- Dict-BERT: Enhancing Language Model Pre-training with Dictionary, 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 [NSF page]
- NSF RETTL: Collaborative Research: Advancing STEM Online Learning by Augmenting Accessibility with Explanatory Captions and AI [NSF page]
- NSF III: Small: Comprehensive Methods to Learn to Augment Graph Data [NSF page]
- NIH R21: Advancing Real-Time Suicide Risk Detection Through the Digital Phenotyping Smartphone Application Screenomics
- NSF CAREER: Synergistic Approaches for Specialized Intelligent Assistance[NSF page]
- CICP: AnalytiXIN Manufacturing Data Asset Research
- ONR: Towards Third Generation Network Intrusion Detection and Prevention Systems
- NSF RI: Small: Learning to Retrieve Structured Information for Summarization and Translation of Unstructured Text
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]
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Last updated on September 21, 2022.