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 data science research focuses on 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 knowledge graph, graph machine learning, information extraction, text mining, and text generation. [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 CAREER award. I am recruiting one or 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 --
• January 2023: One paper was accepted to ICLR on Knowledge-intensive NLP! One survey paper was accepted to EACL on Multi-task NLP!
• December 2022: KnowledgeNLP-AAAI Workshop will be at AAAI 2023 on February 13: Knowledge Augmented Methods for NLP!
• October 2022: Two paper were accepted to EMNLP on unified NLP frameworks!
• June 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!
• 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.
• Generate rather than Retrieve: Large Language Models are Strong Context Generators, ICLR, 2023.
• A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods, EACL, 2023.
• AutoGDA: Automated Graph Data Augmentation for Node Classification, LoG, 2022.
• A Unified Encoder-Decoder Framework with Entity Memory, EMNLP, 2022. (Oral)
• Retrieval Augmentation for Commonsense Reasoning: A Unified Approach, EMNLP, 2022.
• 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)

Last updated on January 22, 2023.