Welcome!
We are the Knowledge Intelligence for Discovery and Decision-Making (KIND) Lab at the University of Oregon, led by Yu Wang. Our research lies in exploring knowledge intelligence solutions in discovery and decision-making through data mining and machine learning techniques for advancing social-good applications.
We are recruiting PhD students to work with us in the KIND Lab on topics in our general interests. Master, undergraduate students, and visiting scholars are also welcome. Please see here for position details.
News
05/2025
Utkarsh's first paper, A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models, is now online!
05/2025
Yongjia's first paper, "Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases", has been accepted at ACL 2025 Findings!
05/2025
3 papers accepted at ACL 2025, 1 paper accepted at KDD 2025!
04/2025
My student, Yongjia, has been awarded the SDM Doctorum Forum Poster Session Honorable Mention Award!
03/2025
Congratulations to Yongjia for receiving the internship offer from Adobe!
03/2025
Our collaboration paper "Building Trust in Machine Learning-Powered Networking: The Network Explainer Framework" has been accepted at SDM-AI4TS2025, congratulations Riya!
03/2025
Our paper "Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases" has been accepted at NAACL SRM2025!
03/2025
Our paper "GRS-QA - Graph Reasoning-Structured Question Answering Dataset" has been accepted at NAACL SRM2025!
03/2025
Our workshop "ML on Graphs in the Era of Artificial General Intelligence" has been accepted at KDD2025!
02/2025
Yongjia is awarded the SIAM Student Travel Award to attend SDM2025!
02/2025
Welcome two new PhD members!
Zhisheng and Dingyi join KIND Lab
02/2025Zhisheng and Dingyi join KIND Lab
Our preprint Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases is now online!
01/2025
Congratulations to Riya for being selected as a fellow for the competitive 2025 Pulse Research Fellowship!
01/2025
Our paper on Demystifying the Power of Large Language Models in Graph Generation has been accepted at NAACL'25 Findings!