R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning. - Shengyao Lu; Bang Liu; Keith G. Mills; Shangling Jui; Di Niu (ICLR, 2022)

VPR-Gym: A Platform for Exploring AI Techniques in FPGA Placement Optimization. - Ruichen Chen; Shengyao Lu; Mohamed A. Elgammal; Peter Chun; Vaughn Betz; Di Niu (FPL, 2023)

Building Optimal Neural Architectures Using Interpretable Knowledge. - Keith G. Mills; Fred X. Han; Mohammad Salameh; Shengyao Lu; Chunhua Zhou; Jiao He; Fengyu Sun; Di Niu (CVPR, 2024)

GOAt: Explaining Graph Neural Networks via Graph Output Attribution. - Shengyao Lu; Keith G. Mills; Jiao He; Bang Liu; Di Niu (ICLR, 2024)

EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time. - Shengyao Lu; Bang Liu; Keith G. Mills; Jiao He; Di Niu (ICML, 2024)

TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution. - Jiuding Yang; Shengyao Lu; Weidong Guo; Xiangyang Li; Kaitong Yang; Yu Xu; Di Niu (COLING, 2025)

Machine Learning-Enabled Fast Prediction of GGNMOS Performance and Inverse Design for Electrostatic Discharge Applications. - Zihan Wang; Ruichen Chen; Shengyao Lu; Ian Then; Di Niu; Xihua Wang (IEEE Access, 2025)

TreeX: Generating Global Graphical GNN Explanations via Critical Subtree Extraction. - Shengyao Lu; Jiuding Yang; Baochun Li; Di Niu (CoRR, 2025)