Peiliang Zhang (张培良)

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Ph.D. Student


Office: Rm 1208, Aite Bldg, No. 21 Gongda Road, Hongshan, Wuhan
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Short Bio

Peiliang Zhang is pursuing his Ph.D. degree in the School of Computer Science and Artificial Intelligence at Wuhan University of Technology (武汉理工大学), Wuhan, China, supervised by Prof. Jingling Yuan. He is also a visiting Ph.D. student in Yonsei DataLab supervised by Associate Prof. Yongjun Zhu at Yonsei University (延世大学), Seoul, South Korea. Before that, in 2022, he received his M.S. degree in software engineering from Dalian University (大连大学), Dalian, China, supervised by Prof. Chao Che in Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education.

His research interest broadly lies in the areas of AI for Science, Molecular Representation and Medical Informatics. He has published more than 20 papers in high-quality conferences and journals, including SIGKDD, IJCAI and Information Fusion. He has also served as a reviewer of multiple conferences and journals, including NeurIPS, SIGKDD, CVPR, AAAI, ICJAI, KDD, TII, and Bioinformatics.

Education Background

Papers

All publications in Publications Page.

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SIGKDD 2026
Cross-Domain Molecular Relational Learning: Leveraging Chemical Structure-Activity Analysis
Peiliang Zhang, Jingling Yuan, Shiqing Wu, Mengqing Hu, Chao Che, Yongjun Zhu, Lin Li
PDF
Benefiting from structure-activity analysis, we propose the Domain Adversarial Training Network with Structural-Semantic Transfer Discrepancy to optimize cross-domain adaptive representation for molecular structures and visual images.
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SIGKDD 2026
Representational Alignment with Chemical Induced Fit for Molecular Relational Learning
Peiliang Zhang, Jingling Yuan, Qing Xie, Yongjun Zhu, Chao Che, Lin Li
PDF
With theoretical justification, we propose the Representational Alignment with Chemical Induced Fit (ReAlignFit) to enhance the stability of Molecular Relational Learning.
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IJCAI 2025
Subgraph Information Bottleneck with Causal Dependency for Stable Molecular Relational Learning
Peiliang Zhang, Jingling Yuan, Chao Che, Yongjun Zhu, Lin Li
PDF
CausalGIB leverages causal dependency to guide substructure representation and integrates subgraph information bottleneck to optimize the core substructure representation, generating stable representations.
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INFORM FUSION 2025
Multi-Category Fusion Contrastive Learning with Core Data Selection for Robust RGB Image-based Dental Caries Classification
Peiliang Zhang, Yaru Chen, Yunjiong Liu, Chao Che*, Yongjun Zhu
Code PDF
Instead of fine-tuning the backbone network structure, M3C focuses on improving the robustness of the model to label errors from a novel perspective by identifying core data that is highly relevant to the dental caries category.
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DASFAA 2024
Key Substructure Learning with Chemical Intuition for Material Property Prediction
Peiliang Zhang, Jingling Yuan, Lin Li, Wen Luo, Jiwei Hu, Xin Li
PDF PPT
The paper proposed the key substructure learning with chemical intuition. KSCI is used to capture irregular substructures within molecules and to identify the differential contributions of these substructures to chemical properties.

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