Lijing Zhu, Ph.D.
Assistant Professor of Data Science,
College of Science and Engineering
Contact number: 281-283-3880
Email: zhul@uhcl.edu
Office: Delta 167
Biography
Dr. Lijing Zhu earned her Ph.D. in Data Science from Bowling Green State University
in August 2025. Her research focuses on machine learning, graph-based deep learning,
continual graph learning, and computer vision. She has published in leading venues
such as ECML PKDD, CIKM, and IEEE Big Data.
Areas of Expertise
- Continual Knowledge Graph Learning
- Human-Object Interaction Detection
- Graph Representation Learning
- Natural Language Processing
Publications
- ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding (Accepted by ECML PKDD 2025);
- Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults (Accepted by CIKM 2025)
- E2CB2former: Effective and Explainable Transformer for CB2 Receptor Ligand Activity Prediction (Accepted by IJCNN 2025)
- CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization (Accepted by ICANN 2025)
- Flexible memory rotation (fmr): Rotated representation with dynamic regularization to overcome catastrophic forgetting in continual knowledge graph learning (Accepted by IEEE Big Data 2024)
- Hgtdp-dta: Hybrid graph-transformer with dynamic prompt for drug-target binding affinity prediction (Accepted by ICONIP 2024)
- SKGHOI: Spatial-Semantic Knowledge Graph for Human-Object Interaction Detection (Accepted by ICDM Workshop)
- Lijing Zhu, Qizhen Lan, Qing Tian, Wenbo Sun, Li Yang, Lu Xia, Yixin Xie, Xi Xiao,
Tiehang Duan, Cui
Tao, et al. Ett-ckge: Efficient task-driven tokens for continual knowledge graph embedding. In Proceedings
of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML PKDD), 2025 - Dong Hyun Jeon, Lijing Zhu, Haifang Li, Pengze Li, Jingna Feng, Tiehang Duan, Houbing
Herbert Song,
Cui Tao, and Shuteng Niu. Leveraging vulnerabilities in temporal graph neural networks via strategic
high-impact assaults. In Proceedings of the 34th ACM International Conference on Information and Knowledge
Management (CIKM), Boise, ID, USA, 2025. ACM. to appear - Xi Xiao, Wentao Wang, Jiacheng Xie, Lijing Zhu, Gaofei Chen, Zhengji Li, Tianyang
Wang, and Min Xu.
E2cb2former: Effective and explainable transformer for cb2 receptor ligand activity prediction. Submitted
to 2025 International Joint Conference on Neural Networks(IJCNN), 2025 - Lijing Zhu, Dong Hyun Jeon, Wenbo Sun, Li Yang, Chloe Yinxin Xie, and Shuteng Niu.
Flexible memory
rotation (fmr): Rotated representation with dynamic regularization to overcome catastrophic forgetting
in continual knowledge graph learning. 2024 IEEE International Conference on Big Data (Big Data), Accepted,
2024 - Xi Xiao, Rui Jiying, Jiacheng Xie, Lijing Zhu, Gaofei Chen, Zhengji Li, Tianyang Wang,
and Min Xu.
Hgtdp-dta: Hybrid graph-transformer with dynamic prompt for drug-target binding affinity prediction.
accepted by International Conference on Neural Information Processing(ICONIP) 2024, 2024 - Lijing Zhu, Qizhen Lan, Alvaro Velasquez, Houbing Song, Acharya Kamal, Qing Tian,
and Shuteng
Niu. Skghoi: Spatial-semantic knowledge graph for human-object interaction detection. In 2023 IEEE
International Conference on Data Mining Workshops (ICDMW), pages 1186–1193. IEEE, 2023
Courses (Current Academic Year)
- DASC 5133 Introduction to Data Science
- DASC 5333 Database Systems for Data Science
- DASC 5431 Data Analytics and Machine Learning