Lijing Zhu, Ph.D.

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.

Curriculum Vitae

---

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: Effecitve 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)

---

Courses (Current Academic Year)

• DASC 5133 Introduction to Data Science
• DASC 5333 Database Systems for Data Science
• DASC 5431 Data Analytics and Machine Learning 

---