Lijing Zhu

Lijing Zhu

Assistant professor at University of Houston-Clear Lake. Ph.D. in Data Science from Bowling Green State University. Research focus on machine learning and graph-based deep learning.

About Lijing Zhu

I am an Assistant Professor at the University of Houston-Clear Lake. I earned my Ph.D. in Data Science from Bowling Green State University. My work focuses on machine learning, graph-based deep learning, and applied data science, with an emphasis on building practical data-driven methods for real-world problems.

My research interests include machine learning, graph-based modeling, and intelligent data-driven systems. I am particularly interested in developing computational methods that connect theory with application and support meaningful analysis, decision-making, and problem solving in real settings.

In addition to research, I am committed to teaching and mentoring students in data science, analytics, and related areas. I value clear communication, practical problem solving, and the use of computational tools to make complex ideas more accessible and useful.

Collaboration: I welcome research collaborations and discussions related to machine learning, graph learning, and applied data science. If you are interested in collaboration, student projects, or related research discussions, please feel free to contact me by email.

Research Interests

  • Machine learning
  • Graph-based deep learning
  • Data science and analytics
  • Intelligent data-driven systems

News

  • Septmber 2025: Joined the University of Houston-Clear Lake as Assistant Professor of Data Science.
  • August 2025: Earned a Ph.D. in Data Science from Bowling Green State University.
  • August 2025: Paper accepted at CIKM 2025 on temporal graph neural network robustness: Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults.
  • June 2025: Paper accepted at ECML PKDD 2025 on continual knowledge graph embedding: ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding.
  • May 2025: Paper accepted at IJCNN 2025: E2CB2former: Effective and Explainable Transformer for CB2 Receptor Ligand Activity Prediction.
  • March 2025: Paper accepted at ICANN 2025: CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization.
  • December 2024: Paper accepted at IEEE Big Data 2024: Flexible Memory Rotation (FMR): Rotated Representation with Dynamic Regularization to Overcome Catastrophic Forgetting in Continual Knowledge Graph Learning.
  • December 2024: Paper accepted at ICONIP 2024: HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction.
  • Octomber 2023: Published SKGHOI: Spatial-Semantic Knowledge Graph for Human-Object Interaction Detection at the IEEE ICDM Workshop.

Students

I look forward to mentoring undergraduate and graduate students in data science, machine learning, and related areas. This section will be updated as student research projects, collaborations, and mentoring activities develop.