Lijing Zhu

Dr. Lijing Zhu serves as an Assistant Professor of Data Science within the College of Science and Engineering at the University of Houston-Clear Lake, where she teaches foundational data science courses and conducts research in artificial intelligence.

Academic Background

• Ph.D. in Data Science, Bowling Green State University (August 2025)

Research Focus

Dr. Zhu's research spans machine learning, graph-based deep learning, continual graph learning, and computer vision. Her work addresses critical challenges in knowledge representation through continual knowledge graph learning, human-object interaction detection, and graph representation learning. She develops innovative algorithms that enhance the robustness and efficiency of deep learning models for complex structured data, with particular emphasis on overcoming catastrophic forgetting in dynamic knowledge graphs.

Publication Trends

Her active 2024-2025 publication record in venues like ECML PKDD, CIKM, and IEEE Big Data demonstrates a cohesive research trajectory across three interconnected domains: (1) advancing graph neural network robustness against adversarial attacks, (2) developing continual learning frameworks for evolving knowledge graphs, and (3) applying multimodal deep learning to drug discovery and computer vision problems. This cross-cutting work positions her at the intersection of theoretical machine learning and practical applications.

Teaching and Mentorship

Dr. Zhu teaches DASC 5133 (Introduction to Data Science), DASC 5333 (Database Systems for Data Science), and DASC 5431 (Data Analytics and Machine Learning). As an early-career faculty member building her research program, she offers graduate students opportunities to contribute to high-impact publications while developing expertise in graph-based AI systems and multimodal learning.