Hao Huang

Hao Huang is a research associate in the Scientific Data Management research group at the German National Library of Science and Technology (TIB) and a doctoral student in computer science at Leibniz University Hannover.

Huang's educational background includes:

• Bachelor's degree in Information Security from Hunan University of Science and Technology
• Master's degree in Computer Science from South China University of Technology
• Master's degree in Data Science from the University of Nantes

Huang's research focuses on causal inference and causal representation learning in knowledge graphs, along with graph neural networks. His previous work includes topic models, ontology learning, and genetic algorithms. He actively contributes to:

• TrustKG (funded by the Leibniz Association)
• CLARIFY (an EU-funded project)

Huang's publications, available on ResearchGate and Google Scholar, demonstrate expertise in causal reasoning within knowledge representation systems and advanced graph-based machine learning architectures.

His scientific contributions span:

• Development of causal representation learning methods for knowledge graphs
• Innovations in graph neural network applications
• Ontology learning and topic modeling techniques

Huang maintains active research collaborations through major funded projects at the intersection of artificial intelligence, knowledge representation, and scientific data management.