Zhenyun Deng

Zhenyun Deng is a Researcher in the Department of Computer Science and Technology at the University of Cambridge, specializing in Natural Language Processing (NLP) and Machine Learning. His work focuses on advancing automated fact-checking systems, logical reasoning in large language models, and graph-based reasoning techniques. Key research themes include document-level claim extraction, logic-driven data augmentation, and multi-hop question answering.

His recent contributions span automated verification of textual claims, abstract meaning representation-based data augmentation, and robust node classification on graph data. He collaborates on initiatives like the FEVER workshop and develops datasets such as TaKG for table-to-text generation enhanced with knowledge graphs.

Notable trends in his publications include:

• Integration of logical reasoning into NLP systems
• Improving model interpretability for multi-step reasoning tasks
• Handling noisy data in graph neural networks

His research bridges foundational machine learning theory with real-world applications in information retrieval and causal inference. No awards or grants are explicitly listed in the provided materials.