Xiang Yin

Xiang Yin is a Research Associate at the Department of Computing in Imperial College London, affiliated with the Computational Logic and Argumentation group (CLArg). His work bridges Explainable AI (XAI) and Computational Argumentation (CA), focusing on the explainability of Quantitative Bipolar Argumentation Frameworks (QBAFs) through attribution and counterfactual explanations.

Research Interests:

• Explainable AI (XAI)
• Computational Argumentation
• Quantitative Bipolar Argumentation Frameworks
• Model Interpretability
• Human-AI Interaction
• Logical Reasoning for AI

Publication Trends reveal a focus on argumentation-based explainability, with 2025-2024 works addressing large language models for claim verification, truth-discovery frameworks, and counterfactual explanations. Earlier works (2023-2022) explore random forest explanations, faithfulness criteria, and QBAF analysis. His 2018 publications on aircraft prediction systems demonstrate applied machine learning expertise.

Education

• PhD in Artificial Intelligence under Prof. Francesca Toni and Dr. Nico Potyka
• Pre-PhD: Machine Learning R&D Engineer at Baidu

Labs & Teams
Xiang is part of the CLArg group at Imperial College London, focusing on integrating computational argumentation with AI explainability and contestability.