Zhisheng Huang

Vrije Universiteit Amsterdam, Queen Mary University of London

Papers

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About

Zhisheng Huang is a pioneering researcher whose work spans the critical intersection of artificial intelligence, cognitive science, and mental health informatics. His most impactful contributions include pioneering computational approaches to suicide prevention through social media analysis, as demonstrated by his highly cited study on Sina Weibo's "Tree Hole" depression communities (2022, 12 citations). This work identified high-risk suicide messages and collective suicide warning signs, establishing a framework for AI-driven mental health surveillance. Huang's foundational research in common-sense reasoning (1999, 5 citations) formalized how perception-based beliefs are adopted, bridging human cognition and artificial agents. His innovative Ontology Based Object Categorisation (OBOC) system (2007, 5 citations) addressed the symbol grounding problem in robotics, enabling machines to meaningfully connect representations with real-world entities. By combining logical analysis with practical applications in mental health and robotics, Huang has demonstrated how AI can serve both clinical intervention and autonomous systems development. His work continues to influence researchers in computational psychiatry, cognitive robotics, and knowledge representation, making him a key figure in translating AI theory into socially impactful technologies.