Doctoral Researcher, CAS Institute of Automation, China
Dr. Xiangyu Zhang is a doctoral researcher at the Institute of Automation, Chinese Academy of Sciences (CASIA). With a strong foundation in mechanical engineering and robotics, Dr. Zhang transitioned into the realm of social computing and artificial intelligence. His research addresses real-world crises by developing cognitive AI agents and multi-agent systems for public health emergency response. His collaborative work with the Chinese CDC and the Beijing CDC has resulted in intelligent models that predict disease outbreaks and optimize response strategies using advanced machine learning and large language models. His forward-thinking research contributes significantly to the growing intersection between AI and epidemiology, aiming to build resilient, data-driven health systems. A rising voice in the AI and public health community, Dr. Zhang continues to lead impactful research published in leading journals and presented at top international conferences.
Author’s Profile
🎓 Education
Dr. Zhang’s educational journey exemplifies his interdisciplinary approach. He is currently pursuing his Ph.D. in Social Computing at CASIA under the University of Chinese Academy of Sciences. His research integrates AI, epidemiology, and intelligent systems at the State Key Laboratory of Multimodal Artificial Intelligence Systems. Prior to this, he earned his M.Sc. in Mechanical Engineering from the University of Electronic Science and Technology of China (UESTC), where he specialized in biomimetic actuators in robotics. His undergraduate studies in Mechanical Design (B.Eng., UESTC) laid the foundation for his engineering acumen, particularly in robotic arm systems and control mechanisms. This unique blend of mechanical design and cognitive AI enables him to craft deeply technical yet socially responsive research, blending physical systems with computational intelligence to solve contemporary global health challenges.
🏢 Experience
Dr. Zhang’s research experience spans across both engineering innovation and public health intelligence. As a Ph.D. researcher at CASIA, he is engaged in groundbreaking work on cognitive agent-based systems for pandemic response and crisis management. He has worked extensively with high-stakes research projects funded by the National Natural Science Foundation of China and the Next-Generation AI Development Plan (2015–2030). His key contributions include developing simulation models for outbreak prediction and adaptive intervention frameworks using LLMs and multi-agent systems. He has collaborated directly with the Chinese CDC and Beijing CDC, grounding his work in critical, real-world public health needs. His previous research in robotics labs focused on elastic actuators and autonomous learning systems, bringing an engineering lens to his AI-driven innovations. Despite his early career stage, his experience is already making waves in intelligent public health system design.
🔬 Research Focus
Dr. Zhang’s research centers on the development of intelligent decision-support systems for public health emergencies. His primary focus lies in the integration of multi-agent systems, epidemiological modeling, and large language models (LLMs) to create dynamic, real-time frameworks for disease surveillance, prediction, and intervention. He has proposed agent-based simulation architectures that replicate human-like decision-making in health crisis contexts, enabling systems to not only forecast disease spread but also adaptively respond with optimal strategies. His novel work in spatiotemporal forecasting for urban epidemics (IEEE ISI 2025) and cognitive frameworks for crisis modeling (Frontiers of Engineering Management) positions him as a forward-thinking researcher in AI for social good. His contributions are pioneering the use of AI to enhance epidemiological intelligence and resilience against future pandemics—an area of urgent global need.
📚 Publication Top Notes
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Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions
Technologies, 2025, 13, 272.
This paper reviews the current methodologies and advancements in agent-based modeling (ABM) for epidemics. Dr. Zhang explores how ABMs simulate human behaviors and interactions to better understand disease spread and the effectiveness of policy interventions.
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Large Language Models: Technology, Intelligence, and Thought
Frontiers of Engineering Management
Co-authored with Z. Cao and D. Zeng, this paper examines the philosophical and functional implications of large language models (LLMs) in understanding intelligence and cognition, particularly in the context of public service applications.
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LLM-Driven Spatiotemporal Forecasting of Urban Infectious Diseases
IEEE ISI 2025 Conference
A real-world case study on Haidian District, this work presents a cutting-edge LLM-integrated system for forecasting infection patterns, enabling early interventions based on data-driven spatial and temporal analysis.
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ShadowPainter: Robotic Painting via Active Learning
Journal of Intelligent & Robotic Systems, 2022, 105(3), 61
This research introduces an AI-powered robotic system capable of learning artistic techniques through visual replication. While outside public health, it showcases Dr. Zhang’s skills in human-like machine learning.
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Novel Multi-Configuration Elastic Actuator
Advanced Intelligent Systems, 2024, 6(10): 2400079
This paper introduces an elastic actuator capable of energy modulation, relevant for dynamic robotic systems. It highlights Dr. Zhang’s prior contributions in robotics and mechanical innovation.
Conclusion
Dr. Xiangyu Zhang presents a strong and promising research portfolio marked by high-impact publications, innovative interdisciplinary work, and societal relevance in AI-driven public health crisis management. While still early in his career, his contributions clearly demonstrate leadership potential and research excellence, especially in emerging fields.
