Qinghai Miao

University of Chinese Academy of Sciences

Papers

Total Citations

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About

Qinghai Miao is a leading researcher at the intersection of artificial intelligence and material science, with a focus on bridging the virtual and real worlds. His primary research areas include parallel learning, computational learning across synthetic and real domains (Syn2Real and Sim2Real), and AI-driven material science. Miao’s most significant contribution is the development of the virtual-to-real paradigm, which trains machine learning models on virtual data to solve real-world problems, effectively addressing data scarcity. His influential 2023 paper, “Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real,” has garnered 78 citations, underscoring its impact on the field. Additionally, Miao has proposed a novel 2D collision detection method based on Contact Theory, offering an efficient solution for mechanical and robotic engineering, though less cited. His recent work in AI for material science (2024) signals a forward-looking approach to applying computational intelligence to materials discovery. Miao’s research is pivotal for advancing autonomous systems and data-efficient learning, making him a key figure in modern AI applications.