Papers
10
Total Citations
218
H-Index
7
About
Xiao Wang is a multidisciplinary researcher whose work sits at the intersection of parallel intelligence, smart manufacturing, cyber-physical-social systems (CPSS), and autonomous robotics. His most influential contributions center on the virtual-to-real learning paradigm — bridging synthetic and real-world data to overcome machine learning's persistent data scarcity challenges — as articulated in his widely cited 2023 overview of parallel learning (78 citations). Wang has been a prominent voice in advancing Manufacturing 5.0, exploring how generative AI and decentralized frameworks like DeFACT can build human-centric, privacy-aware production ecologies (44 citations). His work extends into the oil and gas sector, advocating a paradigm shift from Industry 4.0's cyber-physical systems toward socially conscious CPSS architectures. Beyond industrial applications, Wang demonstrates a creative breadth through projects like ShadowPainter and Skywork-daVinci, which apply robotic systems and CPSS principles to artistic creation and human-robot collaboration. His research portfolio also encompasses mobile robot perception, reinforcement learning-based path planning, and emergency crowd evacuation — reflecting a commitment to deploying intelligent systems across diverse, real-world contexts. With over 200 cumulative citations across recent publications, Wang is an emerging and versatile force in intelligent systems research.
Research Focus
Key Achievements
Top Papers
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- 6Skywork-daVinci: A Novel CPSS-based Painting Support System17 citations · 2020
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- 10Parallel Intelligence for CPHS: An ACP Approach3 citations · 2023