Jingyao Wang

Institute of Software, Beijing University of Technology

Papers

Total Citations

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About

Jingyao Wang is a researcher whose work spans the intersecting domains of multimodal machine learning, sentiment analysis, and autonomous robotics navigation. Their most recognized contribution, "AMSA: Adaptive Multimodal Learning for Sentiment Analysis" (2022), addresses a critical challenge in affective computing by moving beyond unimodal approaches to develop adaptive frameworks capable of more nuanced emotion recognition. With 22 citations, this work has garnered meaningful attention within the human-computer interaction and healthcare communities, where accurate emotion detection holds significant practical value in applications ranging from service robotics to disease diagnosis. Wang's research also extends into the realm of intelligent motion planning, as demonstrated by "HDPP: High-Dimensional Dynamic Path Planning Based on Multi-Scale Positioning and Waypoint Refinement" (2022), which tackles the limitations of classical algorithms like RRT and A* when applied to dynamic, high-dimensional environments. Together, these contributions position Wang as a researcher working at the frontier of intelligent systems, combining perceptual understanding with physical autonomy. Their early-career output suggests a growing influence in building AI systems that can both sense human context and navigate complex real-world spaces effectively.