Prof. Xiaoying Li | Design | Best Researcher Award 

Prof. Xiaoying Li | Design | Hubei University of Technology | China

Prof. Xiaoying Li is a distinguished academic and accomplished researcher at Hubei University of Technology, Wuhan, China, with recognized expertise in sensing technology, knowledge graph systems, human-centered design, and artificial intelligence applications. Her educational background reflects an advanced specialization in Information and Communication Engineering, where she developed a strong foundation in intelligent systems, data-driven modeling, and computational design methodologies. Through her academic and professional journey, Prof. Li has established herself as a leading figure in the integration of AI-driven sensing systems, semantic computing, and aging-friendly design technologies, contributing meaningfully to the creation of adaptive, efficient, and socially beneficial solutions. Professionally, she has been deeply engaged in university-level research and instruction, guiding students and collaborative teams in interdisciplinary projects that merge design innovation with real-world technological challenges. Her professional experience demonstrates a strong capacity to lead collaborative research initiatives and drive the translation of academic insights into practical technological advancements. Prof. Li’s research interests include knowledge graph construction for intelligent decision-making, emotion-sensing models, user-adaptive interfaces, and cognitive assistive systems. Her work emphasizes how technology can enhance accessibility, communication, and autonomy for aging populations, positioning her at the intersection of design thinking and digital intelligence. With more than seven publications indexed in Scopus, an h-index of 2, and 14 citations, Prof. Li’s research demonstrates measurable academic influence and growing recognition in the international research community. Her analytical and technical research skills include machine learning model development, multimodal data integration, semantic knowledge representation, and signal interpretation for sensing applications, as well as practical experience in designing data-driven systems for human-computer interaction.