University of Tasmania - Australia
Author Profile
GOOGLE SCHOLAR
🎓 Early academic pursuits
Dr. Wenli yang’s academic journey is rooted in strong interdisciplinary training, having earned dual phds that laid the foundation for her career in artificial intelligence. her early focus on knowledge representation and algorithmic modeling sparked her interest in scalable systems. through rigorous academic training and research exposure, she developed a deep understanding of power electronics, data engineering, and image analysis. this multidisciplinary education shaped her distinctive approach to solving real-world ai challenges using technically grounded methodologies.
👩🏫 Professional endeavors
Currently a lecturer at the school of ict, university of tasmania, dr. yang has quickly become a recognized figure in the artificial intelligence research community. she has actively led and contributed to over 10+ research projects and 8+ consultancy/industry projects, showcasing her technical capabilities across domains. her dedication extends beyond academia, with involvement in field-based data collection initiatives and industry-driven sustainability projects. her expertise also spans power electronics, where her data-driven methodologies enhance system efficiency and smart energy solutions.
🧠 Contributions and research focus
Dr. Yang’s research areas include knowledge representation, ai-powered image analysis, explainable ai, and generative ai. she is passionate about building interpretable and robust ai Computer Science and Artifical intelligence models that perform reliably under real-world conditions. having authored 29 peer-reviewed publications, with 20 as first author, she has made consistent contributions to high-impact q1 journals. her recent studies integrate power electronics with intelligent systems, creating scalable algorithms for energy-efficient computing, smart imaging, and automation across scientific domains.
🌐 Impact and influence
Wenli yang’s work has impacted both academic and applied sectors. her h-index of 9 (scopus) and h-index of 12 (google scholar), with over 647 citations, highlight her growing influence in the ai research community. her collaborative initiatives with australia seafood industries (asi), imas, csiro, and sense-t reflect her commitment to applying ai for public good. through ai- Computer Science and Artifical intelligencedriven oyster genotyping and sustainable fisheries management, she has contributed to ecological and operational advancements across australia’s marine industries.
🔮 Legacy and future contributions
Dr. Wenli yang’s vision is to create scalable, transparent, and adaptable ai systems that serve real-world applications. she continues to expand her research in explainable ai and data Computer Science and Artifical intelligence engineering, with future goals focused on integrating power electronics into intelligent systems for sustainable smart environments. her legacy lies in bridging theoretical research with field impact, inspiring future generations to pursue responsible and innovative ai solutions across multidisciplinary domains.
Notable Publications
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Title: A survey on blockchain-based internet service architecture: requirements, challenges, trends, and future
Author(s): W. Yang, E. Aghasian, S. Garg, D. Herbert, L. Disiuta, B. Kang
Journal: IEEE Access
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Title: Survey on explainable AI: From approaches, limitations and applications aspects
Author(s): W. Yang, Y. Wei, H. Wei, Y. Chen, G. Huang, X. Li, R. Li, N. Yao, X. Wang, X. Gu, ...
Journal: Human-Centric Intelligent Systems
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Title: Blockchain: Trends and future
Author(s): W. Yang, S. Garg, A. Raza, D. Herbert, B. Kang
Journal: Knowledge Management and Acquisition for Intelligent Systems: 15th Pacific …
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Title: Design of intelligent transportation system supported by new generation wireless communication technology
Author(s): W. Yang, X. Wang, X. Song, Y. Yang, S. Patnaik
Journal: International Journal of Ambient Computing and Intelligence (IJACI)
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Title: A decision model for blockchain applicability into knowledge-based conversation system
Author(s): W. Yang, S. Garg, Z. Huang, B. Kang
Journal: Knowledge-Based Systems