Guohui Yang
Papers
1
Total Citations
3
H-Index
1
About
Guohui Yang is a researcher whose work sits at the intersection of data mining, innovation analytics, and emerging technology assessment. Their key research areas include patent intelligence, talent evaluation algorithms, and the application of dimensionality reduction techniques to complex innovation ecosystems. Yang’s major contribution lies in developing novel frameworks for mining and classifying technological innovation talent, particularly through the use of t-SNE algorithms applied to patent indices. In their most cited work, “Mining Technological Innovation Talents Based on Patent Index using t-SNE Algorithms: Take the Field of Intelligent Robot as an Example” (2020, 3 citations), Yang demonstrates how adaptive learning can dynamically assess and categorize technical talent based on real-time patent data. This approach offers a scalable, data-driven method for identifying high-potential innovators in fast-evolving fields like intelligent robotics. While the citation count is modest, the work’s methodological novelty—combining unsupervised learning with patentometrics—positions it as a foundational step toward more responsive talent discovery systems. Yang’s research holds practical implications for R&D management, helping organizations pinpoint expertise shifts and nurture innovation capacity in real time.
Research Focus
Key Achievements
Top Papers
- 1