Papers
2
Total Citations
29
H-Index
2
About
Jingjing Yang is a leading researcher in bio-inspired optimization algorithms and medical robotics. Her primary research areas include meta-heuristic optimization, swarm intelligence, and minimally invasive surgical robot design. Yang’s most impactful contribution is the development of the orthogonal opposition-based learning-driven dynamic salp swarm algorithm (OBDSSA), a novel framework that significantly enhances the performance of the standard salp swarm algorithm for solving complex global optimization problems. This work, published in 2022, has already garnered 26 citations, demonstrating its rapid influence in the optimization community. In parallel, Yang has advanced the field of medical robotics through the design and performance evaluation of a novel vascular interventional surgery robot. This system, introduced in 2024, aims to reduce surgical fatigue for healthcare professionals by assisting with precise manipulation of micro guidewires and catheters during cardiovascular procedures. By bridging theoretical algorithm innovation with practical medical device engineering, Yang’s work offers powerful tools for both computational optimization and life-saving clinical applications, making her a notable figure in interdisciplinary engineering research.
Research Focus
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Top Papers
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