Lingling Jiao

Papers

Total Citations

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About

Lingling Jiao is a leading researcher in machine vision and federated learning, with a focus on advancing robotic perception systems. Her most cited work, "Robot target recognition using deep federated learning" (2021, 51 citations), introduces InVision—a novel framework that integrates deep geometric learning with convolutional neural networks to enhance robot target recognition. This contribution addresses a fundamental challenge in autonomous systems: enabling robots to accurately perceive and identify objects in dynamic environments while preserving data privacy through federated learning. Jiao's research bridges the gap between distributed machine learning and practical robotics, offering scalable solutions for real-world applications such as industrial automation and autonomous navigation. Her work has been recognized for its innovative approach to improving perception capabilities, laying the groundwork for more intelligent and privacy-preserving robotic systems. With a growing citation impact, Jiao continues to shape the intersection of deep learning and robotics, making her a notable figure in the field of artificial intelligence and computer vision.