Jingyi Chen

Stanford University, University of California, Los Angeles, Northwestern Polytechnical University

Papers

Total Citations

H-Index

About

Jingyi Chen is a robotics and artificial intelligence researcher whose work spans from foundational multiprocessor architectures to modern agricultural automation and mobile GUI detection. Chen’s early seminal contribution, “NYMPH: A multiprocessor for manipulation applications” (1986, 41 citations), developed at the Stanford Artificial Intelligence Laboratory, pioneered a novel computational system for robotics by integrating multiple NSC 32016 processors with an MC68010-based processor on a shared Intel Multibus—a design that influenced real-time control in manipulation tasks. Decades later, Chen addressed pressing agricultural challenges with “A Low-cost Robot with Autonomous Recharge and Navigation for Weed Control in Fields with Narrow Row Spacing” (2021, 28 citations), introducing an affordable, autonomous robot that reduces herbicide overuse and labor costs, directly impacting sustainable farming. More recently, Chen advanced software engineering with “YOLOv5-MGC: GUI Element Identification for Mobile Applications Based on Improved YOLOv5” (2022, 17 citations), enhancing object detection accuracy for tiny interface elements, a critical step for automated mobile testing. With over 86 citations across these works, Chen’s career exemplifies a rare versatility—bridging hardware-level robotics, field-deployed agricultural systems, and deep learning for software testing—making lasting contributions to both theory and practical application.