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About

Yanjun Yan is a researcher whose work bridges artificial intelligence and swarm robotics, with a focus on real-world applications. Her key research areas include deep learning for environmental sensing and swarm intelligence for multi-robot coordination. Yan’s most cited work, “Trash and Recycled Material Identification using Convolutional Neural Networks (CNN)” (2020, 35 citations), applies computer vision to improve municipal waste management, contributing to the development of smart cities. In robotics, she has advanced swarm algorithms, notably simulating micro-robots using Particle Swarm Optimization to collaboratively locate points of interest under noise and communication constraints (2017, 9 citations). Yan has also made significant contributions to democratizing swarm robotics research. Her work on making Kilobots—low-cost, accessible micro-robots—has been instrumental in enabling broader empirical validation of collective algorithms. Through papers detailing in-house construction and debugging of Kilobots for shape formation (2016–2020), she has lowered barriers for researchers and students worldwide. Yan’s research demonstrates a commitment to solving practical problems—from waste sorting to robot swarms—while fostering open, accessible tools for the scientific community.