Yuling Luo

Papers

Total Citations

101

H-Index

About

Dr. Yuling Luo is a pioneering researcher in bio-inspired autonomous systems, specializing in spiking neural networks (SNNs) and reinforcement learning for mobile robotics and intelligent transportation. Her work bridges computational neuroscience and real-world control, with a focus on how biological reward mechanisms can modulate spike-timing-dependent plasticity (STDP) to enable self-learning robots. Her 2021 paper on multi-task autonomous learning for mobile robots (36 citations) introduced a novel SNN architecture that allows robots to adapt to diverse environments without explicit programming. Expanding on this, her 2023 study on traffic signal control (35 citations) developed a teacher-student reinforcement learning framework, demonstrating how hierarchical learning can optimize complex urban systems. Her 2021 paper on reward-modulated STDP (30 citations) further established a biologically plausible method for robots to learn from environmental feedback, akin to animal conditioning. With over 100 cumulative citations, Dr. Luo’s work is foundational for energy-efficient, adaptive AI systems, offering scalable solutions for autonomous navigation and smart city infrastructure. Her research continues to inspire advances in neuromorphic computing and lifelong learning for embodied agents.