Yingyu He

Papers

Total Citations

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About

Yingyu He is a rising researcher in computational intelligence and real-time systems, whose work focuses on integrating deep reinforcement learning (DRL) with optimization algorithms to address complex scheduling challenges. His most-cited paper, "A DRL-based online real-time task scheduling method with ISSA strategy" (2024), has already garnered 7 citations, demonstrating early impact in the field. He’s known for pioneering a hybrid approach that combines DRL with an improved sparrow search algorithm (ISSA) to dynamically allocate tasks in time-critical environments, significantly enhancing system efficiency and adaptability. This work addresses a key bottleneck in cyber-physical systems and edge computing, where latency and resource constraints demand intelligent, adaptive scheduling. Beyond this, He’s contributions extend to algorithm design and performance optimization, offering practical solutions for real-world applications like autonomous systems and industrial automation. His research is particularly notable for bridging theoretical advances in reinforcement learning with tangible engineering outcomes, making it a valuable resource for students and researchers exploring the intersection of AI and real-time computing. With a growing citation trajectory, Yingyu He is establishing himself as a promising voice in the next generation of intelligent systems research.