Papers
2
Total Citations
53
H-Index
2
About
Xibei Yang is a pioneering researcher at the intersection of artificial intelligence, control systems, and financial engineering. His work fundamentally advances two distinct fields: AI-driven financial optimization and adaptive robotic control. In financial technology, Yang developed a groundbreaking approach that integrates GraphSAGE—a graph neural network—with deep reinforcement learning for portfolio optimization (2023, 46 citations), offering a data-driven solution to complex asset allocation problems. Simultaneously, in robotics, he introduced a novel model-free adaptive sliding mode robust control method for multi-degree-of-freedom robotic exoskeletons (2020, 7 citations). This control scheme is particularly innovative because it operates solely on input-output data, eliminating the need for precise dynamic models that traditional model-based algorithms require. By circumventing the challenges of exact system knowledge, Yang’s method enables more practical and robust control of assistive robotic devices. His dual contributions demonstrate a remarkable ability to apply advanced machine learning and adaptive control theory across disparate domains, from financial markets to human-robot interaction, establishing him as a versatile and impactful scholar in modern computational intelligence.
Research Focus
Key Achievements
Top Papers
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