Xueying Wei

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About

Xueying Wei is a leading researcher at the intersection of machine learning and quantitative finance, with a primary focus on developing advanced deep reinforcement learning (DRL) and graph-based models for complex financial decision-making. Their most influential work, "GraphSAGE with deep reinforcement learning for financial portfolio optimization" (2023), has garnered 46 citations and represents a significant breakthrough in portfolio management. By integrating GraphSAGE—a graph neural network architecture—with DRL, Wei introduced a novel framework that captures intricate asset dependencies and market dynamics, enabling more adaptive and robust portfolio allocation strategies. This contribution addresses a critical gap in traditional optimization methods, which often fail to model relational structures in financial networks. Wei’s research has direct implications for algorithmic trading, risk management, and automated investment systems, offering a data-driven approach that outperforms conventional benchmarks. Their work is widely recognized for bridging theoretical advances in representation learning with practical financial applications, making it essential reading for students and researchers in computational finance. With a growing citation impact, Xueying Wei continues to push the boundaries of AI-driven finance, establishing themselves as a key innovator in the field.