Machine Learning for Financial Time Series Analysis and Portfolio Optimization

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My research focuses on developing machine learning methods for intelligent financial decision making. I am particularly interested in integrating deep learning, reinforcement learning, and financial domain knowledge to build robust and adaptive portfolio management systems. My current work investigates prior-guided reinforcement learning, where traditional portfolio optimization methods are incorporated into deep reinforcement learning to improve learning efficiency, stability, and generalization. More broadly, I aim to study knowledge-guided AI techniques for financial time-series analysis, sequential decision making, and risk-aware investment strategies. My long-term research goal is to develop interpretable and reliable artificial intelligence systems that support practical financial decision making under dynamic and uncertain market environments.