Applied AI for Financial Decision-Making: From Risk-Aware Predictions to Adaptive Portfolio Intelligence

Lunch will be served at 11:45AM

Abstract: Artificial intelligence is transforming financial decision-making by enhancing prediction accuracy, risk management, and portfolio optimization. In this talk, I will present our recent works in applied AI for finance, showcasing how advanced machine learning, large language models, and reinforcement learning can be harnessed to address complex market challenges. These include RAGIC, a risk-aware generative framework for forecasting stock price intervals; DySTAGE, a dynamic spatio-temporal graph learning model for asset pricing; an LLM-based adaptive and explainable margin trading system for portfolio management; and MARS, a meta-adaptive reinforcement learning framework for multi-agent trading. Together, these approaches illustrate how AI can enable more intelligent, adaptive, and interpretable financial systems. In addition, I will briefly introduce our work applying similar AI methodologies to other domains, such as transportation and the judicial system, highlighting the broad impact and potential of applied AI beyond finance.

Prof. Guiling "Grace" Wang, Ph.D., CFA is a Distinguished Professor of Computer Science and the Associate Dean for Research and External Relations at the Ying Wu College of Computing, New Jersey Institute of Technology (NJIT). She established the AI Center for Research at NJIT, where she has been serving as its Founding Director. Prof. Wang led the development of NJIT’s MS in AI program and the AI Certificate Program, which officially launched in 2023 as one of New Jersey’s first AI programs.

Prof. Wang has been honored as a Fellow of the IEEE, becoming the first female IEEE Fellow at NJIT, and is also a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA). Her expertise in AI has been applied to solving complex challenges across fields such as Finance and Transportation. Her research using deep reinforcement learning for traffic light cycle optimization earned the IEEE Vehicular Technology Society’s 2023 Best Paper Award, four years after publication, in recognition of its lasting impact. In 2020, her project was selected as one of four awardees out of 122 submissions nationwide by the U.S. Department of Transportation’s prestigious Exploratory Advanced Research (EAR) program.

Prof. Wang served as Lead Program Co-Chair of the ACM Conference on AI in Finance in 2023 and as General Co-Chair in 2024. She founded the KDD Finance Day and has organized it annually since 2023. She also served as Sponsor Chair or Co-Chair for AAAI 2023, AAAI 2024, and ICDM 2025. In addition, she serves as an Associate Editor for several top-tier journals, including ACM Computing Surveys and IEEE Transactions on Knowledge and Data Engineering.

In addition to her academic achievements, Prof. Wang is involved in multiple governmental advisory roles. She is the sole academic representative on the New Jersey Supreme Court Committee on Artificial Intelligence and the Courts and contributes to the New Jersey Governor’s AI Taskforce Innovation Group. She also serves on the New Jersey Supreme Court Advisory Committee on Access and Fairness and is a Subject Matter Expert in AI for the U.S. Department of Homeland Security’s SAGE program.