Haoxue Wang Millenium Quant & Cambridge Mathematician
QuantVision 2026: Fordham’s Quantitative Conference
Haoxue Wang is a quantitative researcher and AI innovator working at the intersection of machine learning, agent systems, and systematic trading. He currently serves as a Quantitative Researcher at Millennium in London, where he is part of an equity arbitrage trading team conducting alpha research alongside leading portfolio managers.
In parallel, Haoxue is a core contributor to LLMQuant, a global open source AI community with more than 100,000 followers dedicated to advancing large language models and multi agent systems for quantitative finance. Through projects such as Magents, a multi agent generative trading system, and Quant Wiki, he is helping shape the next generation of AI driven research infrastructure for financial markets.
Previously, Haoxue conducted research at Microsoft focused on integrating AI agents into quantitative strategy development, publishing two papers and contributing to open source initiatives such as RD Agent, a research automation framework that has earned more than 10,000 GitHub stars. He also served as a Microsoft Research intern in the machine learning group, where he received an Award of Excellence for work on adversarial training techniques to improve the robustness of gradient boosting models including LightGBM and XGBoost.
In 2024, he conducted AI strategy research at HSBC in London, applying advanced machine learning techniques to investment research and earning recognition as a Senior AI Ambassador. He also gained exposure to financial markets and quantitative research through mentoring experience at Citi.
During his time at the University of Cambridge, Haoxue was a quantitative researcher with the Cambridge Student Investment Fund and a member of the Cambridge University Algorithmic Trading Society. He earned first place in the Morgan Stanley UK Datathon electronic trading competition, ranking at the top among all candidates. Earlier research experience includes developing statistical methods to control false discovery rates at Hong Kong University of Science and Technology. Haoxue is currently pursuing his PHD.
Haoxue holds a mathematics background from the University of Cambridge, where he focused on semiparametric theory and advanced statistical methods including causal inference, Bayesian modeling, convex optimization, and financial modeling. He also studied mathematics at ETH Zürich, specializing in statistics and machine learning under the supervision of Professor Peter Bühlmann.
His work reflects a rare combination of theoretical depth, open source leadership, and practical alpha driven research in global financial markets.
