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个人简介
Professor Fan's research lies in the developments of statistical machine learning theory and methods and their applications in finance, economics, genomics and health. His primary research focuses on developing and justifying statistical machine learng methods and AI algorithms that are used to solve problems from the frontiers of scientific research and business operations, with focus on financial asset pricing, risk modeling, and portfolio choices. This is expanded into other disciplines where the statistics discipline is useful such as genomics, genetics and biomedical studies. Professor Fan devotes most of his efforts to the search for intuitively appealing, computationally scable, data-driven, robust statistical machine learning approaches and AI algorithms and illustrates the approaches by real data and simulated examples. He is also very interested in developing foundational statistical theory and in providing fundamental insights to sophisticated statistical machine learning methods. These include distributed computation, deep learning, high-dimensional statistical learning, factor modeling, network modeling, among others. In Finance, his research focuses on portfolio allocation, high-frequency trading, risk management, financial econometrics, and risk modeling and management.
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crossref(2024)
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2024)
Operations Research (2024)
arxiv(2023)
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ICLR 2024 (2023)
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