Research & Professional Services

My Citations               Students supervised

Current Research Directions

  • High-dimensional Statistics
  • Machine Learning Research Agenda
  • Financial Econometrics and Risk Management
  • Bioinformatics and Biostatistics
  • Graphical and Network modeling
  • Nonparametric and semiparametric modeling

Research Interests

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.

Fan has co-authored four highly-regarded books on Local Polynomial Modeling (1996), Nonlinear time series: Parametric and Nonparametric Methods (2002), The Elements of Financial Econometrics, and Statistical Foundations of Data Science (2020), and authored or co-authored over 200 articles on finance, economics, statistical machine learning, computational biology, semiparametric and non parametric modeling, nonlinear time series, survival analysis, longitudinal data analysis, and other aspects of theoretical and methodological statistics. His research is supported by the National Science Foundation and National Institute of Health.

Editorial Services

Current Editor

Current Associate Editor

Past Editors

Past Associate Editors

Professional Services