Fang Han

Fang Han is Adjunct Associate Professor of Economics at the University of Washington, with joint affiliation in Statistics. He holds a PhD in Biostatistics from Johns Hopkins University and served as Google PhD Fellow during his doctoral studies. His methodological research develops novel statistical tools for dependence measurement, high-dimensional inference, and time series analysis.

Research contributions include:

• Rank-based correlation measures for complex data structures
• Bootstrap methods for dependence testing
• Manifold-adaptive statistical techniques
• Semiparametric regression methods

Han serves as Associate Editor for Bernoulli and editorial board member for Dependence Modeling. His NSF-funded work advances statistical learning theory with applications in econometrics and biostatistics.