Office: MATH 222B
fanzhang@boisestate.edu
About
Fan Zhang’s research focuses on the design and analysis of experiments, Bayesian statistical modeling, and uncertainty quantification, with applications to computer experiments, manufacturing processes, and statistical machine learning.
Dr. Zhang’s work on Bayesian inference and surrogate modeling has centered on indicator-based variable selection for Gaussian process models (CSDA 2023). Contributions to the design of experiments include scalable level-wise screening using locating arrays (JQT 2023) and algorithmic extensions for factor screening in mixed-level supersaturated designs (JSTP 2026). Her most recent research explores decision analysis frameworks for multi-fidelity systems and variable selection methods for QQ inputs.
Selected courses taught
- MATH 360 Engineering Statistics
- MATH 361 Probability & Statistics I