معرفی
Zijun Gao is an Assistant Professor at the USC Marshall School of Business. Previously, they completed a Ph.D. in Statistics at Stanford University under Prof. Trevor Hastie, and served as a Research Associate at the University of Cambridge. Their research focuses on causal inference with heterogeneity, machine learning applications in statistical problems, and methodological advancements in conditional density estimation and batched bandit problems. Key contributions include the LinCDE package for conditional density estimation and validation frameworks for heterogeneous treatment effect estimation. They received the Ric Weiland Graduate Fellowship in 2020.
Major research areas include developing efficient methodologies for estimating and validating heterogeneous causal effects using large-scale healthcare databases, as well as addressing real-world data challenges like conditional density estimation and batched decision-making problems. Their work bridges theoretical statistics and practical applications in healthcare and machine learning.
