Changxiao Cai’s research interests lie broadly in the intersection of statistics, optimization, and machine learning. He is interested in developing provably scalable methods for information extraction from high-dimensional data, with an aim to achieve the optimal interplay between statistical accuracy and computational efficiency.
My research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection. I have extensive collaboration experiences with health services researchers and epidemiologists to improve healthcare and public health practice, and have been providing statistical support to solve sampling and analysis issues on health and social science surveys.
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