Hua's research has focused primarily on improving large scale assessments, specifically on issues emerging from the wide-ranging implementation of computerized testing. His interests are broad, encompassing both theoretical development and applied methodologies, including Computerized Testing , statistically detecting biased items, Cognitive Diagnosis, and asymptotic properties in item response theory. Most recently, his work has been concentrated on developing item selection algorithms for Computerized Adaptive Testing (CAT) to more effectively select test items sequentially that adapts to the examinee's trait level. Although the research of CAT was originally inspired by problems in high stake testing, its findings have been beneficial to other domains such as quality of life measurement, patient report outcome, k-12 accountability assessment, and survey research.