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The Intelligent Adaptive Interventions lab, led by Joseph Jay Williams, encompasses Bayesian and Frequentist statistics, applied machine learning, human-computer interaction, and educational health. His applied statistics research centers around the design and modification of algorithms, tests, and analyses for adaptive experimentation. Williams's work on adaptive experiments involves randomizing participants between various arms, enabling algorithmic data analysis, such as adapting probability assignments based on user clicks on webpage links. He investigates statistically considerate Multi-Armed Bandit algorithms, which facilitate rigorous scientific inferences while providing participants with different treatment options. Williams also explores statistical tests that are suitable for analyzing data in adaptive experiments, with attention to how adaptive interventions can be implemented in various educational health contexts.
Department of Sociology