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Aaditya Ramdas develops statistical inference methods that remain valid when data are examined sequentially. His research also includes multiple testing and machine learning. A central practical question is how to report evidence while allowing data collection to continue or stop in response to the observations.^[ref]

He coauthored distillation for the conditional randomization test, combining conditional independence testing with flexible test statistics and reduced computational cost. The calibration relies on the assumed feature conditional distribution.^[ref]

Molei Liu develops statistical inference and machine learning methods for heterogeneous data sources and populations. His research includes transfer across populations, imperfect statistical models and biomedical applications.^[ref]

With Eugene Katsevich, Lucas Janson and Aaditya Ramdas, he developed distillation for the conditional randomization test. The method tests conditional independence by resampling a feature from its known conditional distribution given the other features, while reducing repeated model-fitting costs.^[ref]