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Eugene Katsevich works on high-dimensional statistics, variable selection and multiple testing, motivated in part by genetics and genomics. These problems require distinguishing reproducible signals from the many associations that can arise when thousands of features are examined.^[ref]

His joint work on the conditional randomization test uses a known feature conditional distribution to calibrate tests of conditional independence. Distillation makes flexible prediction methods more practical as test statistics.^[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]