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Ryan's statistical research interests involve penalized regression modeling approaches, which include well-known methods like the LASSO and elastic net, non-convex penalties such as MCP and SCAD and their variants, as well as structured approaches like the group LASSO. These models are attractive in the analysis of high-dimensional data, particularly in scenarios where the number of available predictors exceeds the number of data-points. A primary advantage of penalized regression over other machine learning approaches is the interpretability of the resulting models. Ryan's research focuses on false discovery rate approaches of statistical inference for penalized regression models.

Ryan is also actively involved in driving research applications focusing on the analysis of driving simulator data in the context of drugged an impaired driving experiments.  These experiments often produce several gigabytes of time series data that must be processed and analyzed within the context of repeated-measures study designs.