Wiens currently heads the Machine Learning for Data-Driven Decisions (MLD³) research group; she is also a Precision Health Co-Director. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. Within machine learning, she is particularly interested in time-series analysis, transfer/multitask learning, causal inference, and learning intelligible models. The overarching goal of her research is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge. Her work has applications in modeling disease progression and predicting adverse patient outcomes. For several years now, Wiens has been focused on developing accurate patient risk stratification approaches that leverage spatiotemporal data, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US.