Abstract: Continued advancement in computational power and high-speed networking is enabling a new model of scientific experiment, experiment-in-the-loop computing (EILC). In this model, one or more high-fidelity simulations are run as data is collected from observational and experimental sources. At the same time, the amount and complexity of data generated by simulations and by observational and experimental sources, such as distributed sensor networks and large-scale scientific user facilities, continues to increase. Several research and development challenges are posed by this paradigm, many of which are independent of the scientific application domain. New algorithms, including artificial intelligence and machine learning algorithms, to merge simulation ensembles and experimental data sets must be developed. High performance data management and transfer techniques must be developed to control the ensembles and move simulated and observed data sets. Workflows must be constructed with high-quality provenance metadata to enable post-analysis and improvement of the computing solution. The Workshop on Large-scale Experiment-in-the-Loop Computing (XLOOP 2019) will be a unique opportunity to promote this cross-cutting, interdisciplinary topic area. We invite papers, presentations, and participants from both the physical and computer sciences.