DescriptionHigh-End Computing (HEC) encompasses both massive computational and big data capability to solve computational problems of significant importance that are beyond the capability of small- to medium-scale systems. Data science includes large-scale data analytics and visualization across multiple scales of data from a multitude of sources. Increasingly on-demand and real-time data intensive computing, enabling real-time analysis of simulations, data-intensive experiments and streaming observations, is pushing the boundaries of computing and resulting in a convergence of traditional HEC and newer cloud computing environments. This panel will explore challenges and opportunities at the intersection of high-end computing and data science. • Which markets will drive the adoption of HEC for Data Science? What new applications could arise from this convergence? What game-changers will this enable? • What impact will this have on conventional workflows, architectures and new memory paradigms (supercomputers versus shared cloud computing environments), software tools and workforce development?