CSER 2018 offered researchers in academia, industry, and government a common forum to present, discuss, and influence systems engineering research. The conference provided access to forward-looking research by renowned academicians as well as perspectives from senior industry and government representatives. Co-founded by the University of Southern California and Stevens Institute of Technology in 2003, the CSER series of conferences has become the preeminent event for researchers in systems engineering across the globe.The conference theme of Systems Engineering in Context was intended to highlight the ways that context-economic, social, cultural, organizational, and otherwise-can shape decision problems and other aspects of systems engineering. In their introduction to the inaugural issue of Environment Systems and Decisions, Linkov, Lambert, and Collier (2013) stated the first word in the title of the journal "is intended to imply not only the natural environment, but also the environment in which a problem, decision, or innovation exists." The focus of CSER 2018 on the context of decision-making and systems engineering is in keeping with this usage of the word environment. Topics for CSER 2018 included: • Systems in context: -Formative methods: requirements -Integration, deployment, assurance -Human factors -Safety and security • Decisions/control and design; systems modeling: -Optimization, multiple objectives, synthesis -Risk and resiliency -Collaborative autonomy -Coordination and distributed decision-making • Prediction: -Prescriptive modeling; state estimation -Stochastic approximation, stochastic optimization, and control • Integrative data engineering: -Sensor management -Design of experimentsThe papers in this issue reflect on the theme of Systems Engineering in Context, developing and implementing methods from set-based design, recommending principles of model validation, and characterizing the complex relationships between engineered technology and people. Buchanan et al. (2019) describe the traditional notions of point-based design, where the systems engineering process aims to quickly converge to a single solution. They argue that this approach is brittle with respect to the time, cost, and optimality of the systems engineering process. They then advocate for set-based design, which for consideration of all solutions within given parameters enables filtering of possibilities to converge at a final solution that may be overlooked by point-based design. After developing and integrating a cost model with an engineering model, the authors demonstrate the approach of set-based design on a light reconnaissance vehicle.