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Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities. Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems. Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize. 

Abstract Sustainable urban systems (SUS) science is a new science integrating work across established and emerging disciplines, using diverse methods, and addressing issues at local, regional, national, and global scales. Advancing SUS requires the next generation of scholars and practitioners to excel at synthesis across disciplines and possess the skills to innovate in the realms of research, policy, and stakeholder engagement. We outline key tenets of graduate education in SUS, informed by historical and global perspectives. The sketch is an invitation to discuss how graduates in SUS should be trained to engage with the challenges and opportunities presented by continuing urbanization.