The vision described above implies a broadening of what it means to be a computer scientist. A significant opportunity for change is in the area of education. This change should include educating computer science students to achieve impact with computing, computational methods, and systems approaches in important domain-specific areas. Such a shift in culture would encourage these students to develop domain expertise and to collaborate directly with domain experts while in graduate school or in preparing for graduate work6 and to address such topics as modeling and predicting energy use and designing for reuse.
Making such a shift successfully will also require a culture of experimentation and innovation in the application of computer science. Further, it will require a research infrastructure in order to make progress. That infrastructure should include the following: (1) available standard data sets, models, and challenge problems to the community in order to assist in developing a common discourse and target for innovation, analogous to Grand Challenges in robotics, speech, vision, and so on; and (2) the building of shared infrastructure through open architectures and testbeds that allow for grounded iterative experimentation in the context of real components, both human and technical. Such architectures could go a long way to increasing the feasibility and impact of experimental research in academia and to creating an ecosystem that supports iterative innovation.
Education and training within the target domains constitute an equally important goal. One challenge is in the translation of problems from one domain or field to another—for instance, describing the power and electric grid systems as a dynamical system and control problem—and then translating sometimes newly exposed assumptions back to the problem domain. Information and data are critical to understanding the challenges, formulating solutions, deploying solutions, communicating results, and facilitating learning and new behaviors that are based on results of the work. Thus a significant component of meeting virtually all sustainability challenges is to infuse computational thinking and computer science- and information-rich approaches into the deploying industry and the research and mission agencies.
PRINCIPLE: Undergraduate and graduate education in computer science should provide experience in working across disciplinary boundaries. Graduate training grants and postdoctoral fellowships should support training in multiple disciplines. Undergraduate and
6These shifts are already underway in various fields—for example, biocomputing.