The world is bursting with data, not just in sheer amounts of it, but also in terms of complexity. Interdependencies among variables abound, and their relationships can change over time in intricate, nonlinear ways. Such complexities are common in nature and intelligent systems have evolved in biological organisms to adapt to these interdependencies and nonlinearities. More recently, engineers have begun to build intelligent systems for applications in health, security, and industry that can similarly adapt. The scientists who study intelligent adaptive systems in nature, as well as the engineers who build them in the lab, are increasingly in need of conceptual and technical abilities to deal with large, complex systems and datasets. These abilities provide a common basis for exchanging hypotheses and theories among mathematicians, physicists, biologists, cognitive scientists, computer scientists and engineers; all of whom work on common problems of adaptation, learning, regulation, and prediction. This National Science Foundation Research Traineeship (NRT) award to the University of California, Merced, will help the next generation of PhD students make interdisciplinary breakthroughs in theories and applications of intelligent adaptive systems. The project anticipates training 100 PhD students, including 50 funded trainees, from doctoral programs in applied mathematics, cognitive and information sciences, electrical engineering and computer science, mechanical engineering, physics, and quantitative and systems biology. Prior research in cybernetics, connectionism, and complex adaptive systems focused on general principles of intelligent adaptive systems that cut across disciplines and domains. The NRT program will advance the next wave of research in this area, by delving more deeply into principles of learning and adaptation as they manifest across a wider range of biological, human, and technological systems. The training program includes an intensive computational basecamp, custom course modules on intelligent adaptive systems, lab rotations, communication skills development workshops, and industry networking opportunities. Taken together, these NRT activities will enable the trainees to achieve conceptual and technical capabilities for dealing with large, complex datasets. All NRT trainees will have the opportunity to learn about entrepreneurship, network with industry mentors, engage in professional development, and engage with the local community to educate, disseminate research, and develop outreach partnerships. The NRT program will transform the capacity for interdisciplinary research and education at UC Merced. At the institutional level, the NRT program will serve as a model for collaborative, interdisciplinary graduate education. An extensive recruitment plan will connect with and enhance resources and programs at other UC campuses and a number of Hispanic-Serving Institutions to increase the diversity of scientists and engineers working on intelligent adaptive systems. Finally, the NRT program will have a direct and transformative economic impact in California's Central Valley, by fostering a culture of innovation and higher education in under-privileged communities. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.
NRT-DESE Intelligent Adaptive Systems: Training computational and data-analytic skills for academia and industry