NSF Workshop on Real-time Learning and Decision Making of Dynamical Systems. To Be Held at NSF, February 12-13, 2018.

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The goal of the workshop is to have a group of leading experts that have complementary background (in the area of control, signal processing, machine learning, communication, power and energy, transportation, etc.) to cross the bridge among various research areas and shape the research paradigm that arises from real-time learning for data-driven dynamical systems. Specifically, we have identified the following data-rich dynamical engineering systems: power and energy systems, transportation systems, as well as signal and information processing systems. Topics for discussions include various real time learning approaches for the engineering systems (such as deep learning architectures, model-based learning, model-free learning, reinforcement learning, etc.), data representation for engineering systems (including the research problems in feature extraction, graphical models, real time unsupervised learning, etc.), and the potential solutions for closing the loop around data. The workshop will examine control, signal processing, machine learning, communication, power and energy, and transportation systems. Intellectual Merit: The workshop will address key questions in real-time learning and decision making that arises from dynamic data-driven engineering systems. Topics of interest include different perspectives of learning and decision making from control, signal processing, machine learning, computational intelligence, and domain applications' viewpoint. They will synergistically contribute towards two of the ten big ideas from NSF; ``Harnessing Data for 21st Century Science and Engineering'' and "Growing Convergent Research at NSF''. Broader Impacts: This workshop will engage and promote convergent research centered around real-time decision making and data in engineering systems. The discussions will have significant impact on the research and education of machine learning and large-scale data-driven engineering systems. The workshop will also generate opportunities for collaborative research between academia and industry. Ideas will also be generated for possible innovation and entrepreneurship. The workshop report will serve as an important resource for the scientific community and the general public.