In spite of tremendous advances in machine learning, the goal of designing truly autonomous cyber-physical systems (CPS), capable of learning from and interacting with the environment to achieve complex specifications remains elusive. This research seeks to address this apparent paradox (advances in machine learning/relatively low levels of autonomy) by developing a new class of verifiable safe learning- enabled CPS, capable of adapting to previously unseen dynamic scenarios where the data is generated, and decisions must be made, as the system operates. It addresses the CPS challenges posed by the data revolution and highly dynamic systems by creating a new framework at the confluence of dynamical systems, machine learning and viability theory, specifically tailored to learning and safely acting in uncertain, data deluged scenarios.

The research is organized around three tightly interacting thrusts -- R1: Joint learning of sparse latent features and manifolds, R2: Real-time inference in dynamic scenarios; and R3: Verifiable decision-making algorithms -- that exploit the underlying sparse structure induced by the dynamics of the CPS to obtain fast solutions to problems that challenge current techniques. A key feature of the proposed framework is its ability to take advantage of the tight coupling between thrusts to obtain tractable problems. Examples are low-complexity real-time inference methods that leverage parsimonious structures unveiled during learning, and control strategies that verify closed-loop properties by using these structures to recast the problem into a hybrid system analysis form.

Education is proactively integrated into this project. At the pre-college level, summer STEM programs for urban high school students will be developed. Participants will explore CPS concepts and complete a final project endowing autonomous vehicles with limited learning capabilities. At the undergraduate level, ideas put forth in this proposal will be infused through the curriculum. The hallmark of the educational program will be its integration through the central metaphor of learning-enabled CPS. At the graduate level, this integrative theme across the disciplines represented by the Co-PIs will be continued, including teaching of a course that includes experiential assignments. In addition, this project will provide opportunities and support for graduate students to engage as members of an interdisciplinary team. The strategy to broaden participation is two pronged: on one hand, it will leverage, in addition to the summer STEM programs for urban youth, NUPRIME (NEU's Program in Multicultural Engineering). On the other hand, it will take advantage of the co-PIs leadership roles in their respective societies to organize events targeting high schoolers and underrepresented groups at conferences.