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Enabling Performant and Trustworthy Learning-enabled CPS-IoT Systems | Mani Srivastava

Special Seminar Series
Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla

Abstract: "The previously discrete technologies of IoT and AI have now entered a tight virtuous embrace. IoT allows sensing and actuation in our physical, social, and urban spaces with unimaginable ubiquity. AI allows sophisticated inferences and decisions to be made algorithmically using deep neural networks, even from unstructured and high- dimensional data, with uncanny performance. Together they seek to perform sophisticated perception-cognition-communication-action loops in diverse applications. However, designers of learning-enabled IoT systems face the challenge of extremely resource-constrained edge platforms operating in uncertain environments while assuring performance and trustworthiness. Moreover, in many applications, the systems go beyond taking actions based on rich inferences about the world state to perform long-term reasoning about complex events and obey the underlying physics, rules, and constraints. Based on our experience in designing such systems in applications including mHealth, ocean animal health, agriculture robotics, and military, This talk explores meeting these challenges through a combination of (i) neurosymbolic architectures that allow the incorporation of physics awareness and human knowledge while enhancing user trust, (ii) automatic platform-aware architecture search and code generation, and (iii) techniques to efficiently adapt to the deployment environment."