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Distinguished Speaker Series

DisSpeakerHerobannerwebpagenologo220512

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Abstract

Machine learning can deliver unprecedented performance. Its application domain has expanded into safety-critical cyber-physical systems such as UAVs and self-driver cars. However, the safety assurance of vehicular control has two conditions: 1) an analytical model of system behaviors such as provable stability, and 2) the software safety certification process (e.g., DO 178C) requires that the software be simple enough so that software safety can be validated by a combination of model checking and near exhaustive testing.

Although ML software, as is, does not meet these two safety requirements, the real-time physics model supervised ML architecture holds the promise to 1) meet the two safety requirements and 2) enable ML software to safely improve control performance and safely learn from its experience in real-time. This talk will review the structure of the proposed architecture and some methods to embed physics into ML-enabled CPS control.

 

About the Speaker

Lui Sha graduated with Ph.D. from CMU in 1985. He is Donald B. Gillies Chair Professor in CS and Drucker Eminent Faculty in Grainger College of Engineering, UIUC. Sha is a fellow of the ACM and the IEEE "for technical leadership and research contributions which enabled the transformation of real-time computing practice from an ad hoc process to an engineering process based on analytic methods." Sha is a recipient of the IEEE Simon Ramos Medal, IEEE's highest honor for exceptional contributions to system science and engineering. Sha was a member of the US National Academy of Science's committee on certifiably dependable software; and a member of the NASA Advisory Council. Recently, he has been working on a scientific model-guided machine-learning for safe autonomy. In cooperation with hospitals, he has been working on computational pathophysiology model-based medical guidance systems for cardiac-pulmonary arrest and sepsis.

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