Embracing the power of deep learning in safety-critical systems

What if the technology that powers our cars, medical devices and energy grids could guarantee safety and reliability like never before? Pengyuan "Eric" Lu, a Ph.D. candidate at the Penn Research in Embedded Computing and Integrated Systems Engineering (PRECISE) Center, has been making this vision a reality through his research in deep learning for safety-critical systems.

Cyber-physical systems (CPS), in which "smart" technology interacts with the physical world, are vital to our daily lives: they power automobiles, medical devices, building heating-and-cooling systems, and smart-grid electricity networks. Lu's research focuses on enhancing the reliability and safety of such systems.

Through his work, Lu, who is advised by PRECISE Center director Insup Lee, Cecilia Fitler Moore Professor in the Department of Computer and Information Science (CIS), and Oleg Sokolsky, research professor in CIS, aims to address a significant challenge in modern control policies of CPS: ensuring that these systems are safe and react appropriately in real time to the physical world. Lu, who joined Penn in 2019, leverages neural network repair techniques to enforce these properties, bringing a new level of safety assurance to deep learning-enabled CPS components.

"I believe my research could have significant implications for the future use of deep models in safety-critical applications," Lu says. "This work highlights the practicality of adapting deep models to meet formal safety and control objectives across extensive input spaces, advancing the industry's progress toward integrating these models into critical systems such as autonomous vehicles and medical devices."

This story is by Liz Wai-Ping Ng. Read more at Penn Engineering.