By Shailja Thakur, New York University

Abstract 

In a world increasingly reliant on Cyber-Physical Systems (CPS), there are critical challenges associated with the integration of complex software and hardware. The enormous and diverse nature of data, alongside pressing security and privacy concerns, demands innovative solutions. My work aims to enhance the intelligence of CPS through AI, aiming for systems that are not only self-aware but also capable of adapting in real-time to changing environments. To that end, my work has spanned the automotive, energy, and hardware sectors, delivering practical solutions engineered alongside industry partners. I have made significant strides in enhancing security in automotive systems and have pioneered tools for deciphering the decision-making processes of machine learning models. In the realm of hardware design, I am exploring the potentials of Large Language Models (LLMs) to automate and optimize the process, reducing human error and increasing efficiency. In the future, I want to expand upon the challenges and scope of applying generative AI in CPS for developing time-efficient, scalable, safe and transparent real-world applications.