Title Of the Talk:Towards Safe, Secure, and Trustworthy Machine Learning Applications in Cyber-Physical Systems
Speaker:Dr. Shailaja Thakur
Host Faculty: Dr. Maunendra Sankar Desarkar
Date & Time: 13th October 2023, 3:30 pm
Seminar link: https://meet.google.com/hoo-vrcn-qgr
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 future, I want to expand upon the challenges and scope of applying large language models in CPS for developing time-efficient, scalable, safe and transparent real-world applications.
Dr. Shailaja Thakur is a Post Doctoral Research Fellow at the Center for Cybersecurity in Tandon School of Engineering, New York University. Her primary research focuses on applying large language models (LLMs) to develop efficient, reliable, and secure cyber-physical systems with particular focus on hardware design automation. She also works on AI fairness, trustworthiness, and privacy for cyber-physical systems.
Friday, 13th October 2023, 3:30 pm