Applied Scientist

The petitioner proposes to enhance the safety and security of large language models (LLMs) to combat malicious cyberattacks and promote trustworthy AI technologies. His work focuses on addressing vulnerabilities in AI systems and designing methods to safeguard against adversarial manipulation.

The petitioner proposes to conduct advanced computational research focused on improving the safety, controllability, and sample efficiency of large language models (LLMs). This work involves developing data-centric and machine-learning-driven frameworks to ensure the responsible and sustainable adoption of generative AI tools. The goal is to enhance the trustworthiness and interpretability of AI systems used in critical sectors like healthcare and national security.

The petitioner proposes to continue developing programming applications and creating reliable, secure platforms for cloud computing, VOIP communication, the Internet of Things, and cyber-physical systems. His work specifically focuses on addressing cyber-attacks and enhancing emergency response capabilities through improved software scalability and code quality.

The petitioner proposes to continue research on developing diagnostic and enhancement approaches to improve the robustness of artificial intelligence-enabled software systems. This work specifically targets safety-critical applications such as facial recognition and self-driving vehicles to strengthen cybersecurity and ensure safe deployment.