Machine Learning Research Engineer

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 leverage advanced artificial intelligence techniques, specifically large language models (LLMs), to improve the quality and security of code generation. This work focuses on developing methodologies to evaluate, debug, and secure AI-generated code to enhance the reliability of digital infrastructure.

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.

The petitioner proposes to continue research at the intersection of natural language processing (NLP) and large language models (LLMs) specifically for cybersecurity threat detection. This work involves developing AI-driven frameworks that enable real-time identification and risk assessment of cyber threats to protect government entities and businesses. The endeavor aims to mitigate financial damage and enhance the security of vital national infrastructure through automated threat intelligence.