PhD Candidate

The petitioner proposes to develop cutting-edge high-performance computing infrastructures and advanced computational algorithms. This work aims to enhance the reliability and efficiency of supercomputing centers, large-scale data networks, and infrastructures supporting artificial intelligence and machine learning.

The petitioner proposes to develop scalable and data-efficient AI systems that can be fine-tuned for critical sectors like healthcare, education, and manufacturing. The work focuses on optimizing performance in data-sparse domains and designing task-specific synthetic data strategies for large language models and vision-language models.

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 her research on adapting state-of-the-art large language models (LLMs) and language generation systems to new domains and user constraints. Her work focuses on text simplification to improve the accessibility and discoverability of technical information for the public.