Data Scientist II

The petitioner proposes to develop advanced predictive models and machine learning methodologies that embed physical laws to improve model accuracy and interpretability. This research focuses on enhancing traffic incident detection, discovering sustainable energy materials, and enabling safer autonomous systems. The work aims to reduce computational costs in large-scale simulations through the use of surrogate models.

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 advance modeling techniques that blend machine learning with statistical analysis to strengthen predictive analytics and improve decision-making across manufacturing, logistics, and healthcare. Her work focuses on developing scalable approaches that can handle complex, data-rich environments to achieve higher efficiency and reliable system performance. The endeavor aims to provide incremental gains that translate into large economic and public benefits for U.S. infrastructure.

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.