URAP

Char Juin Chin

NucScholar

NucScholar is a framework in development that utilizes Natural Language Processing (NLP) to automatically retrieve, categorize, and recommend nuclear science papers. It aims to fully automate the workflow for nuclear science literature searches to improve efficiency in the nuclear data pipeline. In this effort, accurate and comprehensive categorization and labeling of nuclear research papers is crucial for recommendations to be specific and relevant. As such, my work involves implementing NLP techniques such as topic modeling and classification to efficiently label nuclear research papers.

Message to Sponsor

NucScholar has given me many opportunities beyond research itself, from speaking to active researchers who are invested in their tools and learning about their expectations to helping present a poster at an event. Understanding the real world impact of my work has given it a deeper meaning and I greatly appreciate the opportunity to continue exploring this project and its possibilities.
  • Major: Computer Science and Linguistics
  • Mentor: Bethany Goldblum/ Nuclear Engineering