Measuring disruption and novelty in science
I build embedding-based measures of how research reshapes the knowledge it builds on, so that disruptive work can be identified without relying on fragile citation counts.
I develop and analyze representation learning methods and apply them to large-scale science, technology, and policy data to study how scientific and technological advances shape society and how society, in turn, responds to them.
I am a Ph.D. candidate in Data Science at the University of Virginia, advised by Yong-Yeol Ahn. Before the Ph.D. program, I studied Physics at POSTECH in Korea.
News
All news-
Taught a tutorial, “Claude Code in Research for Beginners,” at SICSS Korea.
-
Gave a talk at NetSci 2026.
-
Served as a panelist at the National Academies of Sciences workshop on Disruptive Innovation in Washington, DC.
-
Our work on a robust measure of disruptiveness was published in Science Advances.
Research
All publicationsAI innovation and the future of work
I link patent-level measures of disruption to the tasks that make up occupations, separating AI innovations that reinforce existing work structures from those that alter them.