Applied Math Seminar: Harlin Lee (UCLA)

Title: Understanding scientific fields with network analysis and topic modeling

Abstract: As scientific disciplines get larger, it becomes impossible for an individual researcher to be familiar with the entire body of literature, which forces them to specialize in a sub-field. Such insulation can hinder the birth of ideas that arise from new connections, eventually slowing down scientific progress. As such, discovering fruitful interdisciplinary connections by analyzing scientific publications is an important problem in the science of science. This talk will present several past and ongoing projects in answering that question using tools from network analysis and topic modeling: 1) a dynamic-embedding-based method for link prediction in a machine learning semantic network, where the nodes are concepts in machine learning, and the time-stamped edges indicate co-occurrence in scientific papers, and 2) finding communities in cognitive science that study similar topics but do not cite each other or publish in the same venues.