I am a PhD student in School of Information at University of Michigan, where I am advised by Daniel Romero. For more information, please visit: https://yulin-yu.github.io/
Areas of interest
My research interest is broadly in computational social science and information diffusion . Specifically, I apply data science methods including causal inference, network analysis, natural language processing, machine learning, and experiments to study what drives the popularity of people (e.g artists, scientists) and cultural artifacts (e.g music, vlog, social media posts). I am very interested in understanding these topics in global and cross-cultural perspectives.
Honors & awards
Novelty in what sense? Heterogeneous relationships between novelty and popularity in music. Special Recognition Award, 7th International Conference on Computational Social Science, 2021