I am a 3rd year PhD student in School of Information at University of Michigan, where I am advised by Prof.Daniel Romero. My research interest is broadly in computational social science and innovation. I use big data analysis, social network science, and computational methods to explore the relationship between 'diversity' and 'success' in innovation, particularly in the context of science and creative subjects such as music. My research focuses on understanding how diversity, defined by a range of factors including diverse ideas, perspectives, identities (e.g., gender), and styles, impacts innovative and creative achievement. Through my research, I aim to achieve two main objectives: 1) to identify the types of diversity that are most critical to advance innovation, and 2) to understand how diversity is currently valued across different domains.


  • Gendered Citation Patterns Among the Scientific Elite [Proceedings of the National Academy of Sciences (PNAS) 119 (40), e2206070119, 2022, Link]
    Kristina Lerman, Yulin Yu, Fred Morstatter, Jay Pujara
  • Large-Scale Analysis of New Employee Network Dynamics [WWW '23: Proceedings of the ACM Web Conference 2023, Link]
    Yulin Yu, Longqi Yang, Siân Lindley, Mengting Wan
  • Novelty in what sense? Heterogeneous relationships between novelty and popularity in music [ICWSM 2023 (upcoming), IC2S2 2021, Special Recognition Award]
    Yulin Yu, Ben Cheung, Yong-Yeol Ahn, Paramveer Dhillon [arXiv]
  • For more please see CURRICULUM VITAE

Honors and Awards

  • Special Recognition Award , 7th International Conference on Computational Social Science, 2021 (Music Novelty Paper)

    Research Internship