Yulin Yu is a PhD candidate at University of Michigan School of Information, advised by Prof.Daniel Romero. Her research interest is broadly in computational social science, data science, and innovation. Specifically, she explores the data-science drivers of innovation—data, technology, and people—across science, the workplace, and art, by developing and applying computational frameworks. By doing so, her work uncovers creative strategies across the three drivers for innovation. Specifically, Yulin’s work focuses on: 

Data: understanding how scientific innovation can advance through the novel use of ‘big data’ and how we can automate innovation via predicting the need or use of novel datasets (PNAS 2024) 

Technology: (1) predicting the future of innovation (2) the relationship between novelty and popularity in music on recommendation platforms (ICWSM 2023) 

People connectivity: investigating how emerging changes (e.g., remote work) or social factors (e.g., gender) foster or hinder a more diverse and interconnected human network—which is often crucial for innovation (PNAS 2022,WWW 2023, WWW 2024)

Pronouns

She/Her

Areas of interest

Computational Social Science, Data Science, Innovation

Honors & awards

EECS Rising Star 2024 - MIT, CEW+ Scholars - Umich