Understanding How Big Data and AI Reshape Human Innovation: Assistant Professor Yulin Yu

What excites me most is understanding how people interact with emerging data and AI systems, and how these systems can expand, rather than constrain, human creativity and innovation.

 
Yulin Yu joined the College of Information Science as an assistant professor in 2026 after completing her PhD at the University of Michigan's School of Information and research appointments at Microsoft Research and Northwestern University. A computational social scientist, Yu studies how big data and artificial intelligence are reshaping the ways people explore, create and innovate across science, technology and other creative domains.

What brought you to the College of Information Science?

I come from an iSchool background myself, and I have always valued the interdisciplinary culture of information science. What attracted me to the College of Information Science is the diversity of perspectives here. Faculty come from different disciplinary, methodological and cultural backgrounds, yet we are connected by an interest in understanding information, technology and people.

I find that environment especially exciting because many of the questions I care about—innovation, creativity and human–AI interaction—cannot be fully understood from the perspective of a single discipline. I am excited to be in a place where different ways of thinking can come together and generate new ideas.

What is your current research, and what most excites you about this work?

I am a computational social scientist. A major strength of my work is developing computational measurements that allow us to study human behavior and social phenomena at scale.

A central question motivating my research is: What drives innovation and creativity? Increasingly, I study this question in a world where people interact with massive amounts of data and artificial intelligence. I am interested in how these technologies change what people explore, the ideas they encounter, the connections they make and ultimately what they create.

Scientific innovation is one of my major model systems because science provides both an extremely consequential form of innovation and unusually rich data for studying the innovation process. In one recent study published in the Proceedings of the National Academy of Sciences, for example, we found that scientific papers combining datasets in unusual ways tend to have greater impact. This suggests that innovation may emerge not only from producing new data, but also from finding unexpected connections among information that already exists.

My current work extends these questions to generative AI. I study how interacting with AI changes information seeking, creativity and knowledge work, and when AI helps people explore new possibilities versus steering them toward more similar ideas.

Tell us about your academic editorial and conference involvement.

I regularly present my research and serve as a reviewer for leading journals and conferences in computational social science-related fields, including PNAS Nexus, the International Conference on Computational Social Science (IC2S2), International Conference on the Science of Science and Innovation (ICSSI), ACM Conference on Human Factors in Computing Systems (CHI), ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), and International AAAI Conference on Web and Social Media (ICWSM).
  

What are you teaching this semester, and what do you most enjoy about teaching?

I will be designing a new course, Computational Modeling of Human Behavior, and teaching INFO 505: Foundations of Information.

I am particularly excited about bringing my expertise in computational social science into the classroom. I enjoy teaching students not only how to use computational tools, but also how to translate an interesting question about people or society into something that can actually be measured and studied with data.

How do you engage with students to foster their academic and professional growth?

I strongly believe in the idea of learning through doing. Students have different strengths, interests and stages of development, so I try to understand what is distinctive about each student.

I also enjoy involving students directly in research projects and matching projects with their interests and unique strengths. Through hands-on experience, students can gradually understand what research is really like, identify what they are good at and build confidence in their own ability to contribute.

Beyond research, service and teaching, what are your passions?

Art and creativity are an important part of who I am. I am a classically trained mezzo-soprano and particularly enjoy opera and classical singing.

I am also drawn to activities that involve exploration and a bit of challenge. I enjoy hiking and traveling, and I am continuing to train in freediving.

What advice do you have for InfoSci students?

Every person brings a unique combination of experiences, strengths and perspectives to the world. That uniqueness can become something meaningful. Rather than trying to follow exactly what everyone else is doing, spend time discovering what genuinely interests you, what you are especially good at and what kind of impact you want to have. Then keep learning and growing in that direction.
  

Learn more about Yulin Yu on her faculty page, or explore ways you can support the dynamic, student-invested faculty of the College of Information Science.