Bio

Yutong Xie is a Ph.D. candidate and Barbour Scholar at the University of Michigan School of Information, advised by Prof. Qiaozhu Mei. Her research spans AI behavioral science, AI for science, and AI for innovation, with publications in top-tier venues like PNAS, ICLR, AAAI, WWW, and NAACL. Yutong actively engages in the academic community, co-organizing workshops, and serving as a regular reviewer for conferences such as NeurIPS, ICML, AAAI, KDD, and WWW. Her research has been recognized with prestigious awards including Barbour Scholarship, Gary M. Olson Outstanding Ph.D. Student Award, and D. E. Shaw Research Graduate Women’s Fellowship.

Measuring and Modulating the Social Impact of AI: From Models to Humans

As artificial intelligence systems become interwoven with everyday life, they increasingly shape how people work, make decisions, and relate to one another. Yet fundamental questions remain: Can AI accurately discern human intentions, and can it act as a trustworthy, thoughtful partner? My research addresses these challenges through AI behavioral science, an emerging field at the intersection of artificial intelligence and behavioral science dedicated to fostering mutual understanding between humans and machines.

I pursue this goal through three complementary lines of work: (1) applying behavioral-science methods to analyze and guide AI behaviors; (2) leveraging AI to illuminate human decision-making and enrich behavioral research; and (3) developing foundation models that integrate behavioral data with computational reasoning.

Together, these efforts establish the conceptual and technical bases for cultivating a trustworthy human-AI ecosystem. By jointly modeling how AI acts, how it interprets people, and how it can support behavioral inquiry, my work aims to inform the design of responsible AI systems while deepening the scientific study of human behavior.