About

Expanding Intelligence. Advancing Humanity.

I am a Postdoctoral Research Fellow affiliated with the Center for Science of Science & Innovation at Kellogg, led by Dashun Wang. I am also affiliated with the Northwestern Innovation Institute (NII) and the Ryan Institute on Complexity. We’re building unique platforms and exciting research to cultivate intelligence, connect it, and maximize its impact. More on our “startup” effort to ask and solve first-order questions soon.

I got my PhD in Information Systems at the Michael G. Foster School of Business, University of Washington (2026). I was fortunate to work under the supervision of Professor Yong Tan. Before my Ph.D. career, I studied Information Management and Information Systems at the School of Economics and Management, Tsinghua University (2016-2021).

I study the economics of human–AI and AI–AI interactions to augment human intelligence, improve well-being, and enhance social welfare in high-stakes domains. My current research spans cultivating intelligence through education, expanding its frontiers through science, and translating it into technological innovation and work. I examine these interactions at the individual, market, and societal levels, introducing empirical insights into design principles for AI systems, human–AI collaboration, market mechanisms, and public policy.

I take a multidisciplinary approach, combining economic tools (field experiments, stylized models, structural estimation) with AI methods (primarily reinforcement learning and large language models) to analyze data, infer causality, and optimize policies. My work has appeared in Management Science and Information Systems Research, and has received paper awards from INFORMS, WITS, ICIS, and DSI.

Beyond academic research, I collaborate closely with leading institutions to design, implement, and evaluate policies and algorithms in high-stakes settings. Several interventions I proposed have been deployed in production, where they continue to generate substantial impact.

I enjoy diving deep into data patterns and modeling techniques so that I understand the details firsthand. I also believe good science comes from quiet time and deep thinking (slow science with fast tests) rather than from shallow discussion. Protecting that time means two commitments: First, I keep new collaborations to a small number each year. Second, I prefer meetings with a clear agenda and questions we’ve all thought through in advance. Beyond meetings, I often share developed ideas as written notes, and I welcome the same in return.

For professional contact, please email cedric.x.xu@gmail.com. (For communication within NU, please search “Xingchen Xu” in the email system.)

Note: Only the papers listed on this website represent my views.

Updated: 2026/10