Faculty Directory | Elynn Chen

Elynn Chen is an Assistant Professor of Technology, Operations and Statistics at NYU Stern School of Business. Her research develops the statistical foundations for structured learning and decision-making in modern business and AI systems, where data are high-dimensional, heterogeneous, and constantly shifting, and where reliable decisions require methods that preserve structure and quantify uncertainty.

Her work spans three connected streams: structured multi-way learning for matrix, tensor, and network data; learning and decision-making under heterogeneity, including reinforcement learning, bandits, and dynamic pricing across diverse populations and environments; and statistical decision analytics for business and AI systems, from revenue management to generative and agentic AI. Her research appears in the Journal of the American Statistical Association, the Journal of the Royal Statistical Society, Series B, the Annals of Statistics, and Management Science, and at NeurIPS, ICML, ICLR, and AISTATS. It is supported by an NSF DMS Research Award (2024–2027), following an earlier NSF Postdoctoral Fellowship. She serves as an Associate Editor of ACM Transactions on Knowledge Discovery from Data and as an Area Chair for NeurIPS and ICML.

Before joining NYU Stern, Professor Chen was a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, a research fellow at OpenAI, and a postdoctoral researcher in the Department of Operations Research and Financial Engineering at Princeton University. She received her PhD in Statistics from Rutgers University.

For a current list of publications, see her Google Scholar profile: https://scholar.google.com/citations?user=h5jI05UAAAAJ&sortby=pubdate