Ran Chen

Ran Chen is an Assistant Professor of Statistics and Data Science at Washington University in St. Louis. She holds a Ph.D. from the Wharton School of the University of Pennsylvania and a B.S. in Pure and Applied Mathematics from Tsinghua University. Prior to her current position, she was a postdoctoral researcher at MIT’s Laboratory for Information and Decision Systems.

Her research focuses on reinforcement learning, data-driven decision-making, optimization, and statistical machine learning, with applications in healthcare and revenue management. Key areas include high-dimensional and nonparametric statistics, interpretable models for business decisions, and algorithmic frameworks balancing statistical accuracy and computational efficiency.

Her work spans theoretical, methodological, and applied contributions, such as doubly high-dimensional contextual bandits for retail optimization, personalized reinforcement learning frameworks, and novel Poisson-MNL models for dynamic customer arrivals. She has presented her research at institutions like Harvard, UC Davis, and the INFORMS Business Analytics Conference.

Teaching experience includes courses on Python for data science, forecasting methods, and introductory statistics at the University of Pennsylvania. She has advised students in optimization and machine learning contexts but no named advisees are listed in the provided text.

Notable publications include advancements in convex function estimation, low-rank matrix models for bandits, and supervised centrality estimation in networks. Her research emphasizes practical applications while maintaining rigorous statistical foundations.