My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.
Jun Li, PhD, is Assistant Professor in the department of Technology and Operations in the Ross School of Business at the University of Michigan, Ann Arbor.
Jun Li’s main research interests are empirical operations management and business analytics, with special emphases on revenue management, pricing, consumer behavior, economic and social networks. She has worked extensively with large-scale data, including transactions, pricing, inventory and capacity, consumer online search and click stream data, supply chain relationships and disruptions, clinical and healthcare claims. She is the Winner of INFORMS Revenue Management and Pricing Practice Award for her close collaboration with retailing practitioners in implementing best response pricing algorithms. Her paper on airline pricing and consumer behavior is the finalist for Best Management Science Papers in Operations Management 2012 to 2014. She is also the principal investigator of a National Science Foundation funded project: “Gaining Visibility Into Supply Network Risks Using Large-Scale Textual Analysis”. Her work has enjoyed coverage by The Economist, New York Times and Forbes.
Building data-intensive systems that are more scalable, more robust, and more predictable. He draws from advanced statistical models to deliver practical database solutions to real-world problems. In particular, he adapts concepts and tools from applied statistics, optimization theory, and machine learning.