Yan Huang (CV)

5000 Forbes Avenue
Pittsburgh, PA 15213
Office: TEP 5106
Phone: (412) 268-4311
Email: yanhuang[at]cmu[dot]edu

SSRN | Google Scholar

I am a tenured Associate Professor of Business Technologies at the Tepper School of Business, Carnegie Mellon University. My research examines the economic and social impacts of technologies and identifies effective designs and policies for technology-enabled markets and platforms, using economic theories, structural modeling, statistical modeling, machine learning methods, and an understanding of the underlying technologies. I am an early proponent of the use of structural econometric models to study the design and policy questions in the field of information systems.

My research focuses on the following areas:

  • The economics of machine learning (papers)

  • The design of crowdsourcing and social platforms (papers)

  • Pricing and engagement strategies for digital products (papers)

My research in the area of the economics of machine learning examines the use of machine learning through an economic lens, and focuses on analyzing agents’ incentives and strategic behavior in contexts where machine learning algorithms are used to make decisions, and understanding the social and economic consequence and implications of machine learning algorithms.

My research in the area of crowdsourcing and social platforms examines the underlying economic processes that drive individuals’ behavior on crowdsourcing platforms, and evaluates the impact of design changes and policy interventions on their behavior. Findings from my studies shed light on how to design and manage specific platforms to maximize the efficiency and effectiveness of those platforms.

My research in the area of digital markets and products considers the unique characteristics of digital products, and takes advantage of increased access to micro-level data to directly measure consumer behavior and new opportunities to leverage business analytics to inform decision-making. I use an innovative data analytic approach that combines econometric or statistical methods and optimization techniques to generate prescriptive recommendations.

Prior to joining Tepper, I was an assistant professor at the Ross School of Business, University of Michigan. I received my bachelor's degree from Tsinghua University and Ph.D. degree from Carnegie Mellon University.