Carnegie Mellon University

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January 24, 2018

Business Analytics Professor Dokyun Lee Wins Best Paper

Dokyun Lee, assistant professor of business analytics, Xerox Junior Faculty Chair AY 2017-2018, was recognized at the 2017 International Conference in Information Systems (ICIS) with the Best Track Paper in IT and Social Change as well as Best Conference Paper.

“Micro-Giving: On the Use of Mobile Devices and Monetary Subsidies in Charitable Giving,” which Lee coauthored with Dongwon Lee of the Hong Kong University of Science and Technology and Anand Gopal of the University of Maryland, investigates the effects of push notification, monetary subsidies, and intertemporal choice of monetary subsidy on donation outcomes in mobile cause marketing context. In the context of “micro-giving” — generally small donations by individuals — the study quantifies the effect of push notifications from the app and examines the impact of rebate subsidies, matching subsidies, and intertemporal choice on donation decision and amount.

“ICIS is the largest conference for information systems and business technologies, so this recognition is a great honor,” Lee said. “This kind of micro-giving can make a big difference for organizations that rely on charitable donations.”

ICIS is an annual research conference hosted by the Association for Information Systems. The 2017 event, held in December in Seoul, South Korea, was organized around the theme “Transforming Society with Digital Innovation.” Lee’s paper was submitted within the IT and social change track, one of more than 20 topics under the conference’s theme, and was selected by the track committee as the Best Track Paper. The conference committee then selected it as Best Conference Paper out of each of the winning track papers.

Lee has been a professor at the Tepper School since 2015, after earning his Ph.D. in Operation and Information Management from The Wharton School at the University of Pennsylvania. He holds a master’s degree in statistics from Yale University and a bachelor’s degree in computer science from Columbia University.

His main research area of interest is applying machine learning to solve business problems and includes projects in:

  • Content engineering in social media advertising and e-commerce using natural language processing (NLP) and computer vision.
  • Deep learning based NLP method in interpretable machine learning for business applications.
  • Unintended consequence of algorithmic bias in business.
  • Sharing economy.

He serves on the Business Technology (BT) Ph.D. selection committee, the BT Hiring Committee, the MEAC committee, and organizes BT seminar at the Tepper School, and contributed to the development of the new Master of Science in Business Analytics curriculum. He has received numerous awards for his research, including several best conference paper awards, the Adobe Data Science Faculty Grant for Deep Learning, an NVIDIA Academic GPU grant for Deep Learning Projects, an NET Institute Research Grant, and the Marketing Science Institute Research Grant.