Assistant Professor of Supply Chain & Information Systems
Dr. Chen is an Assistant Professor in the Department of Supply Chain and Information Systems at Penn State University. Previously, she was an Assistant Professor of Marketing at the University of Nebraska-Lincoln College of Business from 2020 to 2022.
Dr. Chen received her Ph.D. in Business Administration with a concentration in Quantitative Marketing from Pennsylvania State University in 2020. Before her doctoral studies, Dr. Chen earned dual master's degrees in Statistics and Urban & Regional Planning from the University of Minnesota, Twin Cities, and dual Bachelor's degrees in Geography and Economics from Peking University. Before beginning her Ph.D. studies, she also gained industry experience at a Deloitte Fast 500 company.
As an empirical modeler, I am interested in developing new tools and methods for solving real-world business problems and enhancing managers’ ability to make data-driven decisions.
Methodologically, I have been exploring graphical models, network analysis, clustering, high dimensional statistics, and Bayesian econometrics to develop new approaches in business analytics. Substantively, I have been exploring topics on the interface between information systems, supply chains, and marketing.
Ph.D., Marketing, The Pennsylvania State University, 2020
M.Sc., Statistics, The University of Minnesota, Twin Cities, 2012
Master, Urban and Regional Planning, The University of Minnesota, Twin Cities, 2012
Bachelor, Geography and Economics, Peking University, 2009
MIS 441 – Bus Intelligence (3)
Application of Information Technology based methods and tools to analyze business data and support decision making. MIS 441 Business Intelligence for Decision Making (3) Business intelligence encompasses the IT tools for exploring, analyzing, integrating, and reporting business data for fact-based, intelligent decision making. This course primarily investigates methods and tools for exploring and analyzing large amounts of business data also called "Big Data." Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Students will be exposed to a variety of methods for analyzing both structured and unstructured data and they will work with business data sets to understand the value that can be extracted from large data sets. They will also learn how to classify and associate data to discover business rules that can be used to support decision making. The course will also cover methods to analyze social media information and about tools that can facilitate such analysis and discovery. Again they will get a chance to work with data from real social networks to gain an appreciation of how value can be obtained from such networks. Finally, they will learn about techniques for visualizing, presenting and communicating information in a useful way, e.g. through dashboards and with other technologies on various platforms.