Qian Chen
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.
Expertise
Her research focuses on business analytics, digital marketing, and operations management, while also examining the broader impact of artificial intelligence (AI) on business and society. She leverages methods and tools from machine learning, graphical models, network analysis, and optimization to enhance data-driven decision-making and drive business innovation.
Education
Ph.D. in Marketing, The Pennsylvania State University, Smeal College of Business, University Park, PA, 2020-01-01 M.Sc. in Statistics, The University of Minnesota, Twin Cities, Twin Cities, MN, 2012-01-01 Master in Urban and Regional Planning, The University of Minnesota, Twin Cities, Twin Cities, MN, 2012-01-01 Bachelor in Geography and Economics, Peking University, Beijing, China, 2009-07-01
Courses Taught
BA 841 – BUS INTELLIGENCE
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 tools that can facilitate such analysis and discovery. Students will 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. Upon successful completion of this course, students will have:- acquired the tools and techniques of data cleaning and preparation, data mining, and data visualization- become competent in analyzing both structured and unstructured data- developed an understanding of, and an appreciation for, the complexities of mining unstructured data such as text data includingdocuments, web pages, emails, etc.- developed an understanding of social networks as well as mobile and location-based analytics
MIS 441 – Applied AI and ML in Business
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.
MIS 441 – Bus Intelligence
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.
Selected Publications
Tao J., Chen Q., Snyder Jr. J. W., Kumar A. S., Meisami A., Xue L. "A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution" Management Science, vol. 71, 2025-01-01
Chen Q., Agarwal A., Fong D., Desarbo W., Xue L. "Model-Based Co-Clustering in Customer Targeting Utilizing Large-Scale Online Product Rating Networks" Journal of Business and Economic Statistics, vol. 43, 2025-01-01
Li D., Srinivasan A., Chen Q., Xue L. "Robust covariance matrix estimation for high-dimensional compositional data with application to sales data analysis" Journal of Business & Economic Statistics, vol. 41, 2023-01-01
Lee K. H., Chen Q., Desarbo W., Xue L. "Estimating finite mixtures of ordinal graphical models" Psychometrika, vol. 87, 2022-01-01
Desarbo W., Chen Q., Stadler Blank A. "A parametric constrained segmentation methodology for application in sport marketing" Customer Needs and Solutions, vol. 4, 2017-01-01
Cao X., Chen Q., Choo S. "Geographic distribution of E-shopping: application of structural equation models in the Twin Cities of Minnesota" Transportation research record, vol. 2383, 2013-01-01
Fan Y., Chen Q., Liao C., Douma F. "UbiActive: Smartphone-based tool for trip detection and travel-related physical activity assessment" 2013-01-01
Fan Y., Chen Q. "Family functioning as a mediator between neighborhood conditions and children's health: Evidence from a national survey in the United States" Social Science & Medicine, vol. 74, 2012-01-01
Fan Y., Das K. V., Chen Q. "Neighborhood green, social support, physical activity, and stress: Assessing the cumulative impact" Health & place, vol. 17, 2011-01-01