Qian Chen

Color portrait of Qian Chen

Assistant Professor of Supply Chain & Information Systems

Department Supply Chain & Information Systems
Office Address 472 Business Building
Phone Number 814-865-0684
Email Address quc20@psu.edu

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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.

Expertise

I am interested in developing new tools and methods, conducting empirical analyses to solve real-world business problems, and enhancing managers’ ability to make data-driven decisions.

Substantively, I have been exploring topics on the interface between information systems, supply chains, and marketing. Methodologically, I have been exploring graphical models, network analysis, clustering, high dimensional statistics, and Bayesian econometrics to develop new approaches in business analytics.

Education

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

Courses Taught

BA 841 – BUS INTELLIGENCE (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 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 including documents, web pages, emails, etc. - developed an understanding of social networks as well as mobile and location-based analytics

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.

Selected Publications

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, no. 4, 2023, pp. 1090-1100
Lee K. H., Chen Q., Desarbo W., Xue L., "Estimating finite mixtures of ordinal graphical models." Psychometrika, vol. 87, no. 1, 2022, pp. 83--106
*Qian Chen is the co-first author.
Desarbo W., Chen Q., Stadler Blank A., "A parametric constrained segmentation methodology for application in sport marketing." Customer Needs and Solutions, vol. 4, 2017, pp. 37--55
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, no. 1, 2013, pp. 18--26
Fan Y., Chen Q., Liao C., Douma F., "UbiActive: Smartphone-based tool for trip detection and travel-related physical activity assessment." 2013
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, no. 12, 2012, pp. 1939--1947
Fan Y., Das K. V., Chen Q., "Neighborhood green, social support, physical activity, and stress: Assessing the cumulative impact." Health & place, vol. 17, no. 6, 2011, pp. 1202--1211