Siyuan Liu
Associate Professor
Department Supply Chain & Information Systems
Office Address 423 Business Building
Phone Number
814-865-5944
Email Address
psu.edu@psu.edu
Siyuan Liu

Associate Professor
Department Supply Chain & Information Systems
Office Address 423 Business Building
Phone Number
814-865-5944
Email Address
psu.edu@psu.edu
Siyuan Liu is Dr. John Coyle Early Career Professor and Associate Professor of Information Systems at the Department of Supply Chain & Information Systems, Smeal College of Business, Pennsylvania State University. Dr. Liu’s research interests are in the intersection of computer science and business analytics with a focus on trajectory analytics and heterogeneous behavior models. His work has been published in prestigious journals including Management Science, Information Systems Research, Production and Operations Management, Nature Communications, INFORMS Journal on Computing, Transportation Research Part B: Methodological, Decision Support Systems, IEEE Transactions on Knowledge and Data Engineering, Information Sciences, IEEE Transactions on Big Data, IEEE Transactions on Multimedia, ACM Transactions on Knowledge Discovery from Data, and IEEE Transactions on Visualization & Computer Graphics. He received several awards including Dr. John Coyle Early Career Professorship in Supply Chain, Management Science Best Paper Award in Information Systems (Finalist) 2022, INFORMS Data Science Best Paper Award 2021, CPIC Research Achievement Award 2019, Marketing Science Institute Award, Google Internet of Things Technology Research Award, Google Faculty Research Award, and USDOT National University Transportation Center for Safety Award. He received his Ph.D. degree from the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology.
Expertise
Trajectory analytics, heterogeneous behavior models, mobile data mining, business analytics, AI for business, and new technology for the digital economy.
Education
Ph D, Hong Kong University of Science and Technology, 2011
Courses Taught
BAN 541 – Data Mining for Business (3)
Intended for recent graduates with little to no professional experience, BAN 541 develops business students' understanding of and ability to apply a variety of data mining tools and techniques for use in detecting and exploiting patterns and relationships in large structured and unstructured data sets derived from a variety of business scenarios. Students will explore the use of cluster analysis, classification, association, and cause-and-effect modeling techniques to explore and reduce data, classify new data elements, identify natural associations among variables, create rules for target marketing or buying recommendations, and describe relationships among data that motivate business performance. Specific techniques may include k-nearest neighbor, discriminant analysis, and association rule mining. Students will learn how to bridge descriptive and predictive analytics across a variety of business scenarios. Coursework includes individual assignments intended to develop confidence with basic data mining techniques, followed by case-based problems that challenge students' creativity and data mining mastery in search of patterns and data relationships leading to useful business insights. While underlying theory will be discussed, the course will prepare business analysts by focusing specifically on data mining applications in marketing, finance, supply chain management, and other business areas, with an emphasis on the unique aspects of decision making in a business environment. Software packages, concepts, and business applications will vary and evolve to keep pace with technology, theory, and instructor interest.
SCM 496 – Indep Studies (Variable)
Creative projects, including research and design, that are supervised on an individual basis and that fall outside the scope of formal courses.
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.
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 includingdocuments, web pages, emails, etc.- developed an understanding of social networks as well as mobile and location-based analytics
MIS 301 – Business Analytics (3)
MIS 301 investigates use of databases, basic data mining tools, social networking software, and advanced level of spreadsheet management for analysis of large amounts of data. Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Topics may include methods for analyzing not only structured data, but also unstructured data from the web, emails, blogs, social networks, click streams, etc. Finally, techniques for visualizing, presenting and communicating information in a useful way will be presented.
MIS 494H – Research Project (Variable)
Supervised student activities on research projects identified on an individual or small-group basis.