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

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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 841 – 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.

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.

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.

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.

Selected Publications

Zhang R., Ku B., Wang Y., Yue X., Liu S., Li K., Qu H., "iFUNDit: Visual Profiling of Fund Investment Styles." Computer Graphics Forum, 2023

Zhou F., Wang G., Zhang K., Liu S., Zhong T., "Semi-Supervised Anomaly Detection via Neural Process." IEEE Transactions on Knowledge and Data Engineering, 2023

Xiao C., Xu X., Zhang K., Liu S., Zhou F., "Counterfactual Graph Learning for Anomaly Detection on Attributed Networks." IEEE Transactions on Knowledge and Data Engineering, 2023

Xu X., Zhou F., Zhang K., Liu S., Trajcevski G., "CasFlow: Exploring Hierarchical Structures and Propagation Uncertainty for Cascade Prediction." IEEE Transactions on Knowledge and Data Engineering, 2022

Xu X., Zhou F., Zhang K., Liu S., "CCGL: Contrastive Cascade Graph Learning." IEEE Transactions on Knowledge and Data Engineering, 2022

Ho I., Liu S., Pu J., Zhang D., "Is it all about You or Your Driving? Designing IoT-enabled Risk Assessments." Production and Operations Management, 2022

Ho I., Liu S., Wang L., "Fun Shopping - A Randomized Field Experiment on Gamification." Information Systems Research, 2022

Liu S., Li J., Zhang K., Tang S., "Responsible IS by Design: A Psychology-Informed Social Connection Recommender System for Mental Health." 2021 INFORMS Workshop on Data Science, Best Paper Award, 2021

Huang K., Han Y., Chen K., Pan H., Zhao G., Yi W., Li X., Liu S., Wei P., Wang L., "A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping." Nature Communications, 2021

Guo H., Zhang D., Liu S., Wang L., Ding, "Bitcoin Price Forecasting: A Perspective of Underlying Blockchain Transactions." Decision Support Systems, 2021

Zhao B., Xu C., Liu S., Zhao J., Li L., "Dynamic Traffic Bottlenecks Identification Based on Congestion Diffusion Model by Influence Maximization in Metrocity Scales." Concurrency and Computation: Practice and Experience, 2021

Tang S., Liu S., Han X., Qiao Y., "Towards Robust Monitoring of Malicious Outbreak." INFORMS Journal on Computing, 2021

Liu S., Tang S., Zheng J., Ni L., "Unsupervised Learning for Human Mobility Behaviors." INFORMS Journal on Computing, 2021

Cheng P., Lian X., Chen L., Liu S., "Maximizing the Utility in Location-Based Mobile Advertising." IEEE Transactions on Knowledge and Data Engineering, 2020

Ghose A., Li B., Liu S., "Mobile Advertising Using Customer Trajectory Patterns." Management Science, 2019

Yue X., Shu X., Zhu X., Du X., Yu Z., Papadopoulos D., Liu S., "BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence." IEEE transactions on visualization and computer graphics, 2019

Cheng P., Lian X., Chen L., Liu S., "Maximizing the Utility in Location-Based Mobile Advertising." 2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019

Zhao B., Xu C., Liu S., Zhao J., Li L., "A Congestion Diffusion Model with Influence Maximization for Traffic Bottleneck Identification in Metrocity Scales." 2019 IEEE International Conference on Big Data, 2019

Liu S., Qu Q., Wang S., "Heterogeneous anomaly detection in social diffusion with discriminative feature discovery." Information Sciences, 2018

He B., Chen X., Zhang D., Liu S., Han D., Ni L., "PBE: Driver Behavior Assessment Beyond Trajectory Profiling." 2018 Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2018

He B., Zhang D., Liu S., Liu H., Han D., Ni L., "Profiling Driver Behavior for Personalized Insurance Pricing and Maximal Profit." 2018 IEEE International Conference on Big Data, 2018

Liu S., Zhang J., "Big Data, Big Marketing." (China Development Press), 2017

Le T. V., Oentaryo R., Liu S., Lau H. C., "Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction." IEEE Transactions on Big Data, vol. 3, no. 2, 2017, pp. 194–207

Zhao J., Qu Q., Zhang F., Xu C., Liu S., "Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data." IEEE Transactions on Intelligent Transportation Systems, 2017

Liu S., Wang S., "Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling." IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 4, 2017, pp. 898–911

Wu T., Zhang C. J., Chen L., Hui P., Liu S., "Object identification with Pay-As-You-Go crowdsourcing." 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 578–585

Le T. V., Liu S., Lau H. C., "Reinforcement learning framework for modeling spatial sequential decisions under uncertainty." Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016, pp. 1449–1450

Zhao K., Tarkoma S., Liu S., Vo H., "Urban human mobility data mining: An overview." 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 1911–1920

Hua Y., Wang S., Liu S., Cai A., Huang Q., "Cross-modal correlation learning by adaptive hierarchical semantic aggregation." IEEE Transactions on Multimedia, vol. 18, no. 6, 2016, pp. 1201–1216

Liu S., Qu Q., "Dynamic collective routing using crowdsourcing data." Transportation Research Part B: Methodological, vol. 93, 2016, pp. 450–469

Qu Q., Liu S., Zhu F., Jensen C. S., "Efficient Online Summarization of Large-Scale Dynamic Networks." IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, 2016, pp. 3231–3245

Liu S., Qu Q., Chen L., Ni L. M., "SMC: A practical schema for privacy-preserved data sharing over distributed data streams." IEEE Transactions on Big Data, vol. 1, no. 2, 2015, pp. 68–81

Liu S., Qu Q., Wang S., "Rationality analytics from trajectories." ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 10, no. 1, 2015, pp. 10

Liu S., Wang S., Liu C., Krishnan R., "Understanding taxi drivers’ routing choices from spatial and social traces." Frontiers of Computer Science, 2015, pp. 1–10

Liu S., Wang S., Zhu F., "Structured Learning from Heterogeneous Behavior for Social Identity Linkage." IEEE Transactions on Knowledge and Data Engineering, 2015

Ghose A., Li B., Liu S., "Trajectory-based Mobile Advertising." Proceedings of the International Conference on Information Systems (ICIS), 2015

Le T. V., Liu S., Lau H. C., Krishnan R., "Predicting bundles of spatial locations from learning revealed preference data." Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015, pp. 1121–1129

Xie W., Zhu F., Liu S., Wang K., "Modelling cascades over time in microblogs." 2015 IEEE International Conference on Big Data (Big Data), 2015, pp. 677–686

Zhang Y., Li B., Krishnan R., Liu S., "Learning from the Offline Trace: A Case Study of the Taxi Industry." Thirty Sixth International Conference on Information Systems, 2015

Liu S., Yue Y., Krishnan R., "Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments." IEEE Transactions on Knowledge and Data Engineering, 2015

Guo X., Chan E. C., Liu C., Wu K., Liu S., Ni L. M., "Shopprofiler: Profiling shops with crowdsourcing data." 2014 Proceedings IEEE INFOCOM, 2014, pp. 1240–1248

Pu J., Liu S., Xu P., Qu H., Ni L. M., "MViewer: mobile phone spatiotemporal data viewer." Frontiers of Computer Science, vol. 8, no. 2, 2014, pp. 298–315

Liu S., Kang L., Chen L., Ni L. M., "How to Conduct Distributed IncompletePattern Matching." IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 4, 2014, pp. 982–992

Liu S., Ni L. M., Krishnan R., "Fraud detection from taxis’ driving behaviors." IEEE Transactions on Vehicular Technology, vol. 63, no. 1, 2014, pp. 464–472

Liu S., Chen L., Ni L. M., "Anomaly detection from incomplete data." ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 9, no. 2, 2014, pp. 11

Lu L., Cao N., Liu S., Ni L., Yuan X., Qu H., "Visual analysis of uncertainty in trajectories." Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2014, pp. 509–520

Zheng J., Liu S., Ni L. M., "User characterization from geographic topic analysis in online social media." 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2014, pp. 464–471

Hua Y. T., Wang S., Liu S., Huang Q., Cai A., "TINA: Cross-modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation." 2014 IEEE International Conference on Data Mining (ICDM), 2014

Zheng J., Liu S., Ni L. M., "Robust Bayesian Inverse Reinforcement Learning with Sparse Behavior Noise.." AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014, pp. 2198–2205

Liu S., Wang S., Krishnan R., "Persistent community detection in dynamic social networks." Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2014, pp. 78–89

Qu Q., Liu S., Jensen C. S., Zhu F., Faloutsos C., "Interestingness-driven diffusion process summarization in dynamic networks." Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2014, pp. 597–613

Qu Q., Liu S., Yang B., Jensen C. S., "Integrating non-spatial preferences into spatial location queries." Proceedings of the 26th International Conference on Scientific and Statistical Database Management, 2014, pp. 8

Liu S., Wang S., Zhu F., Zhang J., Krishnan R., "Hydra: Large-scale social identity linkage via heterogeneous behavior modeling." Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, 2014, pp. 51–62

Qu Q., Liu S., Yang B., Jensen C. S., "Efficient top-k spatial locality search for co-located spatial web objects." 2014 IEEE 15th International Conference on Mobile Data Management (MDM), vol. 1, 2014, pp. 269–278

Zheng J., Liu S., Ni L. M., "Effective mobile context pattern discovery via adapted hierarchical dirichlet processes." 2014 IEEE 15th International Conference on Mobile Data Management (MDM), vol. 1, 2014, pp. 146–155

Chu L., Wang S., Liu S., Huang Q., Pei J., "ALID: Scalable Dominant Cluster Detection." VLDB 2015, 2014

Liu S., Yue Y., Krishnan R., "Adaptive collective routing using gaussian process dynamic congestion models." Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, pp. 704–712

Liu S., Pu J., Luo Q., Qu H., Ni L. M., Krishnan R., "Vait: A visual analytics system for metropolitan transportation." IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, 2013, pp. 1586–1596

Liu S., Liu Y., Ni L., Li M., Fan J., "Detecting crowdedness spot in city transportation." IEEE Transactions on Vehicular Technology, vol. 62, no. 4, 2013, pp. 1527–1539

Liu S., Araujo M., Brunskill E., Rossetti R., Barros J., Krishnan R., "Understanding sequential decisions via inverse reinforcement learning." 2013 IEEE 14th International Conference on Mobile Data Management (MDM), vol. 1, 2013, pp. 177–186

Liu S., Wang S., Jayarajah K., Misra A., Krishnan R., "TODMIS: Mining communities from trajectories." Proceedings of the 22nd ACM International Conference on Information & Knowledge Management (CIKM), 2013, pp. 2109–2118

Pu J., Liu S., Ding Y., Qu H., Ni L., "T-watcher: A new visual analytic system for effective traffic surveillance." 2013 IEEE 14th International Conference on Mobile Data Management (MDM), vol. 1, 2013, pp. 127–136

Liu S., Krishnan R., Brunskill E., Ni L. M., "Modeling Social Information Learning among Taxi Drivers." Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2013, pp. 73–84

Ding Y., Liu S., Pu J., Ni L. M., "Hunts: A trajectory recommendation system for effective and efficient hunting of taxi passengers." 2013 IEEE 14th International Conference on Mobile Data Management (MDM), vol. 1, 2013, pp. 107–116

Liu S., Li L., Krishnan R., "Hibernating Process: Modelling Mobile Calls at Multiple Scales." 2013 IEEE 13th International Conference on Data Mining (ICDM), 2013, pp. 1139–1144

Zheng J., Liu S., Ni L., "Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering." 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2013

Liu S., Liu C., Luo Q., Ni L. M., Krishnan R., "Calibrating large scale vehicle trajectory data." 2012 IEEE 13th International Conference on Mobile Data Management (MDM), 2012, pp. 222–231

Liu S., Kang L., Chen L., Ni L., "Distributed Incomplete Pattern Matching via a Novel Weighted Bloom Filter." 2012 IEEE 32nd International Conference on Distributed Computing Systems (ICDCS), 2012, pp. 122–131

Liu C., Liu S., Hamdi M., "GeRA: Generic rate adaptation for vehicular networks." 2012 IEEE International Conference on Communications (ICC), 2012, pp. 5311–5315

Pu J., Liu S., Qu H., Ni L. M., "Visual Fingerprinting: A New Visual Mining Approach for Large-Scale Spatio-temporal Evolving Data." Event8th International Conference on Advanced Data Mining and Applications (ADMA 2012), 2012, pp. 502–515

Gao Y., Xu P., Lu L., Liu H., Liu S., Qu H., "Visualization of taxi drivers’ income and mobility intelligence." Advances in Visual Computing, 2012, pp. 275–284

Liu S., Liu C., Luo Q., Ni L. M., Qu H., "A visual analytics system for metropolitan transportation." Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2011, pp. 477–480

Liu S., Chen L., Ni L. M., Fan J., "Cim: categorical influence maximization." Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, 2011, pp. 124

Pu J., Xu P., Qu H., Cui W., Liu S., Ni L., "Visual analysis of people’s mobility pattern from mobile phone data." Proceedings of the 2011 Visual Information Communication-International Symposium, 2011, pp. 13

Zhang D., Yang Y., Cheng D., Liu S., Ni L. M., "COCKTAIL: An RF-based hybrid approach for indoor localization." 2010 IEEE International Conference on Communications (ICC), 2010, pp. 1–5

Liu S., Liu Y., Ni L. M., Fan J., Li M., "Towards mobility-based clustering." Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 919–928

Liu S., Wen G., Fan J., "A 3d geosciences modeling system for large-scale water-diversion projects." Computing in science & engineering, vol. 12, no. 1, 2010, pp. 28–35

Research Impact and Media Mentions

"New type of mobile tracking link shoppers' physical movements, buying choices", Science Daily, Internet, www.sciencedaily.com/releases/2019/03/190325080448.htm

"Anticipating Customers’ Next Steps", Insights from MSI, Journal or Magazine