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Bin Zhang

Assistant Professor
Affiliated Faculty, Texas A&M Institute of Data Science
Visiting Research Fellow, Carnegie Mellon University
Department of Information and Operations Management

Education

Ph.D., Information Systems Management, Carnegie Mellon University
M.S., Machine Learning, Carnegie Mellon University

Research Interest

Very large social network analysis, statistical modeling for social network problems, social media, business analytics and deep learning

Courses Taught

ISTM 450: Business Intelligence and Data Mining
ISTM 650: Business Data Mining
ISTM 689: Business Data Mining (Online)

Biography

Bin Zhang is an Assistant Professor of Information & Operations Management at Mays Business School. He has joined Texas A&M since July 2021.

Dr. Zhang’s primary research interests are social media analytics and machine learning. He is specifically interested in designing algorithms to analyze large social network and developing deep learning methods to investigate social media content such as text, image and video. His work has been published in premier IS journals such as MIS Quarterly, Information Systems Research, Journal of Management Information Systems, etc. His projects have been funded by federal agencies such as NSF and NIH. Dr. Zhang has extensive teaching experience in Database, Data Mining, Data Analytics, and Programming Languages. All of these courses receive very high evaluation from the students.

Dr. Zhang has served on multiple review panels for NSF, and is the previous Vice Chair of INFORMS College of AI. He is also a Visiting Research Fellow affiliated with iLab at Carnegie Mellon University.

Research Publications

Guo, C., Zhang, B., Chen, X., & Goes, P. (2024). Using “Super Apps” in Online Retailing Operations: Evidence from a Large Hotel Chain. Production and Operations Management. https://doi.org/10.1177/10591478231224908

Benjamin, V., Zhang, B., Nunamaker, J. F., & Chen, H. (2016). Examining hacker participation length in cybercriminal Internet-Relay-Chat communities. Journal of Management Information Systems, 33(2), 482–510. https://doi.org/10.1080/07421222.2016.1205918

NSF Award Search: Award # 1261977 - The structure and dynamics of generative innovations: An organizational genetics approach. (n.d.). Retrieved from https://www.nsf.gov/awardsearch/showAward?AWD_ID=1261977

Um, S., Zhang, B., Wattal, S., & Yoo, Y. (2022). Software components and product variety in a platform ecosystem: A dynamic network analysis of WordPress. Information Systems Research. https://doi.org/10.1287/isre.2022.1172

XieJiaheng, ZhangBin, MaJian, ZengDaniel, & Lo-CiganicJenny. (2021). Readmission Prediction for Patients with Heterogeneous Medical History: A Trajectory-Based Deep Learning Approach. ACM Transactions on Management Information Systems, 13(2), 1–27. https://doi.org/10.1145/3468780

Zhang, B., Pavlou, P. A., & Krishnan, R. (2018). On Direct vs. Indirect Peer Influence in Large Social Networks. Information Systems Research, 29(2), 292–314. https://doi.org/10.1287/isre.2017.0753

Zhao, K., Zhang, B., & Bai, X. (2018). Estimating contextual motivating factors in virtual interorganizational communities of practice: peer effects and organizational influences. Information Systems Research, 29(4), 910–927. https://doi.org/10.1287/isre.2017.0752