Bill Rand
Business Management
- Phone: 919.515.6947
- Email: wmrand@ncsu.edu
- Office: 2324 Nelson Hall
Bill Rand examines the use of computational modeling techniques, such as agent-based modeling, machine learning, network analysis, natural language processing, and geographic information systems, to help understand and analyze complex systems, such as the diffusion of information, organizational learning, and economic markets.
He also works to develop methods, create pedagogy, and build frameworks to allow researchers and practitioners to use analytics and data-intensive methods in their own work.
He has received funding for his research from the NSF, DARPA, ARL, Google, WPP, and the Marketing Science Institute. His work has been published in JM, JMR, IJRM, Management Science, and JOM.
He received his doctorate in Computer Science from the University of Michigan in 2005 and prior to coming to NC State was at the University of Maryland for eight years.
Research Interests
- Social Media
- Misinformation
- Machine Learning
- Agent-based Modeling
- Digital Marketing
Education
| Degree | Program | School | Year |
|---|---|---|---|
| Ph.D. | Doctor of Philosophy in Computer Science | University of Michigan | 2005 |
Discover more about Bill Rand
- Bill Rand’s New Tool Explores Brand Performance (Feb. 25, 2021)
- Bill Rand Gives Expert Advice on Gas Rewards Credit Cards (Feb. 12, 2021)
- New Business Analytics Initiative Combines Data, Innovation, Leadership (Aug. 31, 2020)
- Professor Bill Rand: Understanding Complex Networks, Data-driven Decisionmaking (May 23, 2019)
- Nine Poole College Faculty Receive Summer Research Awards 2019 (April 15, 2019)
- Poole College Professors Recognized for Research and Teaching (April 12, 2017)
Publications
- A GENERALIZATION OF THRESHOLD-BASED AND PROBABILITY-BASED MODELS OF INFORMATION DIFFUSION
- Jayalath, C., Gunaratne, C., Rand, W., Seneviratne, C., & Garibay, I. (2023), ADVANCES IN COMPLEX SYSTEMS, 26(02). https://doi.org/10.1142/S0219525923500054
- Classification of social media users with generalized functional data analysis
- Weishampel, A., Staicu, A.-M., & Rand, W. (2023), COMPUTATIONAL STATISTICS & DATA ANALYSIS, 179. https://doi.org/10.1016/j.csda.2022.107647
- Special Section on "Inverse Generative Social Science": Guest Editors? Statement
- Epstein, J. M., Garibay, I., Hatna, E., Koehler, M., & Rand, W. (2023), JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 26(2). https://doi.org/10.18564/jasss.5085
- Two decades of agent-based modeling in marketing: a bibliometric analysis
- Romero, E., Chica, M., Damas, S., & Rand, W. (2023). [Review of , ]. PROGRESS IN ARTIFICIAL INTELLIGENCE, 12(3), 213–229. https://doi.org/10.1007/s13748-023-00303-y
- Entropy-Based Characterization of Influence Pathways in Traditional and Social Media
- Garibay, O. O., Yousefi, N., Aslett, K., Baggio, J., Hemberg, E., Jayalath, C., … Garibay, I. (2022), 2022 IEEE 8TH INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING, CIC, pp. 38–44. https://doi.org/10.1109/CIC56439.2022.00016
- Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media
- Gunaratne, C., De, D., Thakur, G., Senevirathna, C., Rand, W., Smyth, M., & Lipscomb, M. (2022), SOCIAL, CULTURAL, AND BEHAVIORAL MODELING (SBP-BRIMS 2022), Vol. 13558, pp. 24–34. https://doi.org/10.1007/978-3-031-17114-7_3
- Simplicity is not key: Understanding firm-generated social media images and consumer liking
- Overgoor, G., Rand, W., Dolen, W., & Mazloom, M. (2022), INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING, 39(3), 639–655. https://doi.org/10.1016/j.ijresmar.2021.12.005
- Agent-based modeling of new product market diffusion: an overview of strengths and criticisms
- Rand, W., & Stummer, C. (2021), ANNALS OF OPERATIONS RESEARCH, 305(1-2), 425–447. https://doi.org/10.1007/s10479-021-03944-1
- Apart we ride together: The motivations behind users of mixed-reality sports
- Westmattelmann, D., Grotenhermen, J.-G., Sprenger, M., Rand, W., & Schewe, G. (2021), JOURNAL OF BUSINESS RESEARCH, 134, 316–328. https://doi.org/10.1016/j.jbusres.2021.05.044
- Inferring mechanisms of response prioritization on social media under information overload
- Gunaratne, C., Rand, W., & Garibay, I. (2021), SCIENTIFIC REPORTS, 11(1). https://doi.org/10.1038/s41598-020-79897-5
