Weifeng Li

Weifeng Li is an associate professor in the Department of Management Information Systems at the University of Georgia. Dr. Li received his Ph.D. in management information systems from the University of Arizona. His research interests are the security of artificial intelligence systems and the development of artificial intelligence systems for cybersecurity applications. His methodological focus includes machine learning, natural language processing, and Bayesian modeling. His research has appeared in peer-reviewed journals and conferences, including MIS Quarterly, Journal of Management Information Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Dependable and Secure Computing, ACM Computing Surveys, ICIS, and IEEE Intelligence and Security Informatics. His research has been supported by the National Science Foundation’s Secure and Trustworthy Cyberspace (SaTC) program.

• PhD, Management Information Systems, University of Arizona, 2017
• BS, Management Information Systems, Shanghai Jiao Tong University, 2012

• Large Language Models
• Machine Learning
• Cybersecurity
• Social Media Analytics
• FinTech

Journal Articles

• Ebrahimi, R., Chai, Y., Li, W., Pacheco, J., & Chen, H. (Forthcoming). RADAR: A Framework for Developing Adversarially Robust Cyber Defense AI Agents with Deep Reinforcement Learning. MIS Quarterly.
• Hughes, J., Pastrana, S., Hutchings, A., Afroz, S., Samtani, S., Li, W., & Santana Marin, E. (2024). The art of cybercrime community research. ACM Computing Surveys, 56(6), 1-26.
• Chai, Y., Liu, Y., Li, W., Zhu, B., Liu, H., & Jiang, Y. (2024). An interpretable wide and deep model for online disinformation detection. Expert Systems with Applications, 237, 121588.
• Li, W., & Chen, H. (2022). Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework. MIS Quarterly, 46(4), 2337-2350.
• Li, W., & Chai, Y. (2022). Assessing and Enhancing Adversarial Robustness of Predictive Analytics: An Empirically Tested Design Framework. Journal of Management Information Systems, 39(2), 542-572.
• Zhang, N., Ebrahimi, M., Li, W., & Chen, H. (2022). Counteracting dark Web text-based CAPTCHA with generative adversarial learning for proactive cyber threat intelligence. ACM Transactions on Management Information Systems (TMIS), 13(2), 1-21.
• Chai, Y., Zhou, Y., Li, W., & Jiang, Y. (2021). An explainable multi-modal hierarchical attention model for developing phishing threat intelligence. IEEE Transactions on Dependable and Secure Computing, 19(2), 790-803.
• Samtani, S., Li, W., Benjamin, V., & Chen, H. (2021). Informing cyber threat intelligence through dark Web situational awareness: The AZSecure hacker assets portal. Digital Threats: Research and Practice (DTRAP), 2(4), 1-10.
• Li, W., Yin, J., & Chen, H. (2017). Supervised topic modeling using hierarchical dirichlet process-based inverse regression: Experiments on e-commerce applications. IEEE Transactions on Knowledge and Data Engineering, 30(6), 1192-1205.
• Li, W., Chen, H., & Nunamaker Jr, J. F. (2016). Identifying and profiling key sellers in cyber carding community: AZSecure text mining system. Journal of Management Information Systems, 33(4), 1059-1086.