Faculty/Staff

Reza Ebrahimi

Mohammedreza (Reza) Ebrahimi

 

Assistant Professor
ebrahimim@usf.edu 
Campus: Tampa
Room: CIS 2062
Phone: 813-974-6758

Reza Ebrahimi is an assistant professor at the School of Information Systems and Management and the lead of at the 911±¬ÁĎÍř.

Ebrahimi’s current research is focused on statistical and adversarial machine learning for AI-enabled secure and trustworthy cyberspace. He leverages a wide range of statistical learning theories, including Transductive Learning, Transfer Learning, Adversarial Learning, and Deep Reinforcement Learning. Ebrahimi’s dissertation on AI-enabled cybersecurity analytics won the ACM SIGMIS best doctoral dissertation award in 2021. He has published 35 articles in peer reviewed journals, conferences, and workshops including MIS Quarterly, NeurIPS, JMIS, IEEE TPAMI, IEEE TDSC, Applied Artificial Intelligence, Digital Forensics, IEEE S&PW, AAAIW, IEEE ICDMW, and IEEE ISI. He has served as a program chair and program committee member in IEEE ICDM Workshop on Machine Learning for Cybersecurity since 2022 as well as the IEEE S&P Workshop on Deep Learning Security and Privacy. He has contributed to several projects supported by the National Science Foundation. He is an IEEE Senior Member and a member of the AIS, ACM, and AAAI.

Ebrahimi received his PhD in information systems from the University of Arizona, where he was a research assistant at the Artificial Intelligence Lab directed by Regents’ Professor Hsinchun Chen, and a master's degree in computer science from Concordia University in Montreal at the Center for Pattern Recognition and Machine Intelligence Lab directed by Ching Y. Suen.

Teaching

  • ISM 6251 - Machine Learning – Undergraduate and Master’s

  • ISM 7568 - Deep Learning for Business Analytics – PhD Seminar

  • ISM 6152 - Deep Learning – Master’s

Research

  • ­­­Ebrahimi R., Chai Y., Li. W., Pacheco J., Chen H. "RADAR: A Framework for Developing Adversarially Robust Cyber Defense AI Agents with Deep Reinforcement Learning", MIS Quarterly, Forthcoming.
  • Birrell J., Ebrahimi R., Behnia R., Pacheco J., "Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement," NeurIPS, Forthcoming.
  • Ebrahimi R., Pacheco J., Hu J., Chen H. “Learning Contextualized Action Representations in Sequential Decision Making for Adversarial Malware Optimization,” IEEE Transactions on Dependable and Secure Computing (TDSC), Forthcoming.

  • Ebrahimi M., Chai Y., Zhang H., Chen H., 2022, “Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.

  • Ebrahimi M., Chai Y., Samtani S., Chen H. 2022, “Cross-Lingual Security Analytics: Cyber Threat Detection in the International Dark 911±¬ÁĎÍřb with Adversarial Deep Representation Learning,” MIS Quarterly, 46(2), pp. 1209-1226.

view more

  • Zhang, N., Ebrahimi, M., Li, W., Chen, H., 2022, “Counteracting Dark 911±¬ÁĎÍřb Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence,” ACM Transactions on Management Information Systems, ACM, 13(2), pp. 1-21.

  • Ebrahimi M., Nunamaker J., Chen, H., 2020, “Semi-Supervised Cyber Threat Identification in Dark Net Markets: A Transductive and Deep Learning Approach,” Journal of Management Information Systems, 37(3), pp.694-722.

  • Ebrahimi M., Suen C.Y., Ormandjieva O., 2016, “Detecting Predatory Conversations in Social Media by Deep Convolutional Neural Networks,” Digital Investigation, Elsevier, Volume 18, pp. 33-49.

Service

  • Workshop Chair - IEEE ICDM Workshop on Machine Learning for Cybersecurity (MLC), 2022, 2023, 2024
  • Committee member - IEEE Security and Privacy (S&P) Workshop on Deep Learning and Security, 2022, 2023, 2024
  • Committee member - IEEE ICDM workshop on Deep Learning for Cyber Threat Intelligence (DL-CTI), 2020
  • Committee member - Informs Data Science Workshop, 2021