Mohamed Sadok Massaoudi, Ph.D.

Assistant Professor of AI & Machine Learning
Computer Science & Electrical Engineering
Tarleton State University
Email: mmassaoudi@tarleton.edu

Trustworthy AI, Cyber-Physical Security, and Resilient Power Systems

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About

I am an Assistant Professor of Artificial Intelligence and Machine Learning in the Department of Computer Science and Electrical Engineering at Tarleton State University. My research focuses on trustworthy artificial intelligence for cyber-physical systems, with an emphasis on smart-grid cybersecurity, deep learning, reinforcement learning, power-system stability, and resilient critical infrastructure.

My work integrates data-driven intelligence with domain-specific physical knowledge to improve cyberattack detection, risk assessment, and secure decision-making in complex engineering systems. I hold two Ph.D. degrees, in Electrical Engineering from Texas A&M University and Electronics–Artificial Intelligence from INSAT, Tunisia. My professional experience spans academic research, electrical engineering, and research laboratory management.

I welcome interdisciplinary collaborations and research opportunities involving artificial intelligence, cybersecurity, smart grids, and intelligent energy systems.

Impact at a glance

2,000+
Citations		 20
h-index		 Top 2%
Scientists worldwide

Citations and h-index as of October 2026, from Google Scholar. Included in the Stanford–Elsevier World’s Top 2% Scientists lists for 2025 and 2026 (official dataset). Also recipient of the IEEE Industry Applications Society Second Prize Paper Award (2024) and competitive research funding and academic fellowships (see Awards & Honors).

Latest research highlights

  • 2026, software paper: DataAiPrep, a machine learning data-quality assessment tool, published in SoftwareX (vol. 34, Art. 102662). Paper · GitHub
  • 2026, journal paper: “Topology-Aware Hierarchical Attack Graph Optimization for Cyber-Physical Power Systems,” Electronics. Paper
  • 2026, journal paper: FADEG, a graph neural network ensemble for intrusion detection in power systems, IEEE Open Journal of Industry Applications. Paper
  • 2026, funding: Lead PI of ReCIPeS-DC, a TEES seed-funded project on resilient cyber-physical infrastructure for data centers in Texas. Details

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