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Peyman Najafirad
AI Security
Knowledge Representation
Probabilistic Decision Making
Reinforcement Learning
About
Peyman Najafirad (Paul Rad), Ph.D. is an Associate Professor of Computer Science and Associate Dean for Research and Partnerships in the College of AI, Cyber and Computing at The University of Texas at San Antonio. He earned his Ph.D. in Electrical and Computer Engineering and also holds M.S. degrees in Computer Science (UTSA) and Computer Engineering & Artificial Intelligence, as well as a B.S. in Computer Science from Sharif University of Technology. His research interests include AI security, knowledge representation, probabilistic decision making, and reinforcement learning.
Research Performance Summary
1996-2026
Active Research Span
First Recorded Paper
Safety Improvement in Robots for Industrial Automation
Year:
1996
Citations:
0
Venue:
International Conference on Application of Control and Robotics
Latest Recorded Paper
A Survey of Roadside Unit Deployment for Intelligent Transportation in the Context of Smart Cities
Year:
2026
Citations:
0
Venue:
From Large-Scale Systems to System of Systems Engineering: Dedicated to
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Top Coauthors
Rad
Najafirad
Jamshidi
Research Impact by Period
Papers
11
Citations
87
Avg. Citations / Paper
7.9
H-Index
2
Papers
82
Citations
4187
Avg. Citations / Paper
51.1
H-Index
26
Papers
66
Citations
2757
Avg. Citations / Paper
41.8
H-Index
31
Papers
2
Citations
83
Avg. Citations / Paper
41.5
H-Index
2
Papers
14
Citations
622
Avg. Citations / Paper
44.4
H-Index
10
Papers
2
Citations
0
Avg. Citations / Paper
0
H-Index
0
Contact and Professional Links
Detected Research Keywords
Wasserstein Generative Adversarial
Generative Adversarial Network
Opportunities Challenges Explainable
Challenges Explainable Artificial
Explainable Artificial Intelligence
Artificial Intelligence Xai
Intelligence Xai Survey
Opportunities Challenges Learning
Challenges Learning Adversarial
Learning Adversarial Robustness