Hamidreza Jafarnejadsani
Papers
2
Total Citations
7
H-Index
2
About
Hamidreza Jafarnejadsani is a rising researcher at the intersection of control theory, cyber-physical security, and autonomous systems. His primary research focuses on developing robust adaptive control frameworks for safety-critical systems, with a particular emphasis on securing autonomous vehicles against adversarial threats. His doctoral dissertation (2018) made a foundational contribution by extending L1 adaptive control theory to a sampled-data framework, enabling robust control of complex multi-input multi-output (MIMO) systems—including underactuated and non-minimum phase systems—with asynchronous sampling rates. This work has been cited 5 times and laid the groundwork for his subsequent investigations into cyber-physical security. More recently, Jafarnejadsani has pioneered the use of reinforcement learning to generate real-time adversarial image perturbations targeting deep neural networks in autonomous vehicles. His 2025 paper demonstrates how subtle, dynamically crafted perturbations can fool object detection and classification systems in driverless cars and UAVs, highlighting critical vulnerabilities in DNN-based perception. With 2 citations already, this work is gaining traction as autonomous vehicle safety becomes a pressing concern. Jafarnejadsani’s research is notable for bridging theoretical control design with practical security challenges, making him a key voice in the future of resilient autonomous systems.
Research Focus
Key Achievements
Top Papers
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