Papers
1
Total Citations
25
H-Index
1
About
Dr. Jun Qi is a leading researcher at the intersection of artificial intelligence, cybersecurity, and robotics, with a primary focus on the robustness and safety of deep reinforcement learning (DRL) systems. His most notable contribution is the development of "Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning" (2020, 25 citations), a seminal work that exposed critical vulnerabilities in DRL-based autonomous systems, including robot navigation and continuous control tasks. By demonstrating how strategically timed adversarial perturbations can degrade DRL performance, Dr. Qi has advanced the understanding of security risks in real-world AI applications. His research bridges theoretical adversarial machine learning with practical implications for robotics and autonomous systems, making him a key voice in the emerging field of AI safety. Dr. Qi’s work is particularly influential for students and researchers exploring the tension between powerful learning algorithms and their susceptibility to malicious inputs, offering both a cautionary tale and a foundation for developing more resilient intelligent systems.
Research Focus
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Top Papers
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