Wenjie Ruan

Papers

Total Citations

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About

Wenjie Ruan is a leading researcher at the intersection of artificial intelligence, cybersecurity, and autonomous systems. His work focuses on the reliability and security of deep learning models, particularly in real-world, safety-critical applications. Ruan’s major contributions include pioneering adversarial attacks on object detection systems used by intelligent robots, demonstrating that these models can be compromised in real time—a critical vulnerability for autonomous vehicles and drones. His 2023 paper, "Adversarial Detection: Attacking Object Detection in Real Time," has garnered significant attention, accumulating 5 citations and highlighting the urgent need for robust defenses. Beyond security, Ruan has advanced foundational methods in reliability assessment and deep reinforcement learning, with early works from 2012 that continue to influence the field. His research not only exposes critical flaws in current AI systems but also drives the development of more trustworthy and resilient autonomous technologies. With a growing citation impact and a focus on practical, high-stakes applications, Wenjie Ruan is shaping the future of safe and secure artificial intelligence.