Prof. Jianjun Yuan is an established researcher whose work spans intelligent systems, deep learning, and secure networked applications, with strong contributions to intrusion detection, image understanding, and robust optimization models. His research emphasizes lightweight and resilient learning architectures for complex real-world environments, including in-vehicle networks, medical imaging, and high-resolution remote sensing. He has authored 39 scholarly documents, receiving 215 citations across 205 citing publications, and holds an h-index of 9, reflecting sustained research impact and influence. Recent publications include RL-IDS, a robust reinforcement learning–based intrusion detection system for in-vehicle networks (Journal of Information Security and Applications, 2026), IMFF, a dual-space optimization framework for remote sensing scene classification (Expert Systems with Applications, 2026), and MLFD, a multi-level feature disentanglement network for medical image recognition (Expert Systems with Applications, 2025). His work advances robust, interpretable, and application-driven artificial intelligence research.