Song Fu

Song Fu is a Professor and Associate Chair for Research in the Department of Computer Science and Engineering at the University of North Texas. His research focuses on interdisciplinary applications of artificial intelligence in aerospace engineering, fault diagnosis systems, radiation belt dynamics, and autonomous vehicle technologies. He leads initiatives in predictive maintenance for mechanical systems, data privacy in vehicular edge computing, and cooperative perception frameworks for connected autonomous vehicles.

Dr. Fu’s work bridges machine learning with domain-specific challenges, such as optimizing shield machine designs via deep reinforcement learning and developing fault diagnosis frameworks for aeroengines using transformer networks and large language models. His contributions extend to space physics, analyzing radiation belt electron dynamics and Martian magnetosphere interactions with solar wind. He has pioneered privacy-preserving techniques for vehicle-edge networks and contributed to additive manufacturing through generative modeling of microstructure simulations.

Key research themes include:

• AI-driven predictive maintenance and system reliability
• Cooperative autonomous systems and edge computing
• Radiation belt modeling and space weather prediction
• Privacy-preserving machine learning architectures

His publications emphasize real-world applications, such as improving aircraft brake monitoring, enhancing flash storage reliability in automotive systems, and optimizing laser-based additive manufacturing processes. Current projects explore multi-agent collaboration in fleet maintenance strategies and next-generation object detection frameworks for autonomous vehicles.