معرفی
Jaime Camelio is a Professor at the University of Georgia's School of Electrical & Computer Engineering, specializing in cyber-physical systems security, smart manufacturing, and statistical process control. His work integrates advanced technologies like machine learning, reinforcement learning, and Bayesian inference to enhance manufacturing resilience and safety. He focuses on vulnerabilities in production systems, digital twin applications, and data-driven quality control. His research spans aerospace composites, IIoT-enabled worker well-being monitoring, and synthetic data frameworks for manufacturing simulations.
Research interests include cybersecurity in additive manufacturing, occupational safety monitoring via statistical control charts, and innovative approaches to fault detection in assembly systems. He leads projects funded by NSF CPS initiatives, such as collaborative research on manufacturing security and cyber-physical vulnerability assessments. His work bridges theoretical advancements with real-world applications in aerospace, automotive, and industrial IoT sectors.
Scientific contributions include frameworks for digital thread integration in product lifecycle management and NURBS-based statistical monitoring of manufacturing surfaces. Publications emphasize both technical innovations and systemic risk mitigation strategies for modern manufacturing ecosystems. His lab explores cutting-edge topics like LLM applications in manufacturing workflows and random sampling strategies to counteract cyber-physical attacks.
