Prof. Qingling Zhao is a leading researcher in embedded systems, real-time systems, mixed-criticality scheduling, and intelligent computing, with significant contributions spanning system architecture, cyber–physical systems, and AI-driven embedded intelligence. With an h-index of 12, 26 scholarly documents, and 456 citations across 345 citing publications, the research output demonstrates sustained academic impact. The work covers core areas such as mixed-criticality scheduling theory, resource synchronization, stack memory optimization, AUTOSAR model optimization, and schedulability analysis, alongside recent advances in deep learning, reinforcement learning optimization, network-on-chip systems, intrusion detection, and remote sensing object detection. Publications appear in high-impact venues including ACM Transactions on Embedded Computing Systems, IEEE Transactions, Journal of Systems Architecture, IEEE Access, and major international conferences. Recent research extends classical real-time system theory toward AI-enabled embedded and cyber-secure systems, reflecting a strong integration of theoretical rigor and practical applicability across safety-critical and intelligent computing platforms.