Sanglu Lu

Sanglu Lu is a Professor in the Department of Computer Science at Nanjing University's School of Computer Science and Engineering. With over 400 publications spanning from 1998 to 2025, Professor Lu has established a significant research presence in mobile computing, edge computing, and wireless sensing technologies. Their work frequently appears in top-tier conferences including INFOCOM, IEEE Transactions journals, and AAAI.

Professor Lu's research focuses on cutting-edge areas in computer science, particularly in edge AI systems, millimeter-wave and RFID-based sensing, graph neural networks, and time series analysis. Their work bridges theoretical advances with practical applications, often addressing resource-constrained environments and real-world deployment challenges. Recent publications show a strong emphasis on privacy-preserving sensing techniques, efficient model deployment at the edge, and multimodal approaches to human activity understanding.

Analysis of publication trends reveals Professor Lu's work has evolved from traditional mobile networking topics toward more AI-centric research, particularly in the last five years. The research demonstrates strong interdisciplinary connections between networking, sensing, and machine learning, with increasing focus on practical applications in healthcare, accessibility technology, and industrial IoT systems. Publications consistently show collaboration with researchers across China and internationally, indicating an active research group and extensive professional network.

Professor Lu has mentored numerous researchers as evidenced by the extensive publication record with various co-authors, though specific student names aren't detailed in the provided information. Their work has received significant attention in the research community, as reflected by the substantial number of publications in high-impact venues.

Current research directions include innovative approaches to edge AI, advanced wireless sensing techniques using commercial hardware, and novel neural network architectures for time series and graph data. Professor Lu's laboratory appears to be actively working on real-world applications of these technologies, particularly in health monitoring, accessibility solutions, and industrial automation contexts.