Chiara Lunerti
Papers
1
Total Citations
7
H-Index
1
About
Chiara Lunerti’s research lies at the intersection of computer vision, edge computing, and the Internet of Things (IoT), with a particular focus on the secure and efficient deployment of deep neural networks (DNNs) across heterogeneous platforms. Her most cited work, "Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms" (2021, 7 citations), addresses the critical challenge of optimizing face recognition (FR) systems for diverse devices such as robots, tablets, and smartphones. Lunerti’s contributions center on automating deployment strategies to balance performance, security, and resource constraints in real-world IoT environments. By tackling the complexities of DNN distribution across varied hardware, she advances the practical applicability of FR technology in dynamic, multi-device ecosystems. Her work is notable for bridging the gap between high-accuracy deep learning models and the operational limitations of edge devices, a key step toward scalable and secure AI-driven IoT solutions. With a growing citation impact, Lunerti’s research offers valuable insights for students and practitioners exploring the frontiers of embedded AI, computer vision, and decentralized intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1