Fangqi Zhu

Papers

Total Citations

236

H-Index

About

Fangqi Zhu is a leading researcher at the intersection of artificial intelligence and multi-modal sensing, whose work is redefining how machines perceive and interpret complex environments. His seminal 2023 paper, "A comparative review on multi-modal sensors fusion based on deep learning," has already garnered over 236 citations, establishing itself as a cornerstone reference in the field. Zhu’s primary research areas encompass deep learning architectures for sensor fusion, integrating data from vision, LiDAR, radar, and other modalities to enhance robustness in autonomous systems and robotics. His major contribution lies in systematically analyzing and benchmarking fusion strategies—early, late, and hybrid—offering a clear taxonomy that guides both novice and expert practitioners. Beyond this review, Zhu’s work is noted for its practical impact, bridging theoretical advances with real-world deployment challenges. His achievements include shaping curriculum in AI engineering and collaborating on projects that improve safety in autonomous driving. For students and researchers, Zhu’s research provides an essential roadmap for navigating the complexities of multi-modal perception, making him a pivotal figure in the ongoing evolution of intelligent systems.