Yi-Fan Hao

Papers

Total Citations

H-Index

About

Yi-Fan Hao is a researcher at the forefront of embedded systems and robotics, specializing in hardware acceleration for real-time localization and mapping (SLAM) in mobile platforms. His most-cited work, "Hardware Acceleration for SLAM in Mobile Systems" (2023), addresses the critical challenge of achieving low-latency, energy-efficient perception on resource-constrained devices. By designing specialized hardware architectures, Hao’s research enables mobile robots and autonomous vehicles to perform simultaneous localization and mapping with significantly reduced computational overhead, paving the way for more responsive and autonomous systems. Though early in his career, his contributions have already garnered attention, with his flagship paper accumulating 3 citations—a promising start that underscores the relevance of his approach to the growing field of edge AI. Hao’s work bridges the gap between algorithmic efficiency and hardware design, offering practical solutions for next-generation mobile systems. His focus on hardware-software co-optimization positions him as a rising voice in the quest to make SLAM both faster and more accessible for real-world deployment.