Yangfan He

Papers

Total Citations

H-Index

About

Yangfan He is a rising researcher in the field of medical robotics, with a particular focus on magnetically actuated capsule endoscopy and precision control systems. His work addresses a critical bottleneck in diagnostic medicine: the accurate positioning of tethered capsule robots within the body. In his most-cited paper, "Learning Friction Model for Magnet-Actuated Tethered Capsule Robot" (2022), He tackles the complex challenge of friction between the capsule and its environment—a factor that often undermines control precision. By developing a learned friction model, he has paved the way for more reliable and autonomous navigation of these devices, enhancing their potential for targeted diagnosis and therapy. Though early in his career, with his top paper accumulating 3 citations, He’s contributions are foundational for a new generation of minimally invasive tools. His work stands out for its interdisciplinary approach, merging machine learning with mechanical design to solve real-world clinical problems. For students and researchers interested in the intersection of soft robotics, control theory, and medical devices, Yangfan He represents a promising voice in the quest for smarter, safer diagnostic technologies.