Ihsan Ullah

Korea University of Technology and Education, Daegu Gyeongbuk Institute of Science and Technology

Papers

Total Citations

H-Index

About

Ihsan Ullah is a researcher at the forefront of intelligent systems, with key contributions spanning federated reinforcement learning and medical robotics. His most cited work, "Federated Reinforcement Learning Acceleration Method for Precise Control of Multiple Devices" (2021, 25 citations), addresses the critical challenge of deploying reinforcement learning in real-world environments. By proposing a federated acceleration method, Ullah enables precise, scalable control of multiple devices—a breakthrough with implications for autonomous driving, robotics, and industrial automation. In the biomedical domain, his paper "Guidewire Tip Tracking using U-Net with Shape and Motion Constraints" (2019, 5 citations) advances micro-robot catheter control for minimally invasive cardiac surgery. By integrating shape and motion constraints into a U-Net architecture, he achieved accurate, decisive guidewire tip tracking, directly improving surgical precision and patient outcomes. Ullah’s work bridges the gap between simulated and real-world environments, demonstrating high impact through practical solutions for complex control tasks. His research not only pushes the boundaries of RL acceleration but also enhances medical robotics, making him a notable contributor to both artificial intelligence and healthcare technology.