Guochen Ning
To the pioneers who lead, the companions who walk beside, and the successors yet to stride.

Dr. Ning (宁国琛) is currently an Assistant Professor and a Principal Investigator (PI) at Tsinghua University.
His research interests encompass structural design and autonomous control of medical robots, with a particular emphasis on intelligence. He also concentrates on research areas involving robot learning and the intersection of robotics and artificial intelligence (AI). The systems he is actively engaged with include ultrasound robots, respiratory intervention robots, and ophthalmic robots. Additionally, he is actively involved in the fields of reinforcement learning (RL), imitation learning (IL), and robotic perception-decision methodologies, with the ultimate goal of achieving autonomous medical robots capable of performing medical tasks in the future.
News
Aug 23, 2024 | I am supported by National Natural Science Foundation of China (面上) in 2024! |
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Apr 26, 2024 | We publised a research paper on Medical Image Analysis (MIA) ! https://www.sciencedirect.com/science/article/pii/S2589750023002017 |
Nov 23, 2023 | We publised a review paper on The Lancet Digital Health! https://www.sciencedirect.com/science/article/pii/S2589750023002017 |
Sep 28, 2023 | I am supported by 青年人才托举项目 of China Association For Science And Technology (CAST) in 2023. |
Selected Publications
2024
- IEEE TMRBCable-Driven Light-weighting and Portable System for Robotic Medical Ultrasound ImagingIEEE Transactions on Medical Robotics and Bionics 2024
- MIAOne-shot neuroanatomy segmentation through online data augmentation and confidence aware pseudo labelMedical Image Analysis 2024
2023
- MIAAnatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registrationMedical Image Analysis 2023
- IEEE TBMEAutonomous Robotic Ultrasound Vascular Imaging System With Decoupled Control Strategy for External-Vision-Free EnvironmentsIEEE Transactions on Biomedical Engineering 2023
- IEEE TIEInverse-reinforcement-learning-based robotic ultrasound active compliance control in uncertain environmentsIEEE Transactions on Industrial Electronics 2023