Hao Yu

Postdoctoral Research Associate in Medical Imaging, Institute for Imaging, Data and Communications

University of Edinburgh

Biography

Hao Yu is a postdoctoral research associate at the University of Edinburgh. He received his Ph.D. in Engineering from the University of Edinburgh in 2025. During his Ph.D., he worked extensively on electrical impedance tomography (EIT) combined with deep learning for lung imaging and two-phase flow volume fraction measurement. Currently, he is exploring advanced artificial intelligence techniques for medical imaging applications and fault diagnosis.

Interests

Artificial Intelligence
Medical Imaging
Fault Diagnosis
Rmote sensing

Education

PhD, Institute for Imaging, Data and Communications, 2020-2025
University of Edinburgh, Edinburgh, UK
MSc, Electrical Engineering and Automation, 2018–2020
Harbin Institute of Technology, Harbin, China
BEng, Exchange student, Armour College of Engineering, 2018
Illinois Institute of Technology, Chicago, USA
BEng., Electrical Engineering and Automation, 2014–2018
North China Electric Power University, Baoding, China

Recent Publications

Hao Yu, Shaobin Li, Yongxiang Xu, Zihao Zhang, Haoyi Mu, and Wei Han.

May, 2025 IEEE Transactions on Industrial Electronics

Time-Frequency Domain Lightweight Dual-Branch MSCFormer for PMSM ITSC Fault Diagnosis

Hao Yu, Hao Yu, Zhongxu Dong, Wei Han, Yang Wu, Chunpen Wang, Zhe Liu and Jiabin Jia.

May, 2025 Measurement

Feasibility Analysis of EIT-Guided Lung Tumor Tracking with Prior Information for Robotic Arm-Assisted Radiotherapy

Ning Li, Hao Yu*, Stephen Finney, and Paul D. Judge.

February, 2025 IEEE Transactions on Industrial Electronics

Long-Horizon FCS-MPC Trained 1-D Convolution Neural Networks for FPGA-Based Power-Electronic Converter Control With a Si/SiC Hybrid Converter Case Study

This paper proposes an FPGA-based compressed 1-D convolutional neural network for long-horizon FCS-MPC, significantly reducing online computation and hardware resource usage while maintaining control performance.

Hao Yu, Haoyu Liu, Zhe Liu, Zeyu Wang, and Jiabin Jia.

January, 2024 Computer methods and programs in biomedicine

High-resolution conductivity reconstruction by electrical impedance tomography using structure-aware hybrid-fusion learning

This study proposes a novel deep learning method, SA-HFL, that fuses multi-branch features to improve the accuracy and robustness of EIT conductivity reconstruction against noise and modeling errors.

Publications

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Yueyue Xiao, Songpei Hu, Chunxiao Chen, Hao Yu, Liang Wang, Jie Yu, Bokai Chen, Ming Lu, Jagath C Rajapakse (2025). A novel real-time efficacy assessment method for tumor treating fields. Engineering Analysis with Boundary Elements.

DOI

Yueyue Xiao, Jie Yu, Chunxiao Chen, Liang Wang, Songpei Hu, Bokai Chen, Hao Yu (2025). A Novel Generative Adversarial Network Based on Conditional VAE for EIT Reconstruction of Lung Diseases. International Journal of Imaging Systems and Technology.

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Wenquan Dong, Songyan Zhu, Jian Xu, Casey M Ryan, Man Chen, Jingya Zeng, Hao Yu, Congfeng Cao, Jiancheng Shi (2025). UFLUX v2.0 A Process-Informed Machine Learning Framework for Efficient and Explainable Modelling of Terrestrial Carbon Uptake. IEEE Geoscience and Remote Sensing Letters.

DOI

Wei Han, Yinghao Li, Hao Yu, Jiabin Jia (2023). V-Pen An Acoustic-Based Fine-Grained Virtual Pen Input System Using Hand Tracking. IEEE Transactions on Instrumentation and Measurement.

DOI

Hao Yu, Xingchen Wan, and Jiabin Jia. (2023). Extracting reference voltages from measurement voltages for oil-water two-phase flow measurement of electrical impedance tomography. Flow Measurement and Instrumentation.

DOI

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Contact

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