Hao Yu

Hao Yu

Postdoctoral Research Associate in Medical AI, 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 Posts

Blog with Jupyter Notebooks!
Easily blog from Jupyter notebooks!
Nov 4, 2023 1 min read
Blog with Jupyter Notebooks!
Welcome to Hugo Blox Builder, the website builder for Hugo
Welcome 👋 We know that first impressions are important, so we’ve populated your new site with some initial content to help you get familiar with everything in no time.
Hao Yu
Dec 13, 2020 3 min read
Welcome to Hugo Blox Builder, the website builder for Hugo
Writing technical content in Markdown
Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest. Highlight your code snippets, take notes on math classes, and draw diagrams from textual representation.
Jul 12, 2019 6 min read
Writing technical content in Markdown

Publications

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Hao Yu (2019). Chen, M., Dong, W.*, Yu, H.*, Woodhouse, I., Ryan, C.M., Liu, H., Georgious, S. and Mitchard E.T., Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data. arXiv preprint arXiv:2311.11777, 2023..

PDF Code Dataset Poster Slides Video Source Document Custom Link

Hao Yu (2019). Chen, M., Dong, W.*, Yu, H.*, Woodhouse, I., Ryan, C.M., Liu, H., Georgious, S. and Mitchard E.T., Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data. arXiv preprint arXiv:2311.11777, 2023999..

PDF Code Dataset Poster Slides Video Source Document Custom Link

Hao Yu (2019). Chen, M., Dong, W.*, Yu, H.*, Woodhouse, I., Ryan, C.M., Liu, H., Georgious, S. and Mitchard E.T., Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data. arXiv preprint arXiv:2311.11777, 2023999..

PDF Code Dataset Poster Slides Video Source Document Custom Link

Hao Yu (2019). Chen, M., Dong, W.*, Yu, H.*, Woodhouse, I., Ryan, C.M., Liu, H., Georgious, S. and Mitchard E.T., Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data. arXiv preprint arXiv:2311.11777, 2023999..

PDF Code Dataset Poster Slides Video Source Document Custom Link

Contact

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