Yipeng Hu
Associate Professor
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolYipeng Hu is an Associate Professor at UCL with eight years of postdoctoral experience focused on medical image computing, machine learning, and image-guided interventions across urology, obstetrics, and gastroenterology. He progressed through research and teaching ranks at UCL since 2008 and has held visiting roles at the University of Oxford, bridging academic collaboration between institutions. His PhD in Medical Image Computing and background in biomedical engineering underpin a track record of developing practical ML tools for clinical imaging and surgical guidance. As an open-source contributor he has applied TensorFlow to medical image registration in projects such as NiftyNet, showing hands-on expertise in algorithm implementation and data handling for image-guided therapy. Colleagues value him for combining rigorous research with translational focus—moving models from prototypes toward clinical workflows. He is based in London and brings a cross-disciplinary perspective shaped by clinical domains and long-term academic mentorship.
8 years of coding experience
11 years of employment as a software developer
Bachelor’s Degree, Biomedical Engineering, Bachelor’s Degree, Biomedical Engineering at Sichuan UniversityHangzhou No.2 High SchoolUniversity College London
English, Chinese
Github Skills (6)
tensorflow10
medical-image-analysis10
python10
image-registration9
machine-learning9
hdf9
Programming languages (5)
Github contributions (5)
NifTK/NiftyNet
Sep 2017 - Feb 2018
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 5 comments in 4 months
Contributions summary:Yipeng contributed to the development of a medical image registration code, as evidenced by the initial file creation and code changes within the `niftynet/contrib/label_registration` directory. They introduced a Python script (`label_reg.py`) that incorporated TensorFlow for image processing and registration tasks. The code demonstrates the use of TensorFlow, data handling with h5py, and various parameters related to the registration algorithm, including learning rates, regularization, and data set configurations.
image-analysistherapymedical-imagedeep-learningconvolutional
YipengHu/COMP0090
Aug 2021 - May 2022
UCL Module: Introduction to Deep Learning
Contributions:1 review, 74 commits, 2 PRs in 9 months
deep-learningmachine-learningucl
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