Jieneng Chen
Postdoctoral Researcher at Stanford University
Baltimore, Maryland, United States
Summary
🤩
Rockstar🎓
Top SchoolJieneng Chen is a postdoctoral researcher and PhD graduate from Johns Hopkins University with eight years of experience at the intersection of computer vision, medical imaging, and large language models. He is best known for developing TransUNet, a widely adopted Transformer-based architecture for medical image segmentation that has inspired over 10,000 subsequent works and whose official code he helped refine and maintain. A Siebel Scholar and recipient of multiple academic awards and grants, Jieneng combines rigorous research with active open-source engagement and practical model engineering. Currently based at Stanford and Baltimore, he has collaborated with leading vision researchers and taught graduate courses on machine imagination, reflecting both deep technical expertise and a commitment to mentoring the next generation.
8 years of coding experience
5 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Tongji UniversityJohns Hopkins UniversityBachelor’s exchange student in Electrical Engineering (Automatic Control Engineering), Bachelor’s exchange student in Electrical Engineering (Automatic Control Engineering) at Technical University of Munich
English, Chinese
Github Skills (12)
vi10
transformer-models10
computer-vision10
pytorch10
custom-configuration10
configurations10
deep-learning10
yml-configuration10
system-configuration10
python10
medical-image-segmentation10
resnet9
Programming languages (2)
Github contributions (5)
Beckschen/TransUNet
Feb 2021 - Dec 2022
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
Role in this project:
ML Engineer
ML EngineerContributions:18 commits, 1 PR, 19 pushes in 1 year 10 months
Contributions summary:Jieneng contributed to the core modeling and configuration files of the TransUNet project, which is focused on medical image segmentation using Transformers. Their commits involved updating the model definition, including changes to the ResNet and ViT architecture configurations and the underlying code for the model. They also removed debug mode and updated the documentation, suggesting a focus on refinement and maintenance of the model.
medical-image-segmentation
Beckschen/beckschen.github.io
Apr 2018 - Jul 2026
Contributions:317 pushes, 1 branch in 8 years 4 months
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