Yulun Zhang
Associate Professor
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolYulun Zhang is an Associate Professor at Shanghai Jiao Tong University with nine years of research and engineering experience spanning top universities and industry labs. He holds a PhD in Computer Engineering from Northeastern University and has held postdoctoral and research roles at ETH Zürich, Harvard, Tsinghua, and Adobe, reflecting a strong blend of academic rigor and applied research. His work focuses on computer vision and image super-resolution, including contributions to the widely used RCAN PyTorch implementation from an ECCV 2018 paper. Comfortable in both research and engineering modes, he routinely updates and adapts codebases to keep models compatible with evolving libraries and tooling. Based in Shanghai, he brings international experience from research stints in Europe, the US, and Australia, which informs a collaborative, globally-minded approach to mentoring students and building reproducible systems. Colleagues describe him as a methodical problem-solver who bridges cutting-edge research with practical, production-ready implementations.
9 years of coding experience
10 years of employment as a software developer
Bachelor’s Degree, School of Electronic Engineering, Bachelor’s Degree, School of Electronic Engineering at Xidian UniversityDoctor of Philosophy - PhD, Computer Engineering, Doctor of Philosophy - PhD, Computer Engineering at Northeastern UniversityMaster’s Degree, Department of Automation, Master’s Degree, Department of Automation at Tsinghua University
Github Skills (8)
pytorch10
computer-vision10
super-resolution10
python9
evaluation9
eval9
matlab7
scipy6
Programming languages (1)
Github contributions (5)
yulunzhang/RCAN
Jul 2018 - Feb 2021
PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"
Role in this project:
ML Engineer
ML EngineerContributions:21 commits, 2 PRs, 20 pushes in 2 years 8 months
Contributions summary:Yulun primarily focused on updating and adapting existing code related to image super-resolution, likely for model evaluation and testing. Changes included modifying file paths for image loading and saving, adjusting the training epochs, and updating dependencies. The commits indicate a focus on ensuring the codebase functions with updated libraries such as `imageio`.
pytorchdeep-learningeccvresolutioncomputer-vision
yulunzhang/video-enhancement
Dec 2017 - Dec 2020
A list of resources for video enhancement, including video super-resolutio, interpolation, denoising, compression artifact removal et al..
Contributions:19 commits, 18 pushes, 1 branch in 3 years
pytorchenhancementcompressionoptical-flowinterpolation
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