Yuqian Zhou
Senior Research Scientist at Adobe
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolYuqian Zhou is a Senior Research Scientist with a decade of experience at the intersection of computer vision, deep learning, and computational imaging, currently co-leading multimodal research at Adobe in Seattle. She has driven production-first research for flagship features—such as Photoshop’s Remove Tool and Adobe Firefly Generative Fill—owning end-to-end R&D from data and architecture to deployment and safety. Her work spans low-level vision, image restoration, and generative models, and includes award-winning denoising and under-display camera research from internships at Face++ and Microsoft. A Ph.D. graduate of UIUC with an MPhil from HKUST, she blends rigorous academic foundations with a track record of shipping models used by millions, and she regularly recruits summer interns for hands-on research projects.
10 years of coding experience
7 years of employment as a software developer
College of Engineering Globex International Exchange Program Bioengineering and Biomedical Engineering, College of Engineering Globex International Exchange Program Bioengineering and Biomedical Engineering at Peking UniversityComputer Science, Computer Science at Sun Yat-sen UniversityPh.D Computer Vision, Ph.D Computer Vision at University of Illinois Urbana-ChampaignHong Kong University of Science and Technology (HKUST)
English, Chinese, chinese(cantonese)
Github Skills (23)
fourier-transform10
high-resolution10
image-synthesis10
generative-adversarial-network10
denoising10
segmentation10
inpainting9
image-restoration9
computer-vision8
diffusion-models8
pytorch7
deep-learning7
testing6
deep-neural-networks5
caffe2
Programming languages (3)
Github contributions (5)
yzhouas/reproducible-image-restoration-state-of-the-art
May 2019 - May 2019
Image Restoration Paper and Code, including Image Denoising, Image Supre-resolution, Image Inpainting etc.
Contributions:31 commits, 6 pushes in 8 days
denoisingimage-restorationinpainting
SHI-Labs/SGL-Retinal-Vessel-Segmentation
Mar 2021 - Feb 2022
[MICCAI 2021] Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels: New SOTA on both DRIVE and CHASE_DB1.
Contributions:30 commits, 27 pushes, 1 branch in 11 months
segmentation
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