Jiuxiang Gu
Research Scientist at Adobe
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolJiuxiang Gu is a research scientist with 11 years of experience applying AI to help computers understand the visual world, currently based in Redmond and conducting research at Adobe since 2020. His trajectory spans research roles at leading institutions including Nanyang Technological University and the Chinese Academy of Sciences, and a PhD in Artificial Intelligence from NTU complemented by a master's in information technology. Jiuxiang blends deep academic rigor with industry impact, moving from early research assistant roles into applied research that informs product-scale solutions. Known for bridging theory and practice, he pursues perceptual and representation learning problems that accelerate real-world image and vision capabilities. An interviewer might note his steady focus on computer vision research across continents, suggesting both adaptability and sustained domain expertise.
11 years of coding experience
5 years of employment as a software developer
Master's Degree, Information Technology, Master's Degree, Information Technology at University of Chinese Academy of SciencesDoctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Nanyang Technological UniversityBachelor's degree, Electrical and Electronics Engineering, Bachelor's degree, Electrical and Electronics Engineering at Jiangsu University
Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (32)
image-processing7
torchvision7
computer-vision7
pytorch6
datasets5
face4
machine-learning4
lstm4
deep-learning4
gpu4
open-datasets4
question-answering3
dialog3
artificial-intelligence3
stock3
Programming languages (3)
Github contributions (5)
anguszxd/video-segmentation
Jul 2015 - Nov 2015
Contributions:29 commits in 3 months
gujiuxiang/MIL.pytorch
Jun 2017 - Aug 2018
PyTorch implementation of Multiple-instance learning
Contributions:13 commits, 11 pushes, 1 branch in 1 year 1 month
pytorchcomputer-visiontorchvisionpytorch-implementationmultiple-instance-learning
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