Hanxiang Hao
Senior Research Scientist at Apple
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolHanxiang Hao is a Senior Research Scientist at Apple with a decade of experience specializing in image processing, computer vision, and biomedical image analysis. He holds a PhD in Electrical and Computer Engineering from Purdue after an MEng from Cornell, and has progressed from research internships to senior roles within Apple’s research organization. Hanxiang blends deep learning research with practical production experience, previously applying computer vision in industrial automation at SoftWear Automation and academic projects at Rice. Known for bridging rigorous academic methods with product-driven experimentation, he focuses on translating novel models into reliable systems for real-world imaging problems. Based in Seattle, he combines a strong theoretical background with hands-on engineering to tackle challenging vision tasks across consumer and biomedical domains.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical and Computer Engineering, Doctor of Philosophy - PhD, Electrical and Computer Engineering at Purdue UniversityMaster of Engineering (MEng), ECE, GPA 3.9, Master of Engineering (MEng), ECE, GPA 3.9 at Cornell UniversityBachelor's degree, Electrical Engineering and Automation, GPA 3.8, Bachelor's degree, Electrical Engineering and Automation, GPA 3.8 at Beijing Institute of Technology
Stackoverflow
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0reached
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Github Skills (31)
python10
machine-learning10
deep-learning10
tensorflow10
convolutional-neural-networks9
ml9
deep-neural-networks9
neural-network9
pruning9
cell9
convolution9
segmentation8
pointnet8
kernel8
algorithm8
Programming languages (4)
Github contributions (5)
Connor323/Convolution-with-sparse-kernel-in-TF
Oct 2017 - Jun 2019
Development a customized op in TensorFlow for convolution with sparse kernel
Contributions:30 commits, 3 PRs, 28 pushes in 1 year 8 months
convolutionkerneltensorflow
Connor323/Cancer-Cell-Tracking
Dec 2016 - Apr 2019
This project focuses on tracking cancer cell based on Fuhua et al. [1], which used watershed algorithm to segment cells and built a feature vector for cell tracking including the information of position, shape, spatial distribution and texture.
Contributions:21 commits, 3 PRs, 21 pushes in 2 years 3 months
tracking
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