Jonathan Chung is an applied scientist with 12 years of experience applying deep learning and research-grade ML to production problems across Amazon, AWS, and Goodnotes. He holds a PhD in Electrical and Computer Engineering from the University of Toronto and has repeatedly moved research into product—building end-to-end handwriting OCR and segmentation pipelines during an Amazon internship that later informed larger applied work. His roles span cloud-scale ML at AWS and consumer-facing features at Goodnotes, demonstrating comfort with both backend model infrastructure and user-centered applications. Based in Seattle, he blends academic rigor with product delivery, making him adept at turning novel algorithms into robust, deployable systems. An active researcher and engineer, he also experiments publicly (GitHub: jonomon) and documents practical ML insights in technical posts.
12 years of coding experience
5 years of employment as a software developer
Bachelor of Applied Science (B.A.Sc.) Electrical Engineering, Bachelor of Applied Science (B.A.Sc.) Electrical Engineering at The University of British Columbia
Doctor of Philosophy (Ph.D.) Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.) Electrical and Computer Engineering at University of Toronto
This repository lets you train neural networks models for performing end-to-end full-page handwriting recognition using the Apache MXNet deep learning frameworks on the IAM Dataset.
Contributions:1 review, 4 commits, 3 PRs in 1 year
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