Justin Ho
Automotive SW Engineer at Qualcomm
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolJustin Ho is an Automotive Software Engineer and University of Toronto Computer Engineering student with eight years of hands-on experience spanning embedded systems, machine learning, and hardware development. He has applied state estimation and EKF techniques on a Formula Racing driverless team, interned on drone flight control and ML projects at ITRI, and now works in automotive software at Qualcomm. Justin combines an entrepreneurial streak—cofounding a campus social startup piloted at U of T—with deep technical curiosity, routinely self-teaching new stacks and research topics. Comfortable bridging software, networks, and hardware, he focuses on pragmatic, safety-minded systems for autonomous platforms. A less obvious strength is his background in both research environments and commercial teams, giving him a knack for turning prototype algorithms into deployable engineering.
8 years of coding experience
1 year of employment as a software developer
International Baccalaureate, International Baccalaureate at Lower Canada CollegeBachelor of Applied Science - BASc, Computer Engineering, Bachelor of Applied Science - BASc, Computer Engineering at University of Toronto
Chinese, English, French
Github Skills (9)
web-platform4
heroku4
react4
django4
python4
machine-learning3
computer-vision3
tensorflow3
neural-network2
Programming languages (1)
Github contributions (5)
arunraja-hub/Metatstatic_AI
Sep 2019 - Dec 2019
Metastatic.AI is a Computer Vision Detection Web Platform that analyzes an input image of a metastatic tissue and output whether tissue is malignant or not. Built with Django and React.js, and it is hosted on Heroku. Machine Learning Model is developed with TensorFlow using Convolutional Neural Network and achieved 93% test accuracy.
Contributions:135 commits in 2 months
herokupythondjangoreact-jstensorflow
j1ai/Metastatic-AI
Jan 2020 - Jan 2020
Metastatic-AI is a Computer Vision Detection Web Platform that analyzes an input image of a metastatic tissue and output whether tissue is malignant or not. Built with Django and React.js, and it is hosted on Heroku. Machine Learning Model is developed with TensorFlow using Convolutional Neural Network and achieved 93% test accuracy.
Contributions:8 pushes, 6 branches in 6 days
herokupythondjangoreact-jstensorflow
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