Sungjoon Choi
Associate Professor
Seoul, South Korea
Summary
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Rockstar🎓
Top SchoolSungjoon Choi is an associate professor in the Department of Artificial Intelligence at Korea University with a decade of experience spanning academia and industry research. Previously a Staff Software Engineer at Google Research leading machine perception work, he holds a Ph.D. in Electrical Engineering from Stanford with a specialization in computer vision. His background blends production-grade ML engineering—from building TensorFlow tutorials and neural style transfer demos to fine-tuning VGG models—with deep research expertise. Based in Seoul, he brings practical systems experience from Microsoft and Google to his academic lab, mentoring the next generation of AI practitioners. A hands-on open-source contributor, his public TensorFlow examples reflect a commitment to making complex vision techniques accessible to engineers and students.
10 years of coding experience
12 years of employment as a software developer
Bachelor's Degree Computer Science and Engineering, Bachelor's Degree Computer Science and Engineering at Seoul National UniversityDoctor of Philosophy (Ph.D.) Electrical Engineering, Doctor of Philosophy (Ph.D.) Electrical Engineering at Stanford University
Github Skills (12)
mle10
mask-rcnn10
faster-rcnn10
convolutional-neural-networks10
tensorflow10
mlp10
ml10
data-processing9
transfer-learning9
neural-style-transfer9
computer-vision8
image-classification8
Programming languages (3)
Github contributions (5)
sjchoi86/Tensorflow-101
May 2016 - Nov 2016
TensorFlow Tutorials
Role in this project:
ML Engineer
ML EngineerContributions:64 commits, 2 PRs, 84 pushes in 6 months
Contributions summary:The user, sjchoi86, made several commits focused on implementing and demonstrating machine learning models within the TensorFlow framework. Their contributions include implementing a simple multi-layer perceptron (MLP) for MNIST, generating and processing image datasets for convolutional neural networks (CNNs), and constructing a deeper MLP model. The user's work also involved fine-tuning a VGG model for image classification, showcasing the application of transfer learning. Furthermore, they made contributions to a Deconvolutional Neural Network (DeconvNet) and created a neural style transfer model.
deep-learningrecurrent-neural-networkstensorflow-tutorialsconvolutional-neural-networkstensorflow
sjchoi86/advanced-tensorflow
Dec 2016 - Nov 2017
Contributions:90 commits, 87 pushes, 1 branch in 11 months
implementationstensorflow
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