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
Doctor of Philosophy (Ph.D.) Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.) Electrical and Computer Engineering at Seoul National University
Korean, English
Github Skills (13)
mle10
mask-rcnn10
faster-rcnn10
machine-learning10
convolutional-neural-networks10
tensorflow10
mlp10
ml10
data-processing9
transfer-learning9
neural-style-transfer9
computer-vision8
image-classification8
Programming languages (4)
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.
tensorflowtensorflow-tutorialsconvolutional-neural-networksrecurrent-neural-networks
sjchoi86/2022-1-intelligent-robotics
Mar 2022 - May 2022
Contributions:13 commits, 15 pushes in 2 months
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