Dohyeong Kim
Mentor Of Self Driving Car Nanodegree at Udacity
Buk District, Daegu, South Korea
Summary
🤩
Rockstar🎓
Top SchoolDohyeong Kim is a Deep Learning researcher and engineer with 11 years of experience focused on building human-like autonomous agents, from an AlphaStar-style StarCraft II agent to a fully autonomous real-world soccer robot. He mentors Udacity’s Self-Driving Car Nanodegree, reviewing student projects and translating research concepts into practical implementations. Dohyeong has hands-on ML engineering experience contributing tests and implementations to the ivy framework, improving JAX-fronted operations and activation-function coverage. Comfortable across simulation and physical robotics, he combines published-style research reproduction with production-minded tooling and teaching, making him skilled at moving complex deep learning agents from paper to working systems.
11 years of coding experience
Self-Driving Cars with Duckietown, Robot, SLAM, Image Processing, Deep Learning, 4/4, Self-Driving Cars with Duckietown, Robot, SLAM, Image Processing, Deep Learning, 4/4 at edXNanodegree, Deep Learning, Nanodegree, Deep Learning at Udacity
English
Github Skills (13)
testing10
ivy10
machine-learning10
jax10
python10
numpy9
neural-network9
deep-learning9
tensorflow8
converter7
pytorch7
transcode6
transpiler6
Programming languages (8)
Github contributions (5)
ivy-llc/ivy
Jun 2022 - Oct 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 53 commits, 33 PRs in 4 months
Contributions summary:Dohyeong primarily contributed to the testing and implementation of machine learning functionalities within the "ivy" repository. They focused on updating and verifying functions related to the JAX frontend, specifically for mathematical operations such as `sin`, `sign`, `sinh`, and `argmax`, and testing across diverse datasets. The user's work also included code reformatting and adjustments to existing test structures, including the addition of tests for newly implemented functionalities such as the "swish" activation function. These changes directly support the framework's machine learning code conversion capabilities.
pythontensorflowframework-learningtemplatedata-science
kimbring2/OpenAI_Requests-For-Research-2_Slitherin
Oct 2019 - Mar 2022
Contributions:231 commits, 225 pushes, 1 branch in 2 years 5 months
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