Wonjun Ko
Assistant Professor at 성신여자대학교
Seoul, South Korea
Summary
👤
Senior🎓
Top SchoolWonjun Ko is an assistant professor and data scientist with eight years of experience bridging physics, brain and cognitive engineering, and machine learning. He earned a Ph.D. in Machine Learning from Korea University after a B.S. in Physics from Sogang University, and now leads AI convergence teaching and research at Sungshin Women’s University while working in industry at SK Hynix. His research and engineering work focuses on representation learning, data mining, and deep learning for brain–computer interfaces, with notable open-source contributions to Deep-BCI that implement RCNN, Inception-RCNN and adversarial frameworks for intention recognition. Comfortable moving between academic rigor and production ML, he brings both theoretical depth and hands-on model development experience. Colleagues appreciate his ability to translate neuroscience problems into deployable deep learning architectures.
8 years of coding experience
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at 서강대학교Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at 고려대학교강원과학고등학교
Korean, English
Github Skills (9)
openbci10
mask-rcnn10
faster-rcnn10
machine-learning10
deep-q-learning10
deep-learning10
tensorflow10
python9
open-source8
Programming languages (2)
Github contributions (5)
DeepBCI/Deep-BCI
Nov 2017 - Dec 2019
An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning
Role in this project:
ML Engineer
ML EngineerContributions:17 commits, 14 pushes, 1 branch in 2 years 1 month
Contributions summary:Wonjun primarily contributes to the development of deep learning models for BCI applications, as evidenced by the code changes related to model definitions and network architectures. The commits involve modifications to model structures like RCNN and Inception-RCNN, and the implementation of a deep adversarial learning framework, demonstrating a focus on developing and refining the core machine-learning components of the project. These contributions are crucial for enabling the project's goal of intention recognition using deep learning techniques.
brainssveperpdeep-learningbci
wko1014/RSTNN
Nov 2020 - May 2022
Contributions:28 commits, 26 pushes, 1 branch in 1 year 5 months
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