Wonjun Ko — CV ⬇ Download PDF Open PDF ↗

Wonjun Ko, Ph.D.

Assistant Professor · School of AI Convergence, Sungshin Women's University
Seoul, Republic of Korea  ·  wjko@sungshin.ac.kr  ·  +82-2-920-7191  ·  Website  ·  LinkedIn  ·  ORCID  ·  Google Scholar

Work Experience

Sungshin Women's University2023-09 – Present
Assistant Professor, School of AI ConvergenceSeoul, Republic of Korea
SK hynix Inc.2022-08 – 2023-08
Data Scientist (Senior Researcher)Icheon, Republic of Korea
  • R&D/M&T Data Analytics, Division of Data Intelligence, DT

Education

Korea University2017-09 – 2022-08
PhD · Brain and Cognitive EngineeringSeoul, Republic of Korea
  • Dissertation: Deep Representation Learning in Biomedicine
  • Advisor: Prof. Heung-Il Suk

Research Interests

Large Language Models for Data-Oriented Representation
  • Retrieval-augmented generation-inspired feature representation learning
  • Service-specialized generative language models for biomedical applications
Deep Learning Algorithms and Neural Network Structures for Biomedical Data Analysis
  • Optimization algorithms based on reinforcement learning (J7, J8), semi-supervised (C11), unsupervised (J6), and self-supervised learning (J1, J2, C3)
  • Novel deep architectures for spectro-spatio-temporal feature representation (J12, C7, C9, C12)
  • Neurodegenerative disease-related brain region discovery via graph neural networks (J11)
  • Bayesian- and topology-grounded machine/deep learning methods (J3)
Multi-modal Data Representation and Decision-Making
  • Neuroimaging-genetic data association mining via deep generative-discriminative learning (C1, C6, J5)
  • Deep learning for industrial problems: synthetic device image generation, yield prediction, and job scheduling

Publications - International Journals

  • (J1) W. Ko, J. Choe, and J. Kang, "SLEEP-SAFE: Self-supervised Learning for Estimating Electroencephalogram Patterns with Structural Analysis of Fatigue Evidence," IEEE Access, vol. 13, pp. 35805-35817, 2025. (JCR-IF 3.400; CS-Information Systems 87/250) link
  • (J2) W. Ko, S. Jeong, S.-K. Song, and H.-I. Suk, "EEG-Oriented Self-Supervised Learning with Triple Information Pathways Network," IEEE Trans. Cybernetics, vol. 54, no. 11, pp. 6495-6508, 2024. (JCR-IF 11.800; CS-Cybernetics 1/24) link
  • (J3) S. Jeong, W. Ko, A. W. Mulyadi, and H.-I. Suk, "Deep Efficient Continuous Manifold Learning for Time Series Modeling," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 46, no. 1, pp. 171-184, 2023. (JCR-IF 23.600; CS-Artificial Intelligence 2/145) link
  • (J4) J. Phyo, W. Ko, E. Jeon, and H.-I. Suk, "TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging," IEEE Trans. Cybernetics, vol. 53, no. 9, pp. 4500-4510, 2023. (JCR-IF 19.118; Automation & Control Systems 1/65) link
  • (J5) W. Ko, W. Jung, E. Jeon, and H.-I. Suk, "A Deep Generative-Discriminative Learning for Multi-modal Representation in Imaging Genetics," IEEE Trans. Medical Imaging, vol. 41, no. 9, pp. 2348-2359, 2022. (JCR-IF 11.037; Radiology, Nuclear Medicine & Medical Imaging 5/136) link
  • (J6) W. Ko, E. Jeon, J. S. Yoon, and H.-I. Suk, "Semi-Supervised Generative and Discriminative Adversarial Learning for Motor Imagery-based Brain-Computer Interface," Scientific Reports, vol. 12, no. 1, pp. 1-14, 2022. (JCR-IF 4.997; Multidisciplinary Sciences 19/74) link
  • (J7) W. Ko, E. Jeon, and H.-I. Suk, "A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs," IEEE Trans. Industrial Informatics, vol. 18, pp. 1873-1882, 2022. (JCR-IF 11.648; CS-Interdisciplinary Applications 4/112) link
  • (J8) E. Jeon, W. Ko, J. S. Yoon, and H.-I. Suk, "Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI," IEEE Trans. Neural Networks and Learning Systems, vol. 34, no. 2, pp. 739-749, 2023. (JCR-IF 14.255; CS-Theory & Methods 4/110) link
  • (J9) W. Ko†, E. Jeon†, S. Jeong, J. Phyo, and H.-I. Suk (†equal contribution), "A Survey on Deep Learning-based Short/Zero-calibration Approaches for EEG-based Brain-Computer Interfaces," Frontiers in Human Neuroscience, vol. 15, p. 258, 2021. (JCR-IF 3.473; Psychology 29/80) link
  • (J10) B.-K. Min, H.-S. Kim, W. Ko, M.-H. Ahn, H.-I. Suk, D. Pantazis, and R. T. Knight, "Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex," NeuroImage, vol. 237, p. 118165, 2021. (JCR-IF 7.400; Neuroimaging 2/14) link
  • (J11) J. Lee†, W. Ko†, E. Kang, and H.-I. Suk (†equal contribution), "A Unified Framework for Personalized Regions Selection and Functional Relation Modeling for Early MCI Identification," NeuroImage, vol. 236, p. 118048, 2021. (JCR-IF 7.400; Neuroimaging 2/14) link
  • (J12) W. Ko, E. Jeon, S. Jeong, and H.-I. Suk, "Multi-Scale Neural Network for EEG Representation Learning in BCI," IEEE Computational Intelligence Magazine, vol. 16, pp. 31-45, 2021. (JCR-IF 9.809; CS-Artificial Intelligence 15/145) link

Publications - International Conferences

  • (C1) C. Park†, M. Cho†, Y. Kim, W. Jung, and W. Ko (†equal contribution), "A Novel Self-Supervised Deep Learning Framework for Pattern Recognition of Neurodegenerative Disease via Graph-based Phenotype-Genotype Relation Modeling," in Proc. ACM/SIGAPP Symposium on Applied Computing (SAC), 2026, pp. 219-221. (Acceptance rate 23.4%) link
  • (C2) Y. Kang, C. Park, Y. Kim, and W. Ko, "Unsupervised Latent Context Representation of Electroencephalography for Label-Efficient Sleep Apnea Screening," in Proc. International Conference on Pattern Recognition (ICPR), 2026, Accepted. (Acceptance rate 49.6%)
  • (C3) W. Ko and H.-I. Suk, "EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation," in Proc. ACM Int. Conf. Information and Knowledge Management (CIKM), 2022, pp. 4143-4147. (Acceptance rate 27.51%) link
  • (C4) J. Phyo, W. Ko, E. Jeon, and H.-I. Suk, "Enhancing Contextual Encoding with Stage-Confusion and Stage-Transition Estimation for EEG-based Sleep Staging," in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1301-1305. (Acceptance rate 45%) link
  • (C5) S. Jeong, W. Ko, A. W. Mulyadi, and H.-I. Suk, "Continuous Riemannian Geometric Learning for Sleep Staging Classification," in Proc. Int. Winter Conf. Brain-Computer Interface (BCI), 2022, pp. 1-2. link
  • (C6) W. Ko, W. Jung, A. W. Mulyadi, E. Jeon, and H.-I. Suk, "ENGINE: Enhancing Neuroimaging and Genetic Information by Neural Embedding," in Proc. IEEE Int. Conf. Data Mining (ICDM), 2021, pp. 1162-1167. (Acceptance rate 20%) link
  • (C7) W. Ko, E. Jeon, and H.-I. Suk, "Spectro-Spatio-Temporal EEG Representation Learning for Imagined Speech Recognition," in Proc. Asian Conf. Pattern Recognition (ACPR), 2021, pp. 335-346. (Acceptance rate 55.1%) link
  • (C8) S. Jeong, E. Jeon, W. Ko, and H.-I. Suk, "Fine-grained Temporal Attention Network for EEG-based Seizure Detection," in Proc. Int. Winter Conf. Brain-Computer Interface (BCI), 2021, pp. 1-4. link
  • (C9) W. Ko, K. Oh, E. Jeon, and H.-I. Suk, "VIGNet: A Deep Convolutional Neural Network for EEG-based Driver Vigilance Estimation," in Proc. Int. Winter Conf. Brain-Computer Interface (BCI), 2020, pp. 1-3. link
  • (C10) E. Jeon, W. Ko, and H.-I. Suk, "Domain Adaptation with Source Selection for Motor-Imagery based BCI," in Proc. Int. Winter Conf. Brain-Computer Interface (BCI), 2019, pp. 1-4. link
  • (C11) W. Ko, E. Jeon, J. Lee, and H.-I. Suk, "Semi-Supervised Deep Adversarial Learning for Brain-Computer Interface," in Proc. Int. Winter Conf. Brain-Computer Interface (BCI), 2019, pp. 1-4. link
  • (C12) W. Ko, J. S. Yoon, E. Kang, E. Jun, J.-S. Choi, and H.-I. Suk, "Deep Recurrent Spatio-Temporal Neural Network for Motor Imagery based BCI," in Proc. Int. Winter Conf. Brain-Computer Interface (BCI), 2018, pp. 1-3. link

Research Projects & Grants

Development of a Unified Diagnostic Framework for Mental Disorder/Brain Disease through User-independent and Protocol-agnostic EEG Representation based on Interpretable Topological Deep Learning Techniques2025 - 2028
  • National Research Foundation of Korea, Outstanding Young Scientist (~$400,000)

Teaching

  • Advanced Machine Learning (LH002800) - Spring 2025. Slides / Notes & Exams
  • Introduction to AI Convergence (LH000100) - Spring 2025, Fall 2024
  • Mathematics for Artificial Intelligence (LH002500) - Fall 2024. Slides / Notes & Exams
  • Artificial Intelligence (LZ001600) - Fall 2024, Spring 2024
  • Data Structures (LZ000500) - Spring 2024. Slides / Notes & Exams
  • Data Structures and Applications (LH000500) - Fall 2023
  • C++ Programming (LZ000500) - Fall 2023. Slides / Exams

Editorial & Review Service

  • Editor - Frontiers in Human Neuroscience
  • Reviewer - IEEE Trans. Neural Networks and Learning Systems; IEEE Trans. Cybernetics; Biomedical Signal Processing and Control; Scientific Reports; Artificial Intelligence in Medicine; Asian Conf. on Pattern Recognition

Advisory

  • 초거대 AI 확산 조성 사업(군 대상품 상태 검사 데이터), (주)에스지앤아이 (2024)

Awards & Honors

  • Sungshin Excellent Teaching Award, Sungshin Women's University (2024)
  • Korea University Achievement Award, Korea University (2022)
  • NAVER Ph.D. Fellowship (~$5,000), NAVER (2021)
  • Fundamental Scientist Scholarship (~$20,000), JW Foundation (2021)
  • Outstanding Trainee Award, Korean Society for Human Brain Mapping (2021)
  • Graduate School Junior Research Fellow Grant, Korea University (2021)
  • Best Paper Award, Korea University (2021)
  • Student Award, International BCI Society (2021, 2018)
  • Best Presentation Award, Korean Society for Human Brain Mapping (2019)
  • Best Paper Award, Korea Computer Congress (2018)

Invited Talks & Seminars

  • Design and Implementation of Deep Learning Models using PyTorch, Sungshin Women's University (2025, 2024)
  • Deep Representation Learning in EEG Analysis, Korean Society for EEG and Neurophysiology (2024)
  • Deep Representation Learning in Biomedicine, The Catholic University of Korea (2024)
  • SK Winter Data Science Engineer School, SK Inc. (2023)
  • Deep Reinforcement Learning and Applications, SK hynix Inc. (2022)

Skills

Programming LanguagesPython, MATLAB, C, C++
ML/DL FrameworksTensorFlow, PyTorch, Scikit-Learn
OthersLaTeX, Linux, Git, Microsoft Office