CHANG-HOON JI

Ph.D. Candidate & AI Researcher specializing in Reinforcement Learning and Agentic AI
Seoul, KR.

About

Highly accomplished Ph.D. candidate at Korea University, specializing in Agentic AI and Reinforcement Learning with a strong focus on innovative applications in medical diagnosis and real-time personalization. Proven ability to conduct cutting-edge research, contribute to high-impact publications (including SCI Top 5% journals), and secure patents, driving advancements in AI for complex problem-solving.

Work

LG CNS
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Tutor, Introduction to Artificial Intelligence

Seoul, Seoul, Korea (Republic of)

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Summary

Provided comprehensive tutoring and mentoring for AI fundamentals and practical applications to students at LG CNS.

Highlights

Mentored numerous students on core AI principles and advanced machine learning techniques, fostering deep understanding and practical application skills.

Developed customized learning materials and exercises to clarify complex AI concepts, enhancing student engagement and knowledge retention.

Provided one-on-one and group support, resulting in improved comprehension of AI algorithms and their real-world implications.

Korea University
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Teaching Assistant, Reinforcement Learning

Seoul, Seoul, Korea (Republic of)

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Summary

Assisted university students with advanced Reinforcement Learning theory, assignments, and practical implementations.

Highlights

Supported students in mastering Reinforcement Learning theories, including advanced algorithms and model architectures, improving their grasp of complex topics.

Guided students through intricate assignments and practical coding implementations, troubleshooting issues and enhancing problem-solving abilities.

Facilitated a deeper understanding of RL concepts, contributing to improved academic performance and project outcomes for students.

Education

Korea University
Seoul, Seoul, Korea (Republic of)

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Integrated M.S. & Ph.D. Program

Artificial Intelligence

Courses

Agentic AI Systems

Reinforcement Learning

GUI-Agentic AI

Personalized AI

Doowon University
Anseong-si, Gyeonggi-do, Korea (Republic of)

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Bachelor's

Display and Electronic Engineering

Awards

Best Paper Award

Awarded By

Conference on Electronics, Semiconductor, and AI 2025

Awarded for the publication 'Adaptive Window Size Selection for MDD Diagnosis using Reinforcement Learning'.

Best Paper Award

Awarded By

Korean Association for Next Generation Computing Spring Conference (KINGPC)

Awarded for the publication 'Research on Dynamic Connectivity Pattern Generation Using Time-Series Generative Adversarial Networks for Autism Spectrum Disorder Diagnosis'.

Publications

Adaptive Static-Dynamic Functional Connectivity Feature Fusion for Diagnosis of Mild Cognitive Impairment.

Published by

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)

Summary

Kang, Y. K., Ji, C. H., Oh, J. H., Kwak, S. Y., Han, J. W., & Kam, T. E. (2026). ICASSP 2026.

Adaptive Reward Weighting via Reinforcement Learning for Major Depressive Disorder Diagnosis.

Published by

2026 International Conference on Electronics, Information, and Communication (ICEIC)

Summary

Hwang, J., Ji, C. H., Oh, J. H., Kang, Y. K., Kwak, S., Han, J. W., & Kam, T. E. (2026). ICEIC 2026.

Agentic Brain: Agentic AI System for Autonomous Diagnosis of Functional Connectivity Network.

Published by

2026 International Conference on Electronics, Information, and Communication (ICEIC)

Summary

Ji, C. H., Oh, J. H., Kang, Y. K., Kwak, S., Han, J. W., Kim, J. M., Hwang, J., & Kam, T. E. (2026). ICEIC 2026.

PT-Diff: Brain Network Generation with Personalized Topology Guided Conditional Diffusion Transformer.

Published by

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)

Summary

Oh, J. H., Ji, C. H., Kwak, S. Y., Kang, Y. K., Han, J. W., Kim, J. M., & Kam, T. E. (2026). ICASSP 2026.

Reinforcement Learning with Multi-Objective Rewards for Functional Connectivity Augmentation.

Published by

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)

Summary

Ji, C. H., Hwang, J., Oh, J. H., Kang, Y. K., Kwak, S. Y., Han, J. W., & Kam, T. E. (2026). ICASSP 2026.

Source-driven enhanced semantic style transfer for addressing BCI illiteracy in subject-independent EEG-based motor imagery classification.

Published by

Applied Soft Computing

Summary

Han, J. W., Shin, D. H., Ji, C. H., Lee, D. J., Kim, J. M., Choi, W., Oh, J. H., Cho, S., Son, Y. H., & Kam, T. E. (2025). Applied Soft Computing, 157, 114482. (IF: 6.6, SCI Top 10%)

Visual Decoding Using a Learnable Wavelet-Based Spatial-Spectral-Temporal EEG Embedding.

Published by

2025 13th International Conference on Brain-Computer Interface (BCI)

Summary

Choi, Y., Kim, J. M., Choi, W., Ji, C. H., Oh, J. H., & Kam, T. E. (2025). BCI, 1-5.

Adaptive Window Size Selection for MDD Diagnosis using Reinforcement Learning.

Published by

Conference on Electronics, Semiconductor, and AI 2025

Summary

Ji, C. H., Hwang, J., Choi, W., Kim, J. M., Oh, J. H., & Kam, T. E. (2025). Electronics, Semiconductor, and AI 2025, 136. (Best Paper Award)

Implicit and Explicit Domain Alignment for Cross-dataset Brain-Computer Inference.

Published by

Conference on Electronics, Semiconductor, and AI 2025

Summary

Kim, J. M., Ji, C. H., Oh, J. H., Bak, S., Choi, W., Hwang, J., & Kam, T. E. (2025). Electronics, Semiconductor, and AI 2025, 180.

Synthetic Augmentation of Functional Connectivity using R3GAN for Major Depressive Disorder Diagnosis.

Published by

Conference on Electronics, Semiconductor, and AI 2025

Summary

Hwang, J., Ji, C. H., Oh, J. H., Choi, W., Kim, J. M., & Kam, T. E. (2025). Electronics, Semiconductor, and AI 2025, 182.

Patient-Specific Spectral-Spatial-Temporal Feature Learning for EEG-based Seizure Prediction.

Published by

The 12th Conference of Korean Artificial Intelligence Association (CKAIA 2025)

Summary

Choi, W., Kim, J. M., Ji, C. H., Hwang, J., & Kam, T. E. (2025). CKAIA 2025.

Towards Reliable Policy Convergence in Multi-Agent Systems with Equilibrium-Based Deep Reinforcement Learning.

Published by

Conference on Electronics, Semiconductor, and AI 2024

Summary

Ji, C. H., Oh, J. H., Kim, J. M., & Kam, T. E. (2024). Electronics, Semiconductor, and AI 2024, 80.

MARS: Multiagent Reinforcement Learning for Spatial-Spectral and Temporal Feature Selection in EEG-based BCI.

Published by

IEEE Transactions on Systems Man, and Cybernetics: Systems

Summary

Shin, D. H., Son, Y. H., Kim, J. M., Ahn, H. J., Seo, J. H., Ji, C. H., Han, J. W., Lee, B. J., et al. (2024). IEEE Transactions on Systems Man, and Cybernetics: Systems, 54(5), 3084-3096. (IF: 8.7, SCI Top 5%)

Sparse Graph Representation Learning based on Reinforcement Learning for Personalized Mild Cognitive Impairment (MCI) Diagnosis.

Published by

IEEE Journal of Biomedical and Health Informatics

Summary

Ji, C. H., Shin, D. H., Son, Y. H., & Kam, T. E. (2024). IEEE Journal of Biomedical and Health Informatics, 28(8), 4842-4853. (IF: 6.8, SCI Top 10%)

Research on Dynamic Connectivity Pattern Generation Using Time-Series Generative Adversarial Networks for Autism Spectrum Disorder Diagnosis.

Published by

Korean Association for Next Generation Computing Spring Conference (KINGPC)

Summary

Oh, J. H., Ji, C. H., & Kam, T. E. (2024). KINGPC. (Best Paper Award)

Integrating Equilibrium based Reinforcement Learning for Improved Multi-Agent Coordination and Decision-Making.

Published by

The 10th International Conference on Next Generation Computing 2024

Summary

Ji, C. H., Oh, J. H., Kim, J. M., Bak, S., Kang, Y. K., & Kam, T. E. (2024). Next Generation Computing 2024, 319-322.

Graph-based Conditional Generative Adversarial Networks for Major Depressive Disorder Diagnosis with Synthetic Functional Brain Network Generation.

Published by

IEEE Journal of Biomedical and Health Informatics

Summary

Oh, J. H., Lee, D. J., Ji, C. H., Shin, D. H., Han, J. W., Son, Y. H., & Kam, T. E. (2023). IEEE Journal of Biomedical and Health Informatics, 28(3), 1504-1515. (IF: 6.8, SCI Top 10%)

Languages

Korean
English

Skills

Programming & Tools

Python, PyTorch, TensorFlow (inferred), Data Analysis Tools.

Artificial Intelligence & Machine Learning

Reinforcement Learning (RL), Agentic AI Systems, Deep Learning, Generative Adversarial Networks (GANs), Graph Representation Learning, Multi-agent Coordination, Adaptive Reward Modeling, Personalized AI, Diffusion Transformers, Machine Learning Algorithms.

Biomedical AI & Healthcare Applications

RL-based Medical Diagnosis, Brain-Computer Interface (BCI), EEG-based Classification, Mild Cognitive Impairment (MCI) Diagnosis, Major Depressive Disorder (MDD) Diagnosis, Autism Spectrum Disorder Diagnosis, Brain Disorder Diagnosis, Signal Processing, Functional Connectivity Analysis.

Research & Development

Scientific Research, Experimental Design, Data Interpretation, Patent Application, Academic Publishing, Problem-Solving, Collaborative Intelligence, Technical Writing.

Teaching & Mentoring

AI Fundamentals, Curriculum Development, Student Mentorship, Technical Instruction, Academic Support.

Projects

Device and method for personalized diagnosis of brain disease based on RL

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Summary

Co-invented a novel device and method utilizing Reinforcement Learning for personalized brain disease diagnosis, leading to a patent application.