Mr. Dohyoung Kim | Distributed Computing Methodologies | Best Researcher Award
Dohyoung Kim is a dedicated computer scientist pursuing his Master’s in IT Convergence Engineering at Gachon University, Korea. With a strong academic foundation in Computer Engineering and extensive research experience in artificial intelligence (AI), federated learning (FL), and medical informatics, he is recognized for his impactful contributions to fairness and privacy-preserving machine learning. As a research assistant in the Open Convergence Lab, he has co-led national AI projects funded by the Ministry of Science and ICT and other government agencies. Dohyoung’s innovative work is reflected in several SCIE-indexed publications, patents, and technical solutions aimed at mental health, anomaly detection, and medical big data. He has received multiple awards, including the Korean Society of Medical Informatics President’s Award and recognition from the Korean Artificial-Intelligence Convergence Technology Society. His vision is to bridge AI and healthcare through equitable, secure, and practical ML systems for real-world deployment.
Profile
🎓 Education
Dohyoung Kim holds a Bachelor of Science in Computer Engineering from Kangwon National University (2016–2020) and Gachon University (2021–2023), with a strong GPA of 3.87/4.5. He is currently pursuing a Master of Science in IT Convergence Engineering at Gachon University (2023–2025), maintaining an impressive GPA of 4.25/4.5. His educational journey is distinguished by scholarships for excellence in AI and medical informatics research, including full graduate tuition support. He has worked on various academic projects such as XLNet-based EEG classification, multimodal data analysis, and pseudonymization methods. Kim’s coursework includes deep learning, big data, cloud computing, and AI ethics, and he has delivered lectures and assisted in courses like Medical Informatics and Big Data Technology. His academic path showcases a deep commitment to integrating advanced machine learning into healthcare, especially in federated systems that respect user privacy and fairness.
🧪 Experience
Dohyoung Kim serves as a Research Assistant at the Open Convergence Lab, Gachon University, under Prof. Youngho Lee, where he has participated in five major national AI projects. These initiatives include building digital health platforms, medical big data systems for rare diseases, brain-computer interface (BCI) standardization, and behavior intervention technologies. His contributions span research design, algorithm development, and system integration. He has published multiple first-author papers in journals such as IEEE Access and Expert Systems with Applications, focusing on federated learning and bias mitigation. He has also registered several patents related to mental health monitoring, gamified behavioral interventions, and emotional assessment. In addition to research, Kim has taught numerous courses as a teaching assistant, such as Big Data Analysis, Cloud Programming, and ChatGPT application. His industry collaboration includes anomaly detection with Good Morning Information Tech and mobile app development, demonstrating well-rounded, real-world technical and leadership experience.
🏅 Awards and Honors
Dohyoung Kim has received numerous prestigious awards, reflecting his excellence in AI and medical informatics. In 2025, he earned the President’s Award from the Korean Society of Medical Informatics (KOSMI) for outstanding academic performance and research. He has twice received KOSMI’s Best Research Group Award (2023, 2024), with grants totaling $3,000 for pioneering smart data systems. His paper on anomaly detection using federated learning won the Excellent Paper Award from the Korean Artificial-Intelligence Convergence Technology Society (KAICTS) in 2023. Kim also achieved 7th place out of 94 in the Dacon Machine Learning Competition (2020), showcasing his early potential in behavioral AI. His academic excellence is further recognized through a series of scholarships from Gachon University, including those for SCI-level publications, departmental distinction, and undergraduate R&D internships. These accolades highlight his consistent contributions to innovation, academic rigor, and the application of ethical AI in healthcare.
🔬 Research Focus
Dohyoung Kim’s research centers on Fair and Privacy-Preserving Federated Learning (FL), with strong interests in semi-supervised learning (SSL), multimodal AI, and clinical information extraction. He develops algorithms like ACMFed and HFAD, designed to tackle bias, fairness, and communication efficiency in healthcare and industrial AI environments. His work advances real-world deployment of federated AI for applications such as pneumonia classification, brain disease prediction, mental health monitoring, and anomaly detection. Kim’s research also extends into the fusion of brain-computer interface (BCI) data with AI, exploring emotional psychology, behavioral gamification, and ethical data governance. His commitment to high-impact, reproducible science is reflected in six peer-reviewed journal articles, multiple national projects, and patents. His interdisciplinary approach blends machine learning with cognitive science and clinical informatics, aiming to empower medical decision-making and mental health support systems through trustworthy AI.
✅ Conclusion
Dohyoung Kim’s innovative contributions to federated learning and AI fairness, coupled with his scholarly excellence, national-level project leadership, and award-winning research, make him an exemplary nominee for the International Cognitive Scientist Award.
Publications
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Development of pneumonia patient classification model using fair federated learning
D Kim, K Oh, S Kang, Y LeeInternational Conference on Intelligent Human Computer Interaction, 153-164 - Addressing bias and fairness using fair federated learning: A synthetic review
D Kim, H Woo, Y LeeElectronics 13 (23), 4664
- AB-XLNet: named entity recognition tool for health information technology standardization
K Oh, M Kang, SH Oh, D Kim, S Kang, Y Lee2022 13th International Conference on Information and Communication …
- ACMFed: Fair semi-supervised federated learning with additional compromise model
D Kim, K Lee, Y Lee, H WooIEEE Access
- Overview of fair federated learning for fairness and privacy preservation
D Kim, K Oh, Y Lee, H WooExpert Systems with Applications, 128568
- Addressing Bias and Fairness using Fair Federated Learning: A Systematic Literature Review
D Kim, H Woo, Y LeePreprints
- HFAD: 공정한 연합학습 및 하이브리드 융합 멀티모달 산업 이상 탐지
김도형, 오경수, 이영호한국정보통신학회논문지 28 (7), 805-814
- Comparison of Federated Learning and Fair Federated Learning for Pneumonia Patient Classification
K Na, D Kim, Y LeeJournal of Health Informatics and Statistics 50 (1), 31-38