Dohyoung Kim | Distributed Computing Methodologies | Best Researcher Award

Published on by Cognitive Scientist Awards

Mr. Dohyoung Kim | Distributed Computing Methodologies | Best Researcher Award

Gachon University | South Korea

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

Googlescholar

Education

Mr. Dohyoung Kim has pursued a strong academic foundation in computer and information technologies, beginning with his undergraduate studies in Computer Engineering. He built a robust understanding of core principles such as programming, data structures, algorithms, and software systems. Continuing his educational journey, he completed further undergraduate coursework, solidifying his expertise and enhancing his practical skills in computer systems and networks. Currently, Mr. Kim is advancing his knowledge through a Master of Science program in IT Convergence Engineering at Gachon University, South Korea. His graduate studies focus on the intersection of information technology and multidisciplinary applications, with particular emphasis on artificial intelligence and machine learning. Notably, he has been involved in a specialized research project titled “SSMFed,” which explores the development of a fair federated learning model using SubSyncModel and semi-supervised learning. This educational path reflects his dedication to innovation, technical excellence, and contributions to evolving digital technologies.

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. 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.  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. His paper on anomaly detection using federated learning won the Excellent Paper Award from the Korean Artificial-Intelligence Convergence Technology Society (KAICTS) , 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.

Publications

Development of pneumonia patient classification model using fair federated learning
Year: 2023
Citations: 9

Addressing bias and fairness using fair federated learning: A synthetic review
Year: 2024
Citations: 7

AB-XLNet: named entity recognition tool for health information technology standardization
Year: 2022
Citations: 4

ACMFed: Fair semi-supervised federated learning with additional compromise model
Year: 2025
Citations: 2

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.

Dingming Wu | Computer Science | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.