Portfolio

I. PERSONAL PROFILE

With a Ph.D. in Industrial Engineering and over a decade of research excellence at the Data Mining Laboratory, I specialize in developing adaptive machine learning frameworks for complex, real-world datasets. My core expertise lies in Continual Learning—mitigating catastrophic forgetting in evolving data environments—and Semi-Supervised Learning, which I have successfully applied to high-impact challenges in infectious disease forecasting and genomic data vectorization.
Having published extensively in premier journals such as JAMIA, BMC Bioinformatics, and Scientific Reports, I have demonstrated a consistent ability to bridge the gap between advanced computational theories and practical solutions for public health and environmental safety. As a proactive researcher with a proven track record of securing patents and leading interdisciplinary projects, I am committed to pioneering 'Smart Environmental Systems' that leverage AI to predict, monitor, and mitigate biological and environmental risks. I aim to contribute to Korea University’s Department of Environmental Systems Engineering by fostering cutting-edge research and nurturing the next generation of engineers at the intersection of AI and environmental sustainability.

II. Educations

• Ajou University, Suwon, Republic of Korea, 2026
  Ph.D. in Industrial Engineering (Machine Learning)
  Thesis Title: Regularization Methods for Mitigating Catastrophic Forgetting in Multi-Task Continual Learning


• Ajou University, Suwon, Republic of Korea, 2014
  M.S. in Industrial Engineering (Machine Learning)
  Thesis Title: Pseudo labeling method for improving prediction accuracy of Semi Supervised Learning


• Ajou University, Suwon, Republic of Korea, 2012
  B.S. in Industrial Engineering

III. Publications

• Juhyeon Kim, Hyunjung Shin. Breast Cancer Survivability Prediction with Labeled, Unlabeled, and Pseudo-Labeled Patient Data. Journal of the American Medical Informatics Association, 2013


• Juhyeon Kim, Insung Ahn. Weekly ILI patient ratio change prediction using news articles with support vector machine. BMC Bioinformatics, 2019


• Soobeom Choi, Juhyeon Kim, Insung Ahn. Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries. PLoS One, 2019


• Juhyeon Kim, Insung Ahn. Infectious disease outbreak prediction using media articles with machine learning models. Scientific Reports, 2021


• Juhyeon Kim, Seyeon Cheon, Insung Ahn. NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations. BMC Bioinformatics, 2022


• Sunghong Park, Dong-gi Lee, Juhyeon Kim, et al. PPIxGPN: plasma proteomic profiling of neurodegenerative biomarkers with protein–protein interaction-based eXplainable graph propagational network. Briefings in Bioinformatics, 2025


• Sunghong Park, Dong-gi Lee, Juhyeon Kim, et al. Explainable multiplex graph propagational network with multimodal neuroimage integration for dementia subtype diagnosis. Neural Networks, 2025


• Sunghong Park, Dong-gi Lee, Juhyeon Kim, et al. BIGPN: Biologically informed graph propagational network for plasma proteomic profiling of neurodegenerative biomarkers. Artificial Intelligence in Medicine, 2025

IV. Conference Presentations

• Juhyeon Kim, Hyunjung Shin. Enhancing the Performance of Semi Supervised Learning with Pseudo-Labeled Data. Proc. of the Business Intelligence and Data Mining Conference (BIDM 2011 Fall), December 1st, 2011. Busan, Korea


• Juhyeon Kim, Hyunjung Shin. Fraudulent Bill claim Abuser Detection in Health care with Boosted Samples from Co-training. Proc. of the Business Intelligence and Data Mining Conference (BIDM 2012 Spring), pp. 30-45. April 24th, 2012. COEX, Seoul, Korea


• Junwoo Lee, Juhyeon Kim and Hyunjung Shin. Fraudulent Bill-Claim Detection in Health Insurance. Proc. of the 8th International Conference on Data Mining (ICDM 2012), pp. 50-56. July 16th-19th, 2012. Las Vegas, USA


• Juhyeon Kim, Hyunjung Shin. Breast Cancer Survivability Prediction with Labeled, Unlabeled, and Pseudo-Labeled Patient Data. Proc. of Translational Bioinformatics Conference 2012 (TBC2012), October 13rd -16th 2012. Jeju island, Korea


• Juhyeon Kim, Insung Ahn. Estimating the Number of Infectious Disease Emergence Using Semi Supervised Learning. Proc. of the Business Intelligence and Data Mining Conference (BIDM), April 15th, 2016. Jeju Island, Korea


• Juhyeon Kim. Detecting Influenza Outbreaks in United States by Analyzing Climatic Heat Maps Using Convolutional Neural Network Proc. of the 2017 International Conference on Data Mining (ICDM 2017), pp. 24-27. Las Vegas, USA


• Juhyeon Kim. DUtilizing News Article data to Predict Infectious Disease Outbreak and Spread Proc. of the 2019 EKC 2019, Jul 17th, 2019, Vienna, Austria


• Juhyeon Kim. Pre-processing SARS-CoV-2 Sequence Data for Application of Machine Learning Techniques for Visualization and Clustering of Virus Characteristics. Proc. of the 2022 Asia-Pacific Signal and Information Processing Association(APSIPA 2022), November 9th, 2022. Chiangmai, Thailand


• Juhyeon Kim. Continual Learning with Flexible Preservation. Proc. of the IEEE International Conference on Big Data and Smart Computing (BigComp), February 11th, 2025. Kotakinabalu, Malaysia

V. Patents

• Juhyeon Kim, Insung Ahn, Youngmahn Han. System and method for real time infectious disease horizon scan. KOR 10-2017-0170011. December 12th, 2017


• Soobeom Choi, Insung Ahn, Juhyeon Kim. PREDICTION APPARATUS FOR SPREADING OF EPIDEMIC, AND CONTROL METHOD THEREOF. KOR 10-2019-0116153. July 31st, 2020