AI신약개발 규제과학전공

Health care big data analysis, standardization, and interoperability based on medical artificial intelligence, machine learning, and deep learning are the main research and lecture fields. As an assistant professor at Catholic University of Medicine, I run the Biomedical Informatics and Data Analytics (BIDA) lab to train related human resources. Regarding the AI-based new drug development field of this project, we have been developing AI models that process large-scale multimodal data such as electronic medical records, bio-signals, and medical images and enhance analysis efficiency for many years. In particular, as the head of research in charge of the Health and Medical Technology Research and Development Project organized by the Ministry of Health and Welfare, he is carrying out the task of 'Development of Medical AI Technology Based on Multicenter-Multimodal Combined Learning' and has published more than 40 SCIE-level papers. Based on these data science research results and experience in carrying out national tasks, he is in charge of operating a curriculum specializing in regulatory science specialized in AI convergence new drug development. 

The main fields of research and education are pharmaceutical research, transitional research, and initial clinical trial design based on pharmacometrics modeling. As a professor of medical school at Catholic University, he has been in charge of the relevant curriculum and has served as editor-in-chief of the official journal (TCP) of the Korean Society of Clinical Pharmacology. He founded and served as CEO of AMS BioScience, a new drug development consulting firm, by incorporating academic research results into the industry. Currently, he is focusing on nurturing human resources as chairman of Cheongram BioAcademy, a new drug development consulting firm. Regarding the field of AI-based new drug development, we have been developing data science-based drug efficacy and side effect prediction model and developing algorithms to enhance the efficiency of clinical trial design that combine artificial intelligence technology. Based on the academic theory in the university and industry-academia-linked practical experience through biotech management, he will lead the development of specialized curriculum and operation of industry-academia projects to cultivate innovative human resources in global regulatory science. 

Clinical Pharmacology, Pharmacometrics, and Real-World Data (RWD) analysis are my primary areas of research and education. As a Clinical Assistant Professor and Research Professor at Seoul St. Mary's Hospital, The Catholic University of Korea, I am actively engaged in clinical pharmacology research and education.

In relation to the AI-based drug development initiatives of this program, I have applied precision medicine analytics and pharmacometric modeling to enhance the efficiency of clinical trials and improve the prediction of drug efficacy and adverse drug reactions. In particular, my research focuses on evaluating the clinical feasibility of drug candidates by leveraging advanced processing and analytical techniques for large-scale Real-World Data (RWD).

In addition, drawing on years of experience as a professional translator specializing in medical journals and pharmaceutical documentation, I support the education and training of globally competent regulatory science professionals equipped to address the regulatory challenges of advanced biotechnology through effective international communication and collaboration.

Data science-based Clinical Pharmacology, Pharmacometrics, and Early Drug Development are my primary areas of research and education. As an Associate Professor at the College of Medicine, The Catholic University of Korea, and a member of the Catholic Institute of Pharmacometrics and Precision Therapeutics (PIPET), I am actively engaged in both research and education in these fields.

In relation to the AI-based drug development initiatives of this program, I have integrated data science methodologies into drug development research by leveraging my academic background in Electrical Engineering. Using platforms and tools such as Linux (AWS), R, and NONMEM, I develop predictive models for drug efficacy and safety while improving the analytical efficiency of early-phase clinical trial data.

In addition, I bring practical industry experience through my role as a Director at AIMS Bioscience, a drug development consulting company. Based on this experience, I contribute to the development of curricula and the operation of industry–academia collaborative projects aimed at cultivating globally competitive regulatory science professionals equipped with advanced technologies.

Based on my research expertise in the College of Medicine, I conduct clinical research across the broad field of Pediatrics. My primary research areas include General Pediatrics and Pediatric Medicine, with a focus on evaluating diagnosis, treatment response, disease progression, and clinical outcomes using real-world clinical information collected during pediatric patient care.

In addition, my research emphasizes the standardization of clinical documentation and the consistency of longitudinal follow-up study design to establish reproducible and validated evaluation systems for pediatric patients.