김경수 연구실

AIBL is distinguished by an integrated three-layer research framework that connects mathematical foundations, advanced AI technologies, and real-world translation. Formal characterization of model assumptions, identifiability, convergence, robustness, computational complexity, and failure modes. The objective is not merely to apply established architectures, but to formulate fundamental research questions and develop original theories, algorithms, and learning principles that can advance Core AI itself. Reliable deployment under practical constraints involving privacy, security, latency, computational resources, and system robustness. New digital medicine technologies that may progress, where appropriate, toward real-world deployment and clinical validation. This research framework establishes a continuous pathway from mathematical principles and original AI algorithms to globally applicable Core AI systems, and subsequently to advanced biomedical technologies. Clinical validation is treated as an important potential endpoint of technological translation rather than the sole focus of the laboratory. Translate research outcomes into globally relevant Core AI and biomedical technologies. Researchers are supported in developing work suitable for publication at premier international AI and machine learning conferences and in leading journals across AI, biomedical AI, bioinformatics, computational biology, and digital medicine. The principal investigator, Professor Kyungsu Kim, is affiliated with the School of Transdisciplinary Innovations, the College of Medicine (Department of Biomedical Sciences), and the AI Graduate School (Department of Engineering, IPAI (Interdisciplinary Program in Artificial Intelligence)) at Seoul National University. Through an extensive collaborat