"2026 Artificial Intelligence Summer School" Successfully Held at Yonsei University (July 15, 2026)

Hosted by the Mechanical and Artificial Intelligence Research Group of the Korean Society of Mechanical Engineers and sponsored by the BK Project Team of the School of Mechanical Engineering, the “2026 Artificial Intelligence Summer School," hosted by the Mechanical and Artificial Intelligence Research Society of the Korean Society of Mechanical Engineers and sponsored by the BK Project Team of the Department of Mechanical Engineering, was successfully held for three days from Monday, July 13, 2026, to Wednesday, July 15, 2026, in Room B106 of the Grand Auditorium at Yonsei University’s College of Engineering. This summer school was an in-depth educational program covering a wide range of topics, from core theories of artificial intelligence to the latest research trends, and consisted of a total of five lectures.

On the first day, Professor Park Beom-soo (Seoul National University of Science and Technology) delivered a lecture on “Generative Artificial Intelligence.” On the second day, Professor Kim Tae-jin (Chonbuk National University) covered “Physics-Informed AI (PINN, DeepONet, Neural Operators),” while Professor Park Chan-hee (University of Seoul) discussed “LLMs and AI Agents (Principles and Learning Methods of LLMs, RAG, Ontology).” On the third day, Professor Ha Jong-moon (Ajou University) lectured on “Foundation Models (Self-Supervised Learning, Transfer Learning),” and Professor Min Kyung-min of the Department of Mechanical Engineering at Yonsei University lectured on “Efficient Learning (Active Learning, Domain Adaptation, and Beyond).”

In particular, Professor Min Kyung-min of the Department of Mechanical Engineering at Yonsei University began by examining real-world engineering problems with limited labeled data as a starting point to provide an overview of the entire landscape of data-efficient learning, and introduced the latest research trends and core methodologies, ranging from query strategies in Active Learning to Domain Adaptation and Autonomous Discovery. The event was attended by a total of 150 participants and concluded amid high interest from researchers in both industry and academia.