people
Dr. Jang-Moo Lee, the incumbent Chairman of the KAIST Board of Trustees, has been re-elected to head the office. His term will begin from the date of approval by the Minister of Science, ICT and Future Planning of Korea and will last for three years.
Dr. Lee received his undergraduate and master’s degrees in mechanical engineering from Seoul National University. He later earned his doctoral degree in mechanical engineering from Iowa State University in the United States.
Joined the faculty of his alma mater in 1976, Dr. Lee held various posts within the university including the dean of the engineering college. He served as the president of the Association of Korean Engineering Colleges, the founding chairman of the Korea Evaluation Institute of Industrial Technology, the president of the Korean Society of Mechanical Engineers, the 24th president of Seoul National University, and the 13th president of the Korean Council for University Education. He now serves as the president of the National Science and Technology Council of Korea and the chairman of Climate Change Center’s Board of Directors.
Dr. Lee has received numerous honors and awards, among others, the Academic Award of the Korean Society of Mechanical Engineers (1985), the Order of Science and Technology Merit from the Korean government (2005), the National Academy of Sciences Award (2005), and the Order of Service Merit in Blue Stripes (2010) from the Korean government. He was also selected as the Alma Mater Proud from Kyunggi High School in 2011.
-
event Professor Mikyoung Lim from Mathematical Sciences to Deliver Keynote at International Conference on Applied Inverse Problems
<Professor Mikyoung Lim from KAIST Department of Mathematical Sciences> Professor Mikyoung Lim from KAIST Department of Mathematical Sciences gave a plenary talk on "Research on Inverse Problems based on Geometric Function Theory" at AIP 2025 (12th Applied Inverse Problems Conference). AIP is one of the leading international conferences in applied mathematics, organized biennially by the Inverse Problems International Association (IPIA). This year's conference was held from July 2
2025-08-14 -
event KAIST Takes the Lead in Developing Core Technologies for Generative AI National R&D Project
KAIST (President Kwang Hyung Lee) is leading the transition to AI Transformation (AX) by advancing research topics based on the practical technological demands of industries, fostering AI talent, and demonstrating research outcomes in industrial settings. In this context, KAIST announced on the 13th of August that it is at the forefront of strengthening the nation's AI technology competitiveness by developing core AI technologies via national R&D projects for generative AI led by the Minis
2025-08-13 -
research KAIST develops “FlexGNN,” a graph analysis AI 95 times faster with a single GPU
<(From Left) Donghyoung Han, CTO of GraphAI Co, Ph.D candidate Jeongmin Bae from KAIST, Professor Min-soo Kim from KAIST> Alongside text-based large language models (LLMs) including ChatGPT, in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial transactions, stocks, social media, and patient records in graph form are being actively used. However, there is a limitation in that full graph learning—training the entire
2025-08-13 -
research KAIST Develops World’s First Wireless OLED Contact Lens for Retinal Diagnostics
<ID-style photograph against a laboratory background featuring an OLED contact lens sample (center), flanked by the principal authors (left: Professor Seunghyup Yoo ; right: Dr. Jee Hoon Sim). Above them (from top to bottom) are: Professor Se Joon Woo, Professor Sei Kwang Hahn, Dr. Su-Bon Kim, and Dr. Hyeonwook Chae> Electroretinography (ERG) is an ophthalmic diagnostic method used to determine whether the retina is functioning normally. It is widely employed for diagnosing hereditary
2025-08-12 -
research KAIST Develops AI That Automatically Designs Optimal Drug Candidates for Cancer-Targeting Mutations
< (From left) Ph.D candidate Wonho Zhung, Ph.D cadidate Joongwon Lee , Prof. Woo Young Kim , Ph.D candidate Jisu Seo > Traditional drug development methods involve identifying a target protin (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate. KAIST researchers have developed an AI model th
2025-08-12