On December 18, the National Academy of Engineering of Korea selected the 100 top future technologies that will be the engine growth for Korea in 2025. It also announced experts that will lead the selected technologies. Out of the 238 experts listed, eight professors are from KAIST.
The academy chose technologies based on their potential to be commercialized and contribution to developing related industries in Korea. Giving more weight to young scientists or engineers, it also selected up to three experts who are playing a crucial role in realizing each technology.
Out of 238 experts, 78 of them are from universities, 76 from major enterprises, 65 from public institutions and 19 from SMEs. KAIST is the instituttion that produced the second most experts, follwoing Seoul National University.
<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
2025-08-14KAIST (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<(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<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< (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