For the first time, the university has broken into the ranks of top 50 global universities since the first release of the rankings in 2004. The 2015 QS World University Rankings were released on September 15, 2015. Overall, KAIST ranked 43rd, advancing eight steps up from last year’s results. Its engineering and technology rank now places it 13th in the global rankings, but it is first in Korea's rankings. Both represent the highest record KAIST has ever attained since the QS Rankings began in 2004. The QS Rankings uses six performance indicators to assess universities’ global reputation, research impact, staffing levels, and international complexion. The indicators are: academic reputation (40%), employer reputation (10%), student-to-faculty ratio (20%), number of citations per faculty publications (20%), international to domestic faculty ratio (5%), and international to domestic student ratio (5%). The Massachusetts Institute of Technology (MIT) topped the 2015 list, with Harvard University coming in second place. The University of Cambridge and Stanford University jointly ranked third. For details on the 2015 QS World University Rankings, see http://www.topuniversities.com/university-rankings-articles/world-university-rankings/qs-world-university-rankings-201516-out-now.
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<(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<(From left)Professor Jimin Park, Ph.D candidate Myeongeun Lee, Ph.D cadidate Jaewoong Lee,Professor Jihan Kim> Cells use various signaling molecules to regulate the nervous, immune, and vascular systems. Among these, nitric oxide (NO) and ammonia (NH₃) play important roles, but their chemical instability and gaseous nature make them difficult to generate or control externally. A KAIST research team has developed a platform that generates specific signaling molecules in situ from a si
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