Reuters News released a list of the World’s Top 100 Most Innovative Universities on September 15, 2015.
Nine of the top ten universities on the list were American institutions. KAIST took tenth place, the only non-American and Asian university to do so.
Stanford University ranked first, followed by the Massachusetts Institute of Technology (MIT) in second, and Harvard University in third.
The inaugural Reuters Top 100 survey based its rankings on ten criteria: patent volume, patent successes, global patents, patent citations, patent citation impact, percent of patents cited, patent to article citation impact, industry article citation impact, percent of industry collaborative articles, and the total number of science papers.
Japan had nine universities in the survey, more than all countries except for the United States. South Korea has a total of eight universities on the list including Pohang University of Science and Technology, Seoul National University, Yonsei University, and Hanyang University.
For the full details of the survey, see
http://www.reuters.com/article/2015/09/15/idUSL1N11K16Q20150915.
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
2025-08-12