%41%49
-
Reading the Optical Fingerprint of Materials in Real-Time with AI
< (From Left) KAIST Dr. Jongchan Kim, Professor Sanghoo Park >
Just as every person has a unique fingerprint, every material has its own unique ‘optical fingerprint.’ Spectroscopy, which has identified materials without contact in fields ranging from semiconductor processes to environmental monitoring, disease diagnosis, and space research, has been called the ‘eyes of science.’ A KAIST research team has implemented spectroscopic analysis, which previously relied on the experience of experts, into AI-based automatic and real-time interpretation technology, greatly expanding its applicability in various industrial fields such as semiconductors, environment, and medicine.
The research team led by Professor Sanghoo Park of our university's Department of Nuclear and Quantum Engineering announced on the 3rd that they have developed ‘AI-based deep spectral interpretation technology’ that allows artificial intelligence to automatically interpret various spectral data in real-time, overcoming limitations such as noise, contamination, defects, and complex overlapping signals.
A spectrum is a graph that spreads out light emitted or absorbed by a material like a rainbow. Existing spectroscopic analysis had to manually analyze signals appearing as numbers in this spectrum by comparing them one by one with well-known reference data. Instead of this method, the research team enabled the artificial intelligence to recognize the entire spectrum as a single ‘image’ and learn its patterns.
< Deep learning-based spectrum technology >
As a result, even in situations where noise was mixed in the data or some parts were lost, the AI accurately identified material information as if it were recognizing an object in a photo. Furthermore, it equipped a function to self-check whether the prediction results are scientifically valid, significantly increasing the reliability of the analysis.
The research team verified this technology by applying it to absorption spectroscopy data widely used in atmospheric and plasma chemistry. As a result, they succeeded in predicting the concentrations of eight chemical substances, including ozone and nitrogen oxides, with very high accuracy even among complexly mixed signals. It was not only more accurate than existing manual analysis but also showed stable performance even in environments with poor data quality.
This research is expected to be a turning point in converting vast amounts of spectroscopic data, which were previously discarded due to the difficulty of analysis, into ‘immediately usable information.’ In particular, it has high potential for use in various high-tech industrial fields, such as improving yield in semiconductor plasma processes, stable control of nuclear fusion plasma, environmental monitoring in smart cities, and non-contact disease diagnosis.
< Research Image >
Professor Sanghoo Park said, “This technology is an achievement that significantly lowers the entry barrier for spectroscopic data analysis, which used to rely on the experience of experts,” and added, “It can be immediately applied to overall industries requiring spectral analysis, such as environmental monitoring, healthcare, and plasma diagnosis.”
In this study, doctoral students Jongchan Kim and Seong-Cheol Huh participated as co-first authors, and Jin Hee Bae and Su-Jin Shin also contributed to the research. The results were published online on January 12th in the prestigious international academic journal in the field of measurement and analytical chemistry, ‘Sensors and Actuators B: Chemical.’
※ Paper title: Deep spectral deconvolution for image-based broadband spectral data analysis DOI: https://doi.org/10.1016/j.snb.2025.139369
Meanwhile, this research was conducted with support from the Ministry of Science and ICT’s Global TOP Strategic Research Group Support Program, the KAIST Leap Research Project, and the Korea Institute of Materials Science (KIMS).
2026.02.03 View 2035 -
KAIST Team Wins Grand Prize at Kakao AI Incubation Project
<(From Left) Professor Jongse Park, Professor youngjin Kwon, Professor Jaehyuk Huh, Professor Knunle Olukotun>
Currently, Large Language Model (LLM) services like ChatGPT rely heavily on expensive GPU servers. This structure faces significant limitations, as costs and power consumption skyrocket as service scales increase. Researchers at KAIST have developed a next-generation AI infrastructure technology to address these challenges.
KAIST announced on January 30th that the ‘AnyBridge AI’ team, led by Professor Jongse Park from the School of Computing, has developed a next-generation AI infrastructure software. This software allows for efficient LLM services by integrating various AI accelerators instead of relying solely on GPUs. The technology won the Grand Prize at the "4 ISTs (Science & Tech Institutes) × Kakao AI Incubation Project" hosted by Kakao.
This project is a joint industry-academic collaboration between Kakao and the four major science and technology institutes (KAIST, GIST, DGIST, and UNIST). It selected outstanding teams by evaluating the technical prowess and business viability of preliminary startup teams based on AI technology. The Grand Prize winning team receives a total of 20 million KRW in prize money and up to 35 million KRW in Kakao Cloud credits.
AnyBridge AI is a technical startup team led by Professor Jongse Park (CEO), with Professors Youngjin Kwon and Jaehyuk Huh from KAIST's School of Computing participating. Based on research achievements in AI systems and computer architecture, the team aims to develop technology applicable to actual industrial sites. Furthermore, Professor Kunle Olukotun of Stanford University—co-founder of the Silicon Valley AI semiconductor startup SambaNova—is participating as an advisor to push for global technology and business expansion.
The AnyBridge team noted that most current LLM services are dependent on expensive GPU infrastructure, leading to structural limits where operating costs and power usage surge as services scale. The researchers analyzed that the root cause of this issue lies not in the performance of specific hardware, but in the absence of a system software layer capable of efficiently connecting and operating various AI accelerators, such as NPUs (AI-specialized chips) and PIMs (next-gen chips that process AI within memory), alongside GPUs.
<Technical diagram of AnyBridge: Enhancing LLM performance by flexibly utilizing various AI accelerators>
In response, the AnyBridge team proposed an integrated software stack that can service LLMs across the same interface and runtime environment, regardless of the accelerator type. Specifically, they received high praise for pointing out the limitations of existing GPU-centric LLM serving structures and presenting a "Multi-Accelerator LLM Serving Runtime Software" as their core technology.
This technology enables the implementation of a flexible AI infrastructure where the most suitable AI accelerator can be selected and combined based on the task's characteristics, without being tied to a specific vendor or hardware. This is evaluated as a major advantage that can reduce costs and power consumption while significantly increasing scalability for LLM services.
<Illustration of the Multi-Accelerator LLM Service Platform - AI-generated image>
Additionally, based on years of accumulated research in LLM serving system simulation, the AnyBridge team possesses a research foundation that can pre-verify various hardware/software design combinations without building a large-scale physical infrastructure. This point demonstrated both the technical maturity and the industrial feasibility of their work.
"This award is a result of recognizing the necessity of system software that integrates various AI accelerators, moving beyond the limits of GPU-centric AI infrastructure," said Professor Jongse Park. He added, "It is meaningful that we could expand our research results into industrial fields and entrepreneurship. We will continue to develop this into a core technology for next-generation LLM serving infrastructure through cooperation with industrial partners."
This award is seen as a prime example of KAIST's research moving beyond academic papers toward next-generation AI infrastructure technology and startups. AnyBridge AI plans to advance and verify its technology through future collaborations with Kakao and related industrial partners.
<Photo of the Grand Prize ceremony: Left - Kakao Investment CEO Do-young Kim; Right - KAIST Prof. Jongse Park>
2026.01.30 View 3342 -
World-Renowned Masters Rodin and Chagall Meet at KAIST Museum
KAIST announced that its museum has received a donation of works by world-renowned masters Auguste Rodin and Marc Chagall from an anonymous donor, and has opened a permanent exhibition starting on the 29th. This donation is expected to not only cultivate the cultural and artistic sensibilities of the KAIST community but also contribute to the qualitative expansion of the museum's permanent collection.
The donation was made possible through the wishes of a donor who chose to remain anonymous. The donor expressed, "I hope that the members of KAIST will expand their sensibilities and imagination through art, beyond their scientific and technological research. I want the KAIST Museum to become a cultural landmark on campus and a space that provides inspiration to students."
The donated pieces consist of one bronze sculpture by Auguste Rodin, often called the "Saint of Sculpture," and one lithograph by Marc Chagall, a master of 20th-century modern art.
Rodin’s <Study for Adam Near a Column> is a preparatory piece created while conceiving "Adam," a figure featured in his immortal masterpiece, The Gates of Hell. Based on a plaster mold created around 1912 during Rodin's lifetime, this is the fourth (4/12) of twelve official casts produced posthumously by the Musée Rodin in France. It captures the essence of Rodin’s sculptural art, delicately expressing inner human agony through muscular detail and a twisted posture.
<Rodin’s Study for Adam Near a Column (45-degree side view, post-installation)]
Chagall’s <Circus with a Yellow Clown> is a lithograph produced in 1967 at the Mourlot lithography studio in France. Throughout his life, Chagall used the theme of the "circus" to express the joys and sorrows of humanity and a surreal world of dreams. This work combines the dynamism of circus performers with a fantastical atmosphere through vibrant colors and a free-form composition, capturing a unique worldview akin to a piece of poetry. It is the 104th piece of a total of 150 (104/150) and is regarded as a masterpiece that maximizes Chagall's signature lyrical imagination.
<Chagall’s Circus with a Yellow Clown (unframed)>
KAIST President Kwang-Hyung Lee stated, "It is very meaningful to acquire masterpieces of such high value in global art history. Through these works, which contain diverse perspectives on humanity and the world, I look forward to the KAIST Museum establishing itself as a cultural space where intelligence and emotion coexist."
The donated works by Rodin and Chagall will be on permanent display at the KAIST Museum starting today. While currently open to students and the general public, the museum plans to expand its public accessibility through special exhibitions and educational programs starting in April.
2026.01.30 View 1623 -
AI Enters the Experienced Hire Era... Teaching Learned Knowledge with Ease
< (From left) KAIST Professor Hyunwoo J. Kim, Postdoctoral Researcher Sanghyeok Lee, M.S candidate Taehoon Song, Korea University Ph.D candidate Jihwan Park >
How inconvenient would it be if you had to manually transfer every contact and photo from scratch every time you switched to a new smartphone? Current Artificial Intelligence (AI) models face a similar predicament. Whenever a superior new AI model—such as a new version of ChatGPT—emerges, it has to be retrained with massive amounts of data and at a high cost to acquire specialized knowledge in specific fields. A Korean research team has developed a "knowledge transplantation" technology between AI models that can resolve this inefficiency.
KAIST announced on January 27th that a research team led by Professor Hyunwoo J. Kim from the School of Computing, in collaboration with a research team from Korea University, has developed a new technology capable of effectively "transplanting" learned knowledge between different AI models.
Recently, Vision-Language Models (VLM), which understand both images and text simultaneously, have been evolving rapidly. These are easily understood as multimodal AIs, like ChatGPT, which can provide explanations when a user shows them a photo and asks a question. These models have the advantage of adapting relatively quickly to new fields using small amounts of data by pre-learning large-scale image and language data.
However, the need to repeat this "adaptation process" from scratch every time a new AI model is released has been pointed out as a major inefficiency. Existing adaptation techniques also faced limitations: they were difficult to use if the model structure changed even slightly, or they significantly increased memory and computational costs because multiple models had to be used simultaneously.
To solve these problems, the research team proposed "TransMiter," a transferable adaptation technique that allows learned knowledge to be reused regardless of the model's structure or size. The core of this technology is directly transferring the "adaptation experience" accumulated by one AI as it learns to another AI model.
< TransMiter: A transferable adaptation technique reusable regardless of model structure, size, etc. >
The researchers' technology does not overhaul the complex internal structure of the AI; instead, it adopts a method of passing on "know-how" learned by observing only the prediction results (output) to another AI. Even if the AI models have different architectures, if the know-how learned by one AI is organized based on the answers given to the same questions, another AI can utilize that knowledge immediately. Consequently, there is no need to undergo the complex and time-consuming retraining process, and there is almost no slowdown in speed.
This study is highly significant as it is the first to prove that AI adaptation knowledge—previously considered almost impossible to reuse if model structures or sizes differed—can be precisely transplanted regardless of the model type. This is expected to not only reduce repetitive learning costs but also be utilized as a so-called "knowledge patch" technology that updates Large Language Models (LLMs) in real-time according to specific needs.
Professor Hyunwoo J. Kim explained, "By extending this research, we can significantly reduce the cost of post-training that had to be performed repeatedly whenever a rapidly evolving hyper-scale language model appears. It will enable 'model patches' that easily add expertise in specific fields."
The study involved Taehoon Song (Master's student, KAIST School of Computing), Sanghyeok Lee (Postdoctoral researcher), and Jihwan Park (Doctoral student, Korea University) as co-authors, with Professor Hyunwoo J. Kim serving as the corresponding author. The research results were accepted for oral presentation (4.6% acceptance rate as of 2025) at AAAI 2026 (Association for the Advancement of Artificial Intelligence), the most prestigious international conference in the field of AI, and were presented on January 25th.
Paper Title: Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization
DOI: https://doi.org/10.48550/arXiv.2508.08604
Meanwhile, Professor Hyunwoo J. Kim's laboratory presented a total of three papers at the conference, including this paper and "TabFlash," a technology developed in collaboration with Google Cloud AI to enhance the understanding of tables within documents.
2026.01.27 View 3610 -
KAIST’s Reliability-Aware AI Opens Path to Faster Cathode Design and Next-Generation Batteries
< (From front left) Professor Seungbum Hong, Professor EunAe Cho (From back left) Chaeyul Kang, Benediktus Madika, Jung Hyeon Moon, Taemin Park (Top) JooSung Shim >
The power that makes electric vehicles travel further and smartphones last longer comes from battery materials. Among them, the core material that directly determines the performance and lifespan of a battery is the cathode material. What if artificial intelligence could replace the numerous experiments required for battery material development? KAIST's research team has developed an artificial intelligence (AI) framework that presents both the particle size of cathode materials and prediction reliability even in situations where experimental data is insufficient, opening the possibility of expansion to next-generation energy technologies such as all-solid-state batteries.
KAIST announced on January 26th that a research team led by Professor Seungbum Hong of the Department of Materials Science and Engineering, in joint research with Professor EunAe Cho's team, has developed a machine learning framework that accurately predicts the particle size of battery cathode materials even when experimental data is incomplete and provides the degree of reliability of the results.
The cathode material inside the battery is the core material that allows lithium-ion batteries to store and use energy. Currently, the most widely used cathode material for electric vehicle batteries is an NCM-based metal oxide mixed with nickel (Ni), cobalt (Co), and manganese (Mn), which greatly affects the battery's lifespan, charging speed, driving range, and safety.
KAIST research team focused on the fact that the size of the very small primary particles that make up these cathode materials is a key factor in determining battery performance. This is because if the particles are too large, performance deteriorates, and conversely, if they are too small, stability problems may occur. Accordingly, the research team developed an AI-based technology that can accurately predict and control particle size.
< Battery performance prediction related (AI-generated image) >
In the past, to determine the particle size, numerous experiments had to be repeated while changing the sintering temperature, time, and material composition. However, in actual research fields, it was difficult to measure all conditions without omission, and experimental data were often missing, which limited the precise analysis of the relationship between process conditions and particle size.
To solve this problem, the research team designed an AI framework that supplements missing data and presents prediction results along with reliability. This framework is characterized by combining a technology (MatImpute) that supplements missing experimental data by considering chemical characteristics and a probabilistic machine learning model (NGBoost) that calculates prediction uncertainty.
This AI model does not stop at simply predicting particle size but also provides information on the extent to which the prediction can be trusted. This serves as an important criterion for deciding under what conditions to actually synthesize materials.
As a result of learning by expanding experimental data, the AI model showed a high prediction accuracy of about 86.6%. According to the analysis, it was found that the cathode material particle size is more significantly affected by process conditions such as baking temperature and time than by material components, which aligns well with existing experimental understanding.
To verify the reliability of the AI prediction, the research team conducted an experiment by newly producing four types of cathode material samples synthesized under manufacturing conditions not included in the existing data while maintaining the same metal component ratio of NCM811 (Ni 80% / Co 10% / Mn 10%) composition. As a result, the particle size predicted by the AI almost matched the actual microscopic measurement results, and most of the errors were 0.13 micrometers (μm) or less, which is much smaller than the thickness of a human hair. In particular, the actual experimental results were included within the prediction uncertainty range presented by the AI, confirming that not only the predicted value but also its reliability was valid.
< Distribution shift condition experiment verification using 4 types of samples >
This study is significant in that it has opened a way to find conditions with a high probability of success first without performing all experiments in battery research. Through this, it is expected to speed up the development of battery materials and significantly reduce unnecessary experiments and costs.
Professor Seungbum Hong said, "The key is that the AI presents not only the predicted value but also how much the result can be trusted," and added, "It will be of practical help in designing next-generation battery materials more quickly and efficiently."
In this study, Benediktus Madika, a doctoral student in the Department of Materials Science and Engineering, participated as the first author, and it was published on October 8, 2025, in 'Advanced Science', an internationally prestigious academic journal in the field of materials science and chemical engineering.
※ Paper Title: Uncertainty-Quantified Primary Particle Size Prediction in Li-Rich NCM Materials via Machine Learning and Chemistry-Aware Imputation, DOI: https://doi.org/10.1002/advs.202515694
Meanwhile, this research was conducted by researchers Benediktus Madika, Chaeyul Kang, JooSung Shim, Taemin Park, Jung Hyeon Moon, and the research team of Professor EunAe Cho and Professor Seungbum Hong, and was conducted with support from the Ministry of Science and ICT (MSIT) National Research Foundation of Korea (NRF) Future Convergence Technology Pioneer (Strategic) (Project No. RS-2023-00247245).
< Battery performance prediction (AI-generated image) >
2026.01.26 View 2829 -
KAIST Proposes a Multinational AI Cooperation Strategy Beyond U.S.–China Dominance
2026.01.19 View 3199 -
Playground for Future Quantum Technology: KAIST-MIT Quantum Information Winter School Successfully Concluded
< Group photo of the KAIST-MIT Quantum Information Winter School >
“Through the KAIST-MIT Quantum Information Winter School, I was able to view research from a broader perspective. The experience of collaborating with students from various universities and majors to complete a project was very refreshing,” said Jun-hyeong Cho, a student at the KAIST School of Electrical Engineering.
KAIST announced on the 16th that the Graduate School of Quantum Science and Technology successfully concluded the ‘KAIST-MIT Quantum Information Winter School,’ held jointly with the Massachusetts Institute of Technology (MIT) from January 5th to 16th at the KAIST main campus in Daejeon.
For this year’s Winter School, 50 junior and senior undergraduate students from Korea and abroad were selected to receive intensive training to grow into next-generation quantum talent. Eight world-renowned scholars from KAIST and MIT participated in the program, providing a multi-dimensional curriculum that spanned theory and practice—ranging from theoretical lectures and introductions to cutting-edge quantum experiments to visits to government-funded research institutes and student poster presentations.
Celebrating its third anniversary since its inception in 2024, the Winter School is now evaluated as a premier quantum information education program in Korea. Alongside KAIST faculty, world-class scholars from MIT participated directly in lectures and field training, operating an intensive curriculum that covered the entirety of quantum information science.
The lecturing faculty included world authorities in quantum computing, quantum devices, quantum machine learning, and quantum simulation, such as MIT professors Pablo Jarillo-Herrero, Seth Lloyd, Kevin P. O’Brien, and William D. Oliver, as well as KAIST scholars Jaewook Ahn, Joonwoo Bae, Gil-Young Cho, and Jae-yoon Choi.
Going beyond theoretical lectures, participants gained a broad understanding of research trends, technical limitations, and future development directions of state-of-the-art quantum technology through experimental training in core areas such as quantum computing, communication, sensing, and simulation.
< Scene from a Winter School lecture >
Furthermore, students visited the Korea Research Institute of Standards and Science (KRISS) and the Electronics and Telecommunications Research Institute (ETRI) to experience actual research sites, engaging in field-oriented education that bridges quantum theory and practice. The poster presentation session, where students shared their own research ideas, received enthusiastic responses as a forum for deep academic exchange, allowing students to receive direct feedback from MIT faculty.
Tae-hee Kim, a student from Pusan National University, remarked, “I was greatly inspired by the passion of the MIT faculty and the high level of questions from the students. It served as a motivation for me to pursue deeper studies independently.” Byung-jin Hwang, a student from Yonsei University, added, “I expected lectures from world-class scholars to be difficult, but I was impressed by the explanations tailored to the undergraduate level. The poster presentation session was particularly memorable.”
Eun-seong Kim, Dean of the KAIST Graduate School of Quantum Science and Technology, stated, “The KAIST-MIT Quantum Information Winter School is a special educational program where students can learn directly from world-renowned quantum researchers and experience cutting-edge research. We look forward to the active participation of future talents who will lead the quantum industry.”
Participants for this Winter School were selected through a document review process, and the program was operated entirely free of charge. KAIST covered all educational expenses and provided dormitory accommodations and lunch. Detailed information about the event can be found on the KAIST Graduate School of Quantum Science and Technology website (https://quantumschool.kaist.ac.kr/).
< Poster for the KAIST-MIT Quantum Information Winter School >
2026.01.16 View 4069 -
Chairman Jae-Chul Kim of Dongwon Group Donates a Total of 60.3 Billion Won to KAIST
<Jae-Chul Kim, Honorary Chairman of Dongwon Group>
"In the era of AI, a new future lies within the sea of data. I ask that KAIST leaps forward to become the world's No. 1 AI research group." — Jae-Chul Kim, Honorary Chairman of Dongwon Group
KAIST announced on January 16th that Honorary Chairman Jae-Chul Kim of Dongwon Group has pledged an additional 5.9 billion KRW in development funds to foster Artificial Intelligence (AI) talent and strengthen research infrastructure, bringing his total contribution to 60.3 billion KRW. This marks his second additional donation since 2020, continuing his steadfast support for strengthening South Korea's national competitiveness in the field of AI.
Through his initial donation in 2020, Chairman Kim established the 'Kim Jaechul Graduate School of AI' at KAIST, urging the university to secure world-class capabilities. Upon hearing that KAIST’s AI research level ranked 5th among global universities over the past five years (2020–2024), Chairman Kim requested that the university strive to reach the No. 1 spot in the world.
In response, President Kwang Hyung Lee explained, "To surpass Carnegie Mellon University (CMU), which is currently evaluated as the world’s best with an AI faculty of about 45, the KAIST Graduate School of AI needs to expand its faculty to over 50 and construct a dedicated research building." Chairman Kim responded by saying, "I will build the building," and this latest donation is a fulfillment of that promise.
This third pledge of 5.9 billion KRW was decided to cover the projected budget shortfall as the design of the AI Education and Research Building enters full-scale development.
The AI Education and Research Building will be a facility with 8 floors above ground and 1 basement level, covering a total floor area of 18,182 m² (approx. 5,500 pyeong). It is scheduled for completion in February 2028. Once finished, it will serve as a global AI research hub housing 50 faculty members and 1,000 students.
Since the 2021 academic year, KAIST has been selecting 60 Master’s and 10 Doctoral students annually as 'Dongwon Scholars' outside of the regular quota for a period of 10 years. While the tuition and research incentives for the first three years were supported by the donation, KAIST has been utilizing its own budget since the 2024 academic year to ensure students can focus entirely on their research.
Moving forward, the Kim Jaechul Graduate School of AI plans to build a world-class faculty and operate systematic Master's and Doctoral programs to cultivate global AI leaders. In addition to technical expertise, the school will offer educational programs focused on character and holistic development, leading the charge in strengthening Korea's AI competitiveness.
Honorary Chairman Jae-Chul Kim stated, "I hope this donation serves as a small 'priming water' for South Korea to leap forward as an AI powerhouse. I look forward to seeing global core talents grow here and contribute to our national strength."
President Kwang Hyung Lee expressed his gratitude, saying, "Chairman Kim’s unwavering support is the greatest driving force for KAIST to secure global AI sovereignty. We will ensure the Kim Jaechul Graduate School of AI becomes a mecca where the world's best AI minds gather to innovate, honoring the Chairman’s vision."
2026.01.16 View 3297 -
KAIST Proposes AI-Driven Strategy to Solve Long-Standing Mystery of Gene Function
<(From Left) Distinguisehd Professor Sang Yup Lee, Dr. Gi Bae Kim, Professor Bernhard O. Palsson>
“We know the genes, but not their functions.” To resolve this long-standing bottleneck in microbial research, a joint research team has proposed a cutting-edge research strategy that leverages Artificial Intelligence (AI) to drastically accelerate the discovery of microbial gene functions.
KAIST announced on January 12th that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Bernhard Palsson from the Department of Bioengineering at UCSD, has published a comprehensive review paper. The study systematically analyzes and organizes the latest AI-based research approaches aimed at revolutionizing the speed of gene function discovery.
Since the early 2000s, when whole-genome sequencing became a reality, there were high expectations that the genetic blueprint of life would be fully decoded. However, even twenty years later, the roles of a significant portion of genes within microbial genomes remain unknown.
While various experimental methods—such as gene deletion, analysis of gene expression profiles, and in vitro activity assays—have been employed, discovering gene functions remains a time-consuming and costly endeavor. This is primarily due to the limitations of large-scale experimentation, complex biological interactions, and the discrepancy between laboratory results and actual in vivo responses.
To overcome these hurdles, the research team emphasized that an AI-driven approach combining computational biology with experimental biology is essential.
In this paper, the team provides a comprehensive overview of computational biology approaches that have facilitated gene function discovery, ranging from traditional sequence similarity analysis to the latest deep-learning-based AI models.
Notably, 3D protein structure prediction technologies such as AlphaFold (developed by Google DeepMind) and RoseTTAFold (developed by the University of Washington) have opened new doors. These tools go beyond simple functional estimation, offering the potential to understand the underlying mechanisms of how gene functions operate. Furthermore, generative AI is now extending research boundaries toward designing proteins with specifically desired functions.
Focusing on transcription factors (proteins that act as genetic switches) and enzymes (proteins that catalyze chemical reactions), the team presented various application cases and future research directions that integrate gene sequence analysis, protein structure prediction, and diverse metagenomic analyses.
<Schematic illustration of computational biology methods for enzyme function prediction>
2026.01.12 View 3562 -
KAIST-Yonsei Team Identifies Origin Cells for Malignant Brain Tumor Common in Young Adults
<Dr. Jung Won Park, (Upper Right) Professor Jeong Ho Lee, Professor Seok-Gu Kang>
IDH-mutant glioma, caused by abnormalities in a specific gene (IDH), is the most common malignant brain tumor among young adults under the age of 50. It is a refractory brain cancer that is difficult to treat due to its high recurrence rate. Until now, treatment has focused primarily on removing the visible tumor mass. However, a Korean research team has discovered for the first time that normal brain cells acquire the initial IDH mutation and spread out through the cortex long before a visible tumor mass harboring additional cancer mutations forms, opening a new path for early diagnosis and treatment to suppress recurrence.
KAIST announced on January 9th that a joint research team led by Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering and Professor Seok-Gu Kang from the Department of Neurosurgery at Yonsei University Severance Hospital has identified that IDH-mutant gliomas originate from Glial Progenitor Cells (GPCs) present in normal brain tissue.
Glial Progenitor Cells (GPC): Cells that exist in the normal brain and can become the starting point for malignant brain tumors if genetic mutations occur.
Through precise analysis of tumor tissue obtained via extensive resection surgery and the surrounding normal cerebral cortex, the research team discovered that "cells of origin" harboring the IDH mutation already existed within brain tissue that appeared normal to the naked eye.
< Brain-Derived Refractory Brain Tumor Origin Cells (AI-Generated Image) >
This result proves for the first time that malignant brain tumors do not emerge suddenly at a specific point in time, but rather begin within a normal brain and progress slowly over a long period.
The research team then used "spatial transcriptomics"—a cutting-edge analysis technology that shows "which genes are operating where" simultaneously—to confirm that these origin cells with mutations were indeed Glial Progenitor Cells (GPCs) located in the cerebral cortex.
Furthermore, they successfully reproduced the process of brain tumor development in an animal model by introducing the same genetic "driver mutation" found in patients into the GPCs of mice.
This study is a significant expansion of previous research identifying the "origin" of IDH wildtype malignant brain tumors. In 2018, the joint research team led a paradigm shift in brain tumor research by revealing that IDH wildtype glioblastoma, a representative malignant brain tumor, originates not from the tumor body itself, but from neural stem cells in the subventricular zone—the source of new brain cells in the adult brain (Lee et al., Nature, 2018).
The current study clarifies that even though "IDH wildtype glioblastoma" and "IDH-mutant glioma" are both types of brain cancer, their starting cells and points of origin are entirely different, proving that different types of brain tumors have fundamentally different developmental processes.
< Mechanistic Elucidation of Malignant Brain Tumor Development Induced by IDH Mutations and Subsequent Genetic Alterations in Normal Cortical Glial Progenitor Cells >
Professor Seok-Gu Kang (Co-Corresponding Author) stated, "Brain tumors may not start exactly where the tumor mass is visible. A target approach focused on the origin cells and the site of origin according to the brain tumor subtype will serve as a crucial clue to changing the paradigm of early diagnosis and recurrence suppression treatment."
Based on these research results, Sovagen Co., Ltd, a faculty startup from KAIST, is developing an innovative RNA-based drug to suppress the evolution and recurrence of IDH-mutant malignant brain tumors. Additionally, Severance Hospital is pursuing the development of technologies to detect and control early mutant cells in refractory brain tumors through the Korea-US Innovative Result Creation R&D project.
Dr. Jung Won Park (Postdoctoral Researcher at KAIST Graduate School of Medical Science and Engineering), a neurosurgeon and the sole first author of the study, said, "This achievement was made possible by combining KAIST’s world-class basic science research capabilities with the clinical expertise of Yonsei Severance Hospital. The question I kept asking while treating patients—'Where does this tumor originate?'—was the starting point of this research."
The findings were published on January 8th in the world-renowned academic journal Science.
Paper Title: IDH-mutant gliomas arise from glial progenitor cells harboring the initial driver mutation
DOI: 10.1126/science.adt0559
Authors: Jung Won Park (KAIST, First Author), Seok-Gu Kang (Yonsei Severance Hospital, Corresponding Author), Jeong Ho Lee (KAIST, Sovagen, Corresponding Author)
This research was conducted with support from the Suh Kyung-bae Science Foundation, the National Research Foundation of Korea, the Ministry of Science and ICT, the Ministry of Health and Welfare, and the Korea Health Industry Development Institute (Physician-Scientist Training Program).
2026.01.09 View 2869 -
Direct Printing of Nanolasers, the Key to Optical Computing and Quantum Security
< (From left) Professor Ji Tae Kim (KAIST), Dr. Shiqi Hu (First Author, AI-based Intelligent Design-Manufacturing Integrated Research Group, KAIST-POSTECH), and Professor Junsuk Rho (POSTECH) >
In future high-tech industries, such as high-speed optical computing for massive AI, quantum cryptographic communication, and ultra-high-resolution augmented reality (AR) displays, nanolasers—which process information using light—are gaining significant attention as core components for next-generation semiconductors. A research team at our university has proposed a new manufacturing technology capable of high-density placement of nanolasers on semiconductor chips, which process information in spaces thinner than a human hair.
KAIST announced on January 6th that a joint research team, led by Professor Ji Tae Kim from the Department of Mechanical Engineering and Professor Junsuk Rho from POSTECH (President Seong-keun Kim), has developed an ultra-fine 3D printing technology capable of creating "vertical nanolasers," a key component for ultra-high-density optical integrated circuits.
Conventional semiconductor manufacturing methods, such as lithography, are effective for mass-producing identical structures but face limitations: the processes are complex and costly, making it difficult to freely change the shape or position of devices. Furthermore, most existing lasers are built as horizontal structures lying flat on a substrate, which consumes significant space and suffers from reduced efficiency due to light leakage into the substrate.
To solve these issues, the research team developed a new 3D printing method to vertically stack perovskite, a next-generation semiconductor material that generates light efficiently. This technology, known as "ultra-fine electrohydrodynamic 3D printing," uses electrical voltage to precisely control invisible ink droplets at the attoliter scale ($10^{-18}$ L).
Through this method, the team successfully printed pillar-shaped nanostructures—much thinner than a human hair—directly and vertically at desired locations without the need for complex subtractive processes (carving material away).
The core of this technology lies in significantly increasing laser efficiency by making the surface of the printed perovskite nanostructures extremely smooth. By combining the printing process with gas-phase crystallization control technology, the team achieved high-quality structures with nearly single-crystalline alignment. As a result, they were able to realize high-efficiency vertical nanolasers that operate stably with minimal light loss.
Additionally, the team demonstrated that the color of the emitted laser light could be precisely tuned by adjusting the height of the nanostructures. Utilizing this, they created laser security patterns invisible to the naked eye—identifiable only with specialized equipment—confirming the potential for commercialization in anti-counterfeiting technology.
< 3D Printing of Perovskite Nanolasers >
Professor Jitae Kim stated, "This technology allows for the direct, high-density implementation of optical computing semiconductors on a chip without complex processing. It will accelerate the commercialization of ultra-high-speed optical computing and next-generation security technologies."
The research results, with Dr. Shiqi Hu from the Department of Mechanical Engineering as the first author, were published online on December 6, 2025, in ACS Nano, an international prestigious journal in the field of nanoscience.
Paper Title: Nanoprinting with Crystal Engineering for Perovskite Lasers
DOI: https://doi.org/10.1021/acsnano.5c16906
This research was conducted with support from the Ministry of Science and ICT’s Excellent Young Researcher Program (RS-2025-00556379), the Mid-career Researcher Support Program (RS-2024-00356928), and the InnoCORE AI-based Intelligent Design-Manufacturing Integrated Research Group (N10250154).
2026.01.06 View 3782 -
President Kwang Hyung Lee, 2026 New Year Message
In his 2026 New Year’s Address, KAIST President Kwang Hyung Lee stated, “Based on the ‘QAIST-New Culture Strategy’ that encourages asking questions and taking on challenges, KAIST will accelerate AI-centered innovation in education and research to leap forward as a world-class university.”
President Lee highlighted the major achievements of the past year, including:
▲Educational innovation to nurture inquisitive minds ▲Advancement of education and research systems through the establishment of the College of AI ▲Establishment of a postdoc-centered research ecosystem through the InnoCORE Program, a national initiative to cultivate world-class AI talent ▲An approximately 20 percent increase in research funding ▲Expansion of research collaboration with global companies and universities
Particularly in the education sector, he explained that KAIST has fostered a culture where failure is viewed as a starting point for new challenges. This has been achieved through initiatives such as student-generated exam questions, the ‘Problem Definition to Solution Program (PDSP)’—where students define and solve problems on their own—and the operation of ‘KAIST Failure Week.’
He emphasized that these changes led to tangible results, with undergraduate early-admission applicants increasing by 1.9 times and graduate school applicants by 1.3 times over the past three years.
At the same time, efforts have been made to improve students' learning and living environments. KAIST has pursued the renovation and remodeling of all dormitories and enhanced student dining facilities and menu options to ensure students enjoy a more comfortable campus life. President Lee stressed, “The most precious members of KAIST are our students, and the university's role is to create an environment where they can freely ask questions and take on challenges.”
In the research field, KAIST secured billions of Korean won in annual research funding through joint initiatives with Germany’s Merck and Taiwan’s Formosa Group. It also established a strategic hub connecting to the global startup ecosystem through the KAIST–IDIS Silicon Valley Campus. Furthermore, 26 buildings have been newly constructed or expanded over the past five years, and 24 buildings are currently under construction or scheduled to break ground, ensuring the continuous expansion of education and research infrastructure.
In terms of technology commercialization, as of 2025, KAIST launched 59 deep-tech startups and completed technology transfers totaling KRW 8.2 billion. Notably, Sovargen, a faculty startup, successfully concluded a KRW 750 billion technology export deal.
President Lee presented the following key priorities for 2026: ▲Elevating the College of AI to a world-leading level ▲Advancing the Pyeongtaek, Osong, and Sejong campus projects ▲Expanding global partnerships ▲Ensuring the safe execution of 24 major construction projects
President Lee concluded by saying, “KAIST has now firmly established itself as a leading university representing the Republic of Korea, and its global recognition is rising rapidly through internationalization efforts. If we continue to work together in 2026, KAIST will stand proudly as a truly world-class university.”
2025.12.31 View 7659