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KAIST School of Transdisciplinary Studies Is Driving Innovation in Korean Education
<(From Left) Professor Jaeseung Jeong, haed of the School of Transdiciplinary Studies, Dr, Albert Chau, Vice President of Hong Kong Baptist University>
KAIST (President Kwang Hyung Lee) announced on the 24th of July that its School of Transdisciplinary Studies has been consistently showcasing the results of its experiments and practices for educational innovation both domestically and abroad.
On June 27, Professor Jaeseung Jeong, head of the School of Transdisciplinary Studies, was invited to speak at the “Pacific Asia Summit on Transdisciplinary Education 2025 (PASTE 2025)” held at Hong Kong Baptist University. He presented the Korean model of transdisciplinary education under the title “The Philosophy and Achievements of the KAIST School of Transdisciplinary Studies.”
In his talk, Professor Jeong pointed out the limitations of conventional education systems that rely on answer-centered evaluation, perfectionism, and competitiveness, claiming that they hinder creativity and integrative thinking. He then introduced the philosophy and operational practices of the School of Transdisciplinary Studies, which was established in 2019 to overcome these issues.
Professor Jeong outlined five key principles that define the school's educational philosophy: ①a broad and integrative academic foundation, ②student-driven and customized education, ③creativity and execution, ④a sense of social responsibility and global citizenship, and ⑤learning driven by intrinsic motivation and curiosity. He explained that students are admitted without a declared major, allowed to design their own learning plans, and evaluated under a P/NR system* that focuses on growth rather than competition.
*P/NR system: A non-competitive grading system led by KAIST’s School of Transdisciplinary Studies. Instead of traditional letter grades (A/B/C/Fail), students receive Pass (P) or No Record (NR), with the latter not appearing as a failure and not affecting GPA.
Professor Jeong emphasized, “This experiment at KAIST represents a new educational paradigm that values questions over knowledge, culture over structure, and inquiry over competition. Students are bridging academic learning and real-world practice by addressing societal challenges through technology, which could lead to a fundamental shift in global higher education.”
His presentation provided an opportunity to spotlight how KAIST’s experimental approach to nurturing transdisciplinary talent is pointing to new directions for the global education community beyond Korea.
< Hyungjoon Jang, a student at the School of Transdisciplinary Studies>
The achievements of KAIST’s transdisciplinary education model are also reflected in students’ academic accomplishments. Hyungjoon Jang, a student at the School of Transdisciplinary Studies, participated in a collaborative study led by his mentor, Professor Jaekyung Kim in the Department of Mathematical Sciences, along with researchers from Chungnam National University and the Institute for Basic Science (IBS). Their groundbreaking analytical method enables the accurate estimation of inhibition constants using only a single inhibitor concentration. The paper was published in the prestigious journal Nature Communications in June, with Jang listed as co–first author.
Jang played a leading role throughout the research process by developing the experimental methodology, creating a software package to support the method, drafting the manuscript, and engaging in peer review. He also effectively communicated mathematical and statistical models to pharmaceutical experts by mastering presentation techniques and visual explanation strategies, thereby setting a strong example of interdisciplinary collaboration.
He emphasized that “the School of Transdisciplinary Studies’ mentor system allowed regular research feedback and the systematic acquisition of essential knowledge and analytical skills through courses in biochemistry and computational neuroscience.”
This example demonstrates how undergraduate students at the School of Transdisciplinary Studies can take leading roles in cutting-edge interdisciplinary research.
The school’s educational philosophy is also reflected in students’ practical actions. Inseo Jeong, a current student and founder of the startup MPAge Inc., made a meaningful donation to help establish a creative makerspace in the school.
<Inseo Jeong, founder of MPAG>
Inseo Jeong explained that the decision was made to express gratitude for the knowledge gained and the mentorship received from professors, saying that at the School of Transdisciplinary Studies, she learned not only how to solve problems with technology but also how to view society, and that learning has helped her grow. She added, “The deep understanding of humanity and the world emphasized by Professor Jaeseung Jeong will be a great asset not only to entrepreneurs but to all students pursuing diverse paths,” expressing support for her fellow students.
Inseo Jeong collaborated for over two years with Professor Hyunwook Ka of the School of Transdisciplinary Studies on software research for individuals with hearing impairments. After numerous algorithm designs and experimental iterations, their work, which considered the social scalability of technology, was presented at the world-renowned CSUN Assistive Technology Conference held at California State University, Northridge. The project has filed for a patent under KAIST’s name.
※ Presentation title: Evidence-Based Adaptive Transcription for Sign Language Users
KAIST is now working to complete the makerspace on the third floor of the Administrative Annex (N2) in Room 314 with a size of approximately 33 m2 during the summer. The makerspace is expected to serve as a hands-on, integrative learning environment where various ideas can be realized and implemented, playing a key role in fostering students’ creative problem-solving and integrative thinking skills.
KAIST President Kwang Hyung Lee stated, “The School of Transdisciplinary Studies is both an experimental ground and a practical field for overcoming the limitations of traditional education and nurturing global talents with creative problem-solving skills and integrative thinking, which are essential for the future.” He added, “KAIST will continue to lead efforts to cultivate question-asking, inquiry-driven, transdisciplinary talents and propose new paradigms for education and research.”
2025.07.24 View 407 -
Why Do Plants Attack Themselves? The Secret of Genetic Conflict Revealed
<Professor Ji-Joon Song of the KAIST Department of Biological Sciences>
Plants, with their unique immune systems, sometimes launch 'autoimmune responses' by mistakenly identifying their own protein structures as pathogens. In particular, 'hybrid necrosis,' a phenomenon where descendant plants fail to grow healthily and perish after cross-breeding different varieties, has long been a difficult challenge for botanists and agricultural researchers. In response, an international research team has successfully elucidated the mechanism inducing plant autoimmune responses and proposed a novel strategy for cultivar improvement that can predict and avoid these reactions.
Professor Ji-Joon Song's research team at KAIST, in collaboration with teams from the National University of Singapore (NUS) and the University of Oxford, announced on the 21st of July that they have elucidated the structure and function of the 'DM3' protein complex, which triggers plant autoimmune responses, using cryo-electron microscopy (Cryo-EM) technology.
This research is drawing attention because it identifies defects in protein structure as the cause of hybrid necrosis, which occurs due to an abnormal reaction of immune receptors during cross-breeding between plant hybrids.
This protein (DM3) is originally an enzyme involved in the plant's immune response, but problems arise when the structure of the DM3 protein is damaged in a specific protein combination called 'DANGEROUS MIX (DM)'.
Notably, one variant of DM3, the 'DM3Col-0' variant, forms a stable complex with six proteins and is recognized as normal, thus not triggering an immune response. In contrast, another 'DM3Hh-0' variant has improper binding between its six proteins, causing the plant to recognize it as an 'abnormal state' and trigger an immune alarm, leading to autoimmunity.
The research team visualized this structure using atomic-resolution cryo-electron microscopy (Cryo-EM) and revealed that the immune-inducing ability is not due to the enzymatic function of the DM3 protein, but rather to 'differences in protein binding affinity.'
<Figure 1. Mechanism of Plant Autoimmunity Triggered by the Collapse of the DM3 Protein Complex>
This demonstrates that plants can initiate an immune response by recognizing not only 'external pathogens' but also 'internal protein structures' when they undergo abnormal changes, treating them as if they were pathogens.
The study shows how sensitively the plant immune system changes and triggers autoimmune responses when genes are mixed and protein structures change during the cross-breeding of different plant varieties. It significantly advanced the understanding of genetic incompatibility that can occur during natural cross-breeding and cultivar improvement processes.
Dr. Gijeong Kim, the co-first author, stated, "Through international research collaboration, we presented a new perspective on understanding the plant immune system by leveraging the autoimmune phenomenon, completing a high-quality study that encompasses structural biochemistry, genetics, and cell biological experiments."
Professor Ji-Joon Song of the KAIST Department of Biological Sciences, who led the research, said, "The fact that the immune system can detect not only external pathogens but also structural abnormalities in its own proteins will set a new standard for plant biotechnology and crop breeding strategies. Cryo-electron microscopy-based structural analysis will be an important tool for understanding the essence of gene interactions."
This research, with Professor Ji-Joon Song and Professor Eunyoung Chae of the University of Oxford as co-corresponding authors, Dr. Gijeong Kim (currently a postdoctoral researcher at the University of Zurich) and Dr. Wei-Lin Wan of the National University of Singapore as co-first authors, and Ph.D candidate Nayun Kim, as the second author, was published on July 17th in Molecular Cell, a sister journal of the international academic journal Cell.
This research was supported by the KAIST Grand Challenge 30 project.
Article Title: Structural determinants of DANGEROUS MIX 3, an alpha/beta hydrolase that triggers NLR-mediated genetic incompatibility in plants DOI: https://doi.org/10.1016/j.molcel.2025.06.021
2025.07.21 View 555 -
KAIST's Lim Mi-hee wins Korea's L'Oréal-UNESCO Women in Science Award
Lim Mi-hee, a professor at the Korea Advanced Institute of Science and Technology (KAIST) Department of Chemistry, received the Academic Promotion Award at the 24th Korean L'Oréal-UNESCO Women in Science Awards ceremony.
L'Oréal Korea, the Korean National Commission for UNESCO, and the Women’s Bioscience Forum held the 24th Korean L'Oréal-UNESCO Women in Science Awards ceremony on the 16th and noted that Lim Mi-hee was selected for this year’s Academic Promotion Award.
Professor Lim was recognized for her research on the causes of Alzheimer's disease at the molecular level and her efforts in the discovery of intracellular proteins that promote the toxicity of Alzheimer’s-inducing factors. Professor Lim is a full member of the Korean Academy of Science and Technology (KAST) and has received several awards including the Hanseong Science Award, this year's Women in Science and Technology Award, and the RIGAKU-ACCC Award (Asia's top woman scientist).
The fellowship section, awarded to four emerging women scientists, includes Kang Mi-kyung, an assistant professor at Korea University’s Department of Health and Environmental Sciences; Jeon Ji-hye, an assistant professor at Gyeongsang National University’s Department of Life Sciences; Jo Yu-na, a research professor at Pusan National University’s College of Medicine; and Lee Jeong-hyun, an assistant professor at Kongju National University’s Department of Environmental Education.
The recipients of the Academic Promotion Award and fellowships will receive a certificate and a trophy, along with research funding of 30 million won and 7 million won, respectively.
Samuel du Retail, the representative of L'Oréal Korea, said, “The L'Oréal Group continues to support the empowerment of women scientists and the improvement of research environments worldwide under the philosophy that 'the world needs science, and science needs women.' We will actively support more female talents to shine at the center of scientific and technological advancement in the future.”
2025.07.18 View 333 -
KAIST Develops Robots That React to Danger Like Humans
<(From left) Ph.D candidate See-On Park, Professor Jongwon Lee, and Professor Shinhyun Choi>
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
KAIST (President Kwang Hyung Lee) announced on July15th that a joint research team led by Endowed Chair Professor Shinhyun Choi of the School of Electrical Engineering at KAIST and Professor Jongwon Lee of the Department of Semiconductor Convergence at Chungnam National University (President Jung Kyum Kim) developed a next-generation neuromorphic semiconductor-based artificial sensory nervous system. This system mimics the functions of a living organism's sensory nervous system, and enables a new type of robotic system that can efficiently responds to external stimuli.
In nature, animals — including humans — ignore safe or familiar stimuli and selectively react sensitively to important or dangerous ones. This selective response helps prevent unnecessary energy consumption while maintaining rapid awareness of critical signals. For instance, the sound of an air conditioner or the feel of clothing against the skin soon become familiar and are disregarded. However, if someone calls your name or a sharp object touches your skin, a rapid focus and response occur. These behaviors are regulated by the 'habituation' and 'sensitization' functions in the sensory nervous system. Attempts have been consistently made to apply these sensory nervous system functions of living organisms in order to create robots that efficiently respond to external environments like humans.
However, implementing complex neural characteristics such as habituation and sensitization in robots has faced difficulties in miniaturization and energy efficiency due to the need for separate software or complex circuitry. In particular, there have been attempts to utilize memristors, a neuromorphic semiconductor. A memristor is a next-generation electrical device, which has been widely utilized as an artificial synapse due to its ability to store analog value in the form of device resistance. However, existing memristors had limitations in mimicking the complex characteristics of the nervous system because they only allowed simple monotonic changes in conductivity.
To overcome these limitations, the research team developed a new memristor capable of reproducing complex neural response patterns such as habituation and sensitization within a single device. By introducing additional layer inside the memristor that alter conductivity in opposite directions, the device can more realistically emulate the dynamic synaptic behaviors of a real nervous system — for example, decreasing its response to repeated safe stimuli but quickly regaining sensitivity when a danger signal is detected.
<New memristor mimicking functions of sensory nervous system such as habituation/sensitization>
Using this new memristor, the research team built an artificial sensory nervous system capable of recognizing touch and pain, an applied it to a robotic hand to test its performance. When safe tactile stimuli were repeatedly applied, the robot hand, which initially reacted sensitively to unfamiliar tactile stimuli, gradually showed habituation characteristics by ignoring the stimuli. Later, when stimuli were applied along with an electric shock, it recognized this as a danger signal and showed sensitization characteristics by reacting sensitively again. Through this, it was experimentally proven that robots can efficiently respond to stimuli like humans without separate complex software or processors, verifying the possibility of developing energy-efficient neuro-inspired robots.
<Robot arm with memristor-based artificial sensory nervous system>
See-On Park, researcher at KAIST, stated, "By mimicking the human sensory nervous system with next-generation semiconductors, we have opened up the possibility of implementing a new concept of robots that are smarter and more energy-efficient in responding to external environments." He added, "This technology is expected to be utilized in various fusion fields of next-generation semiconductors and robotics, such as ultra-small robots, military robots, and medical robots like robotic prosthetics".
This research was published online on July 1st in the international journal 'Nature Communications,' with Ph.D candidate See-On Park as the first author.
Paper Title: Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics
DOI: https://doi.org/10.1038/s41467-025-60818-x
This research was supported by the Korea National Research Foundation's Next-Generation Intelligent Semiconductor Technology Development Project, the Mid-Career Researcher Program, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the Excellent New Researcher Program, and the Nano Convergence Technology Division, National Nanofab Center's (NNFC) Nano-Medical Device Project.
2025.07.16 View 762 -
A KAIST Team Engineers a Microbial Platform for Efficient Lutein Production
<(From Left) Ph.D. Candidate Hyunmin Eun, Distinguished Professor Sang Yup Lee, , Dr. Cindy Pricilia Surya Prabowo>
The application of systems metabolic engineering strategies, along with the construction of an electron channeling system, has enabled the first gram-per-liter scale production of lutein from Corynebacterium glutamicum, providing a viable alternative to plant-derived lutein production.
A research group at KAIST has successfully engineered a microbial strain capable of producing lutein at industrially relevant levels. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, developed a novel C. glutamicum strain using systems metabolic engineering strategies to overcome the limitations of previous microbial lutein production efforts. This research is expected to be beneficial for the efficient production of other industrially important natural products used in food, pharmaceuticals, and cosmetics.
Lutein is a xanthophyll carotenoid found in egg yolk, fruits, and vegetables, known for its role in protecting our eyes from oxidative stress and reducing the risk of macular degeneration and cataracts. Currently, commercial lutein is predominantly extracted from marigold flowers; however, this approach has several drawbacks, including long cultivation times, high labor costs, and inefficient extraction yields, making it economically unfeasible for large-scale production. These challenges have driven the demand for alternative production methods.
To address these issues, KAIST researchers, including Ph.D. Candidate Hyunmin Eun, Dr. Cindy Pricilia Surya Prabowo, and Distinguished Professor Sang Yup Lee, applied systems metabolic engineering strategies to engineer C. glutamicum, a GRAS (Generally Recognized As Safe) microorganism widely used in industrial fermentation. Unlike Escherichia coli, which was previously explored for microbial lutein production, C. glutamicum lacks endotoxins, making it a safer and more viable option for food and pharmaceutical applications.
The team’s work, entitled “Gram-per-litre scale production of lutein by engineered Corynebacterium,” was published in Nature Synthesis on 04 July , 2025.
This research details the high-level production of lutein using glucose as a renewable carbon source via systems metabolic engineering. The team focused on eliminating metabolic bottlenecks that previously limited microbial lutein synthesis. By employing enzyme scaffold-based electron channeling strategies, the researchers improved metabolic flux towards lutein biosynthesis while minimizing unwanted byproducts.
<Lutein production metabolic pathway engineering>
To enhance productivity, bottleneck enzymes within the metabolic pathway were identified and optimized. It was determined that electron-requiring cytochrome P450 enzymes played a major role in limiting lutein biosynthesis. To overcome this limitation, an electron channeling strategy was implemented, where engineered cytochrome P450 enzymes and their reductase partners were spatially organized on synthetic scaffolds, allowing more efficient electron transfer and significantly increasing lutein production.
The engineered C. glutamicum strain was further optimized in fed-batch fermentation, achieving a record-breaking 1.78 g/L of lutein production within 54 hours, with a content of 19.51 mg/gDCW and a productivity of 32.88 mg/L/h—the highest lutein production performance in any host reported to date. This milestone demonstrates the feasibility of replacing plant-based lutein extraction with microbial fermentation technology.
“We can anticipate that this microbial cell factory-based mass production of lutein will be able to replace the current plant extraction-based process,” said Ph.D. Candidate Hyunmin Eun. He emphasized that the integrated metabolic engineering strategies developed in this study could be broadly applied for the efficient production of other valuable natural products used in pharmaceuticals and nutraceuticals.
<Schematic diagram of microbial-based lutein production platform>
“As maintaining good health in an aging society becomes increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other medically and nutritionally significant natural products,” added Distinguished Professor Sang Yup Lee.
This work is supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project 2022M3J5A1056072 and the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries project 2022M3J5A1056117 from the National Research Foundation supported by the Korean Ministry of Science and ICT.
Source:
Hyunmin Eun (1st), Cindy Pricilia Surya Prabowo (co-1st), and Sang Yup Lee (Corresponding). “Gram-per-litre scale production of lutein by engineered Corynebacterium”. Nature Synthesis (Online published)
For further information:
Sang Yup Lee, Distinguished Professor of Chemical and Biomolecular Engineering, KAIST (leesy@kaist.ac.kr, Tel: +82-42-350-3930)
2025.07.14 View 1202 -
KAIST Ushers in Era of Predicting ‘Optimal Alloys’ Using AI, Without High-Temperature Experiments
<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi>
Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study draws attention as it proposes a new direction for future alloy development by predicting in advance how well alloy components will mix during melting, a long-standing challenge in the field.
KAIST (President Kwang Hyung Lee) announced on the 14th of July that Professor Seungbum Hong’s research team from the Department of Materials Science and Engineering, in international collaboration with Professor Chris Wolverton’s group at Northwestern University, has developed a high-accuracy machine learning model that predicts whether alloy components will remain stable during melting. This was achieved using formation energy data derived from Density Functional Theory (DFT)* calculations.
*Density Functional Theory (DFT): A computational quantum mechanical method used to investigate the electronic structure of many-body systems, especially atoms, molecules, and solids, based on electron density.
The research team combined formation energy values obtained via DFT with experimental melting reaction data to train a machine learning model on 4,536 binary compounds.
Among the various machine learning algorithms tested, the XGBoost-based classification model demonstrated the highest accuracy in predicting whether alloys would mix well, achieving a prediction accuracy of approximately 82.5%. The team also applied the Shapley value method* to analyze the key features of the model. One major finding was that sharp changes in the slope of the formation energy curve (referred to as “convex hull sharpness”) were the most significant factor. A steep slope indicates a composition with energetically favorable (i.e., stable) formation.
*Shapley value: An explainability method in AI used to determine how much each feature contributed to a prediction.
The most notable significance of this study is that it predicts alloy melting behavior without performing high-temperature experiments. This is especially useful for materials such as high-entropy alloys or ultra-heat-resistant alloys, which are difficult to handle experimentally. The approach could also be extended to the design of complex multi-component alloy systems in the future.
Furthermore, the physical indicators identified by the AI model showed high consistency with actual experimental results on how well alloys mix and remain stable. This suggests that the model could be broadly applied to the development of various metal materials and the prediction of structural stability.
Professor Seungbum Hong of KAIST stated, “This research demonstrates how data-driven predictive materials development is possible by integrating computational methods, experimental data, and machine learning—departing from the traditional experience-based alloy design.” He added, “In the future, by incorporating state-of-the-art AI techniques such as generative models and reinforcement learning, we could enter an era where completely new alloys are designed automatically.”
<Model performance and feature importance analysis for predicting melting congruency. (a) SHAP summary plot showing the impact of individual features on model predictions. (b) Confusion matrix illustrating the model’s classification performance. (c) Receiver operating characteristic (ROC) curve with an AUC (area under the curve) score of 0.87, indicating a strong classification performance.>
Ph.D. candidate Youngwoo Choi, from the Department of Materials Science and Engineering at KAIST, participated as the first author. The study was published in the May issue of APL Machine Learning, a prestigious journal in the field of machine learning published by the American Institute of Physics, and was selected as a “Featured Article.”
※ Paper title: Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy ※ DOI: 10.1063/5.0247514
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.14 View 503 -
KAIST Kicks Off the Expansion of its Creative Learning Building, a 50th Anniversary Donation Landmark
KAIST announced on July 10th that it held a groundbreaking ceremony on July 9th for the expansion of its Creative Learning Building. This project, which celebrates the university's 50th anniversary, will become a significant donation-funded landmark and marks the official start of its construction.
<(From left) President Kwang Hyung Lee, Former President Sung-Chul Shin>
The groundbreaking ceremony was attended by key donors who graced the occasion, including KAIST President Kwang Hyung Lee, former President Sung-Chul Shin, Alumni Association President Yoon-Tae Lee, as well as parents and faculty member.
The Creative Learning Building serves as a primary space where KAIST undergraduate and graduate students attend lectures, functioning as a central hub for a variety of classes and talks. It also houses student support departments, including the Student Affairs Office, establishing itself as a student-centric complex that integrates educational, counseling, and welfare functions.
This expansion is more than just an increase in educational facilities; it's being developed as a "donation landmark" embodying KAIST's identity and future vision. Designed with a focus on creative convergence education, this project aims to create a new educational hub that organically combines education, exchange, and welfare functions
The campaign included over 230 participants, including KAIST alumni Byung-gyu Chang, Chairman of Krafton, former Alumni Association President Ki-chul Cha, Dr. Kun-mo Chung (former Minister of Science and Technology), as well as faculty members, parents, and current students. They collectively raised 6.5 billion KRW in donations. The total cost for this expansion project is 9 billion KRW, encompassing a gross floor area of 3,222.92㎡ across five above-ground floors, with completion targeted for September 2026.
2025.07.10 View 566 -
KAIST Presents a Breakthrough in Overcoming Drug Resistance in Cancer – Hope for Treating Intractable Diseases like Diabetes
<(From the left) Prof. Hyun Uk Kim, Ph.D candiate Hae Deok Jung, Ph.D candidate Jina Lim, Prof.Yoosik Kim from the Department of Chemical and Biomolecular Engineering>
One of the biggest obstacles in cancer treatment is drug resistance in cancer cells. Conventional efforts have focused on identifying new drug targets to eliminate these resistant cells, but such approaches can often lead to even stronger resistance. Now, researchers at KAIST have developed a computational framework to predict key metabolic genes that can re-sensitize resistant cancer cells to treatment. This technique holds promise not only for a variety of cancer therapies but also for treating metabolic diseases such as diabetes.
On the 7th of July, KAIST (President Kwang Hyung Lee) announced that a research team led by Professors Hyun Uk Kim and Yoosik Kim from the Department of Chemical and Biomolecular Engineering had developed a computational framework that predicts metabolic gene targets to re-sensitize drug-resistant breast cancer cells. This was achieved using a metabolic network model capable of simulating human metabolism.
Focusing on metabolic alterations—key characteristics in the formation of drug resistance—the researchers developed a metabolism-based approach to identify gene targets that could enhance drug responsiveness by regulating the metabolism of drug-resistant breast cancer cells.
< Computational framework that can identify metabolic gene targets to revert the metabolic state of the drug-resistant cells to that of the drug-sensitive parental cells>
The team first constructed cell-specific metabolic network models by integrating proteomic data obtained from two different types of drug-resistant MCF7 breast cancer cell lines: one resistant to doxorubicin and the other to paclitaxel. They then performed gene knockout simulations* on all of the metabolic genes and analyzed the results.
*Gene knockout simulation: A computational method to predict changes in a biological network by virtually removing specific genes.
As a result, they discovered that suppressing certain genes could make previously resistant cancer cells responsive to anticancer drugs again. Specifically, they identified GOT1 as a target in doxorubicin-resistant cells, GPI in paclitaxel-resistant cells, and SLC1A5 as a common target for both drugs.
The predictions were experimentally validated by suppressing proteins encoded by these genes, which led to the re-sensitization of the drug-resistant cancer cells.
Furthermore, consistent re-sensitization effects were also observed when the same proteins were inhibited in other types of breast cancer cells that had developed resistance to the same drugs.
Professor Yoosik Kim remarked, “Cellular metabolism plays a crucial role in various intractable diseases including infectious and degenerative conditions. This new technology, which predicts metabolic regulation switches, can serve as a foundational tool not only for treating drug-resistant breast cancer but also for a wide range of diseases that currently lack effective therapies.”
Professor Hyun Uk Kim, who led the study, emphasized, “The significance of this research lies in our ability to accurately predict key metabolic genes that can make resistant cancer cells responsive to treatment again—using only computer simulations and minimal experimental data. This framework can be widely applied to discover new therapeutic targets in various cancers and metabolic diseases.”
The study, in which Ph.D. candidates JinA Lim and Hae Deok Jung from KAIST participated as co-first authors, was published online on June 25 in Proceedings of the National Academy of Sciences (PNAS), a leading multidisciplinary journal that covers top-tier research in life sciences, physics, engineering, and social sciences.
※ Title: Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets ※ DOI: https://doi.org/10.1073/pnas.2425384122 ※ Authors: JinA Lim (KAIST, co-first author), Hae Deok Jung (KAIST, co-first author), Han Suk Ryu (Seoul National University Hospital, corresponding author), Yoosik Kim (KAIST, corresponding author), Hyun Uk Kim (KAIST, corresponding author), and five others.
This research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute (ETRI).
2025.07.08 View 1216 -
KAIST researcher Se Jin Park develops 'SpeechSSM,' opening up possibilities for a 24-hour AI voice assistant.
<(From Left)Prof. Yong Man Ro and Ph.D. candidate Sejin Park>
Se Jin Park, a researcher from Professor Yong Man Ro’s team at KAIST, has announced 'SpeechSSM', a spoken language model capable of generating long-duration speech that sounds natural and remains consistent.
An efficient processing technique based on linear sequence modeling overcomes the limitations of existing spoken language models, enabling high-quality speech generation without time constraints.
It is expected to be widely used in podcasts, audiobooks, and voice assistants due to its ability to generate natural, long-duration speech like humans.
Recently, Spoken Language Models (SLMs) have been spotlighted as next-generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information. However, existing models showed significant limitations in generating long-duration content required for podcasts, audiobooks, and voice assistants. Now, KAIST researcher has succeeded in overcoming these limitations by developing 'SpeechSSM,' which enables consistent and natural speech generation without time constraints.
KAIST(President Kwang Hyung Lee) announced on the 3rd of July that Ph.D. candidate Sejin Park from Professor Yong Man Ro's research team in the School of Electrical Engineering has developed 'SpeechSSM,' a spoken. a spoken language model capable of generating long-duration speech.
This research is set to be presented as an oral paper at ICML (International Conference on Machine Learning) 2025, one of the top machine learning conferences, selected among approximately 1% of all submitted papers. This not only proves outstanding research ability but also serves as an opportunity to once again demonstrate KAIST's world-leading AI research capabilities.
A major advantage of Spoken Language Models (SLMs) is their ability to directly process speech without intermediate text conversion, leveraging the unique acoustic characteristics of human speakers, allowing for the rapid generation of high-quality speech even in large-scale models.
However, existing models faced difficulties in maintaining semantic and speaker consistency for long-duration speech due to increased 'speech token resolution' and memory consumption when capturing very detailed information by breaking down speech into fine fragments.
To solve this problem, Se Jin Park developed 'SpeechSSM,' a spoken language model using a Hybrid State-Space Model, designed to efficiently process and generate long speech sequences.
This model employs a 'hybrid structure' that alternately places 'attention layers' focusing on recent information and 'recurrent layers' that remember the overall narrative flow (long-term context). This allows the story to flow smoothly without losing coherence even when generating speech for a long time. Furthermore, memory usage and computational load do not increase sharply with input length, enabling stable and efficient learning and the generation of long-duration speech.
SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech.
Additionally, in the speech generation phase, it uses a 'Non-Autoregressive' audio synthesis model (SoundStorm), which rapidly generates multiple parts at once instead of slowly creating one character or one word at a time, enabling the fast generation of high-quality speech.
While existing models typically evaluated short speech models of about 10 seconds, Se Jin Park created new evaluation tasks for speech generation based on their self-built benchmark dataset, 'LibriSpeech-Long,' capable of generating up to 16 minutes of speech.
Compared to PPL (Perplexity), an existing speech model evaluation metric that only indicates grammatical correctness, she proposed new evaluation metrics such as 'SC-L (semantic coherence over time)' to assess content coherence over time, and 'N-MOS-T (naturalness mean opinion score over time)' to evaluate naturalness over time, enabling more effective and precise evaluation.
Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation. This contrasts sharply with existing models, which tended to easily lose their topic and exhibit repetition during long-duration generation.
PhD candidate Sejin Park explained, "Existing spoken language models had limitations in long-duration generation, so our goal was to develop a spoken language model capable of generating long-duration speech for actual human use." She added, "This research achievement is expected to greatly contribute to various types of voice content creation and voice AI fields like voice assistants, by maintaining consistent content in long contexts and responding more efficiently and quickly in real time than existing methods."
This research, with Se Jin Park as the first author, was conducted in collaboration with Google DeepMind and is scheduled to be presented as an oral presentation at ICML (International Conference on Machine Learning) 2025 on July 16th.
Paper Title: Long-Form Speech Generation with Spoken Language Models
DOI: 10.48550/arXiv.2412.18603
Ph.D. candidate Se Jin Park has demonstrated outstanding research capabilities as a member of Professor Yong Man Ro's MLLM (multimodal large language model) research team, through her work integrating vision, speech, and language. Her achievements include a spotlight paper presentation at 2024 CVPR (Computer Vision and Pattern Recognition) and an Outstanding Paper Award at 2024 ACL (Association for Computational Linguistics).
For more information, you can refer to the publication and accompanying demo: SpeechSSM Publications.
2025.07.04 View 1126 -
King Saud University and KAIST discussed Strategic AI Partnership
<From left> President Abdulla Al-Salman(King Saud University), President Kwang Hyung Lee(KAIST)
KAIST (President Kwang Hyung Lee) and King Saud University (President Abdulla Al-Salman) held a meeting on July 3 at the KAIST Campus in Seoul and agreed to pursue strategic cooperation in AI and digital platform development. The global AI landscape is increasingly polarized between closed models developed by the U.S. and China’s nationally focused technology ecosystems. In this context, many neutral countries have consistently called for an alternative third model that promotes both technological diversity and open access. President Lee has previously advocated for a "Tripartite Platform Strategy" (三分之計), proposing an international collaboration framework based on open-source principles to be free from binary digital power structures and foster cooperative coexistence.
This KAIST-KSU collaboration represents a step toward developing a new, inclusive AI model. The collaboration aims to establish an innovative multilateral framework, especially within the MENA, Japan, Korea, and Southeast Asia, by building an open-source-based AI alliance. Both institutions bring complementary strengths to the table. Saudi Arabia possesses large-scale capital and digital infrastructure, while Korea leads in core AI and semiconductor technologies, applied research, and talent cultivation.
Together, the two nations aim to establish a sustainable collaboration model that creates a virtuous cycle of investment, technology, and talent. This initiative is expected to contribute to the development of an open AI platform and promote diversity in the global AI ecosystem.
During the meeting, the two sides discussed key areas of future cooperation, including:
· Joint development of open-source AI technologies and digital platforms
· Launch of a KAIST-KSU dual graduate degree program
· Expansion of exchange programs for students, faculty, and researchers
· Collaborative research in basic science and STEM disciplines
In particular, the two institutions discussed to establish a joint AI research center to co-develop open AI models and explore practical industrial applications. The goal is to broaden access to AI technology and create an inclusive innovation environment for more countries and institutions.
President Abdulla Al-Salman stated, "Under Saudi Vision 2030, we are driving innovation in science and technology through new leadership, openness, and strategic investment. This partnership with KAIST will serve as a critical foundation for building a competitive AI ecosystem in the Middle East."
President Kwang Hyung Lee emphasized, "By combining Saudi Arabia's leadership, market, and investment capacity with KAIST's technological innovation and the rich talent pools from both countries, we will significantly contribute to diversifying the global AI ecosystem."
Both leaders further noted, "Through joint research leading to an independent AI model, our two institutions could establish a new axis beyond the existing US-China digital order—realizing a 'Tripartite AI Strategy' that will propel us into global markets extending far beyond the MENA and ASEAN regions."
KAIST and KSU plan to formalize this agreement by signing an MOU in the near future, followed by concrete actions such as launching the joint research institute and global talent development programs. This collaboration was initiated under the Korea Foundation’s Distinguished Guests Invitation Program, overseen by the Ministry of Foreign Affairs, and is expected to grow into a long-term strategic partnership with continued support from KF.
About King Saud University (KSU)
Founded in 1957, KSU is Saudi Arabia’s first and leading national university. As a top research-oriented institution in the Middle East, it has achieved international recognition in fields such as AI, energy, and biotechnology. It plays a central role in nurturing talent and driving innovation aligned with Saudi Arabia’s Vision 2030, and is expanding global partnerships to further strengthen its research capabilities.
About the Korea Foundation (KF)
Established in 1991 under the Ministry of Foreign Affairs, the Korea Foundation is a public diplomacy institution dedicated to strengthening international understanding and friendship with Korea. KF plays a key role in expanding Korea’s soft power through academic and cultural exchange, people-to-people networks, and global Korean studies programs. Its Distinguished Guests Invitation Program fosters strategic partnerships with global leaders in government, academia, and industry.
2025.07.04 View 1064 -
KAIST Uses AI to Discover Optimal New Material for Removing Radioactive Iodine Contamination
<(From the Right) Professor Ho Jin Ryu, Department of Nuclear and Quantum Engineering, Dr. Sujeong Lee, a graduate of the KAIST Department of Materials Science and Engineering, and Dr. Juhwan Noh of KRICT’s Digital Chemistry Research Center>
Managing radioactive waste is one of the core challenges in the use of nuclear energy. In particular, radioactive iodine poses serious environmental and health risks due to its long half-life (15.7 million years in the case of I-129), high mobility, and toxicity to living organisms. A Korean research team has successfully used artificial intelligence to discover a new material that can remove iodine for nuclear environmental remediation. The team plans to push forward with commercialization through various industry-academia collaborations, from iodine-adsorbing powders to contaminated water treatment filters.
KAIST (President Kwang Hyung Lee) announced on the 2of July that Professor Ho Jin Ryu's research team from the Department of Nuclear and Quantum Engineering, in collaboration with Dr. Juhwan Noh of the Digital Chemistry Research Center at the Korea Research Institute of Chemical Technology (KRICT, President Young Kook Lee), which operates under the National Research Council of Science & Technology (NST, Chairman Youngsik Kim), developed a technique using AI to discover new materials that effectively remove radioactive iodine contaminants.
Recent studies show that radioactive iodine primarily exists in aqueous environments in the form of iodate (IO₃⁻). However, existing silver-based adsorbents have weak chemical adsorption strength for iodate, making them inefficient. Therefore, it is imperative to develop new adsorbent materials that can effectively remove iodate.
Professor Ho Jin Ryu’s team used a machine learning-based experimental strategy to identify optimal iodate adsorbents among compounds called Layered Double Hydroxides (LDHs), which contain various metal elements.
The multi-metal LDH developed in this study – Cu₃(CrFeAl), based on copper, chromium, iron, and aluminum—showed exceptional adsorption performance, removing over 90% of iodate. This achievement was made possible by efficiently exploring a vast compositional space using AI-driven active learning, which would be difficult to search through conventional trial-and-error experiments.
<Picture2. Concept of Developed AI-Based Technology for Exploring New Materials for Radioactive Contamination Removal>
The research team focused on the fact that LDHs, like high-entropy materials, can incorporate a wide range of metal compositions and possess structures favorable for anion adsorption. However, due to the overwhelming number of possible metal combinations in multi-metal LDHs, identifying the optimal composition through traditional experimental methods has been nearly impossible.
To overcome this, the team employed AI (machine learning). Starting with experimental data from 24 binary and 96 ternary LDH compositions, they expanded their search to include quaternary and quinary candidates. As a result, they were able to discover the optimal material for iodate removal by testing only 16% of the total candidate materials.
Professor Ho Jin Ryu stated, “This study shows the potential of using artificial intelligence to efficiently identify radioactive decontamination materials from a vast pool of new material candidates, which is expected to accelerate research for developing new materials for nuclear environmental cleanup.”
The research team has filed a domestic patent application for the developed powder technology and is currently proceeding with an international patent application. They plan to enhance the material’s performance under various conditions and pursue commercialization through industry-academia cooperation in the development of filters for treating contaminated water.
Dr. Sujeong Lee, a graduate of the KAIST Department of Materials Science and Engineering, and Dr. Juhwan Noh of KRICT’s Digital Chemistry Research Center, participated as the co-first authors of the study. The results were published online on May 26 in the internationally renowned environmental publication Journal of Hazardous Materials.
※ Paper title: Discovery of multi-metal-layered double hydroxides for decontamination of iodate by machine learning-assisted experiments ※ DOI: https://doi.org/10.1016/j.jhazmat.2025.138735
This research was supported by the Nuclear Energy Research Infrastructure Program and the Nano-Materials Technology Development Program funded by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.03 View 1387 -
KAIST Develops AI to Easily Find Promising Materials That Capture Only CO₂
< Photo 1. (From left) Professor Jihan Kim, Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of the Department of Chemical and Biomolecular Engineering >
In order to help prevent the climate crisis, actively reducing already-emitted CO₂ is essential. Accordingly, direct air capture (DAC) — a technology that directly extracts only CO₂ from the air — is gaining attention. However, effectively capturing pure CO₂ is not easy due to water vapor (H₂O) present in the air. KAIST researchers have successfully used AI-driven machine learning techniques to identify the most promising CO₂-capturing materials among metal-organic frameworks (MOFs), a key class of materials studied for this technology.
KAIST (President Kwang Hyung Lee) announced on the 29th of June that a research team led by Professor Jihan Kim from the Department of Chemical and Biomolecular Engineering, in collaboration with a team at Imperial College London, has developed a machine-learning-based simulation method that can quickly and accurately screen MOFs best suited for atmospheric CO₂ capture.
< Figure 1. Concept diagram of Direct Air Capture (DAC) technology and carbon capture using Metal-Organic Frameworks (MOFs). MOFs are promising porous materials capable of capturing carbon dioxide from the atmosphere, drawing attention as a core material for DAC technology. >
To overcome the difficulty of discovering high-performance materials due to the complexity of structures and the limitations of predicting intermolecular interactions, the research team developed a machine learning force field (MLFF) capable of precisely predicting the interactions between CO₂, water (H₂O), and MOFs. This new method enables calculations of MOF adsorption properties with quantum-mechanics-level accuracy at vastly faster speeds than before.
Using this system, the team screened over 8,000 experimentally synthesized MOF structures, identifying more than 100 promising candidates for CO₂ capture. Notably, this included new candidates that had not been uncovered by traditional force-field-based simulations. The team also analyzed the relationships between MOF chemical structure and adsorption performance, proposing seven key chemical features that will help in designing new materials for DAC.
< Figure 2. Concept diagram of adsorption simulation using Machine Learning Force Field (MLFF). The developed MLFF is applicable to various MOF structures and allows for precise calculation of adsorption properties by predicting interaction energies during repetitive Widom insertion simulations. It is characterized by simultaneously achieving high accuracy and low computational cost compared to conventional classical force fields. >
This research is recognized as a significant advance in the DAC field, greatly enhancing materials design and simulation by precisely predicting MOF-CO₂ and MOF-H₂O interactions.
The results of this research, with Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of KAIST as co-first authors, were published in the international academic journal Matter on June 12.
※Paper Title: Accelerating CO₂ direct air capture screening for metal–organic frameworks with a transferable machine learning force field
※DOI: 10.1016/j.matt.2025.102203
This research was supported by the Saudi Aramco-KAIST CO₂ Management Center and the Ministry of Science and ICT's Global C.L.E.A.N. Project.
2025.06.29 View 1496