AR
-
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 86 -
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 67 -
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 189 -
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 424 -
KAIST Invites World-Renowned Scholars, Elevating Global Competitiveness
< Photo 1. (From left) Professor John Rogers, Professor Gregg Rothermel, Dr. Sang H. Choi >
KAIST announced on June 27th that it has appointed three world-renowned scholars, including Professor John A. Rogers of Northwestern University, USA, as Invited Distinguished Professors in key departments such as Materials Science and Engineering.
Professor John A. Rogers (Northwestern University, USA) will be working with the Department of Materials Science and Engineering from July 2025 to June 2028 with Professor Gregg Rothermel (North Carolina State University, USA) working with the School of Computing from August 2025 to July 2026, and Dr. Sang H. Choi (NASA Langley Research Center, USA) with the Department of Aerospace Engineering from May 2025 to April 2028.
Professor John A. Rogers, a person of global authority in the field of bio-integrated electronics, has been leading advanced convergence technologies such as flexible electronics, smart skin, and implantable sensors. His significant impact on academia and industry is evident through over 900 papers published in top-tier academic journals like Science, Nature, and Cell, and he comes in an H-index of 240*. His research group, the Rogers Research Group at Northwestern University, focuses on "Science that brings Solutions to Society," encompassing areas such as bio-integrated microsystems and unconventional nanofabrication techniques. He is the founding Director of the Querrey-Simpson Institute of Bioelectronics at Northwestern University.
* H-index 240: An H-index is a measurement used to assess the research productivity and impact of an individual authors. H-index 240 means that 240 or more papers have been cited at least 240 times each, indicating a significant impact and the presumable status as a world-class scholar.
The Department of Materials Science and Engineering plans to further enhance its research capabilities in next-generation bio-implantable materials and wearable devices and boost its global competitiveness through the invitation of Professor Rogers. In particular, it aims to create strong research synergies by linking with the development of bio-convergence interface materials, a core task of the Leading Research Center (ERC, total research budget of 13.5 billion KRW over 7 years) led by Professor Kun-Jae Lee.
Professor Gregg Rothermel, a world-renowned scholar in software engineering, was ranked second among the top 50 global researchers by Communications of the ACM. For over 30 years, he has conducted practical research to improve software reliability and quality. He has achieved influential research outcomes through collaborations with global companies such as Boeing, Microsoft, and Lockheed Martin. Dr. Rothermel's research at North Carolina State University focuses on software engineering and program analysis, with significant contributions through initiatives like the ESQuaReD Laboratory and the Software-Artifact Infrastructure Repository (SIR).
The School of Computing plans to strengthen its research capabilities in software engineering and conduct collaborative research on software design and testing to enhance the reliability and safety of AI-based software systems through the invitation of Professor Gregg Rothermel. In particular, he is expected to participate in the Big Data Edge-Cloud Service Research Center (ITRC, total research budget of 6.7 billion KRW over 8 years) led by Professor In-Young Ko of the School of Computing, and the Research on Improving Complex Mobility Safety (SafetyOps, Digital Columbus Project, total research budget of 3.5 billion KRW over 8 years), contributing to resolving uncertainties in machine learning-based AI software and advancing technology.
Dr. Sang H. Choi, a global expert in space exploration and energy harvesting, has worked at NASA Langley Research Center for over 40 years, authoring over 200 papers and reports, holding 45 patents, and receiving 71 awards from NASA. In 2022, he was inducted into the 'Inventors Hall of Fame' as part of NASA's Technology Transfer Program. This is a rare honor, recognizing researchers who have contributed to the private sector dissemination of space exploration technology, with only 35 individuals worldwide selected to date. Dr. Choi's extensive work at NASA includes research on advanced electronic and energetic materials, satellite sensors, and various nano-technologies.
Dr. Choi plans to collaborate with Associate Professor Hyun-Jung Kim (former NASA Research Scientist, 2009-2024), who joined the Department of Aerospace Engineering in September of 2024, to lead the development of core technologies for lunar exploration (energy sources, sensing, in-situ resource utilization ISRU).
KAIST President Kwang Hyung Lee stated, "It is very meaningful to be able to invite these world-class scholars. Through these appointments, KAIST will further strengthen its global competitiveness in research in the fields of advanced convergence technology such as bio-convergence electronics, AI software engineering, and space exploration, securing our position as the leader of global innovations."
2025.06.27 View 700 -
New and Highly Efficient Recycling Technology to Turn Used Tires into Raw Materials for Rubber and Nylon
< (From left) Kyungmin Choi (MS-Ph.D. integrated course, Department of Chemistry), Dr. Beomsoon Park, Professor Soon Hyeok Hong, Dr. Kyoungil Cho >
Approximately 1.5 billions of tires are discarded globally every year, and this is identified as one of the major causes of serious environmental pollution. The research team at the Department of Chemistry at KAIST has achieved a breakthrough by selectively converting waste tires into high-purity cyclic alkenes, valuable chemical building blocks used in the production of rubber and nylon fibers. This advance marks a new milestone in chemical recycling technology for waste tires.
The team, led by Professor Soon Hyeok Hong, has developed a dual-catalyst-based reaction system that overcomes the long-standing challenges associated with recycling vulcanized rubber materials.
Tires are composed of complex blends of synthetic and natural rubber, and their physical strength and durability are reinforced with additives such as silica, carbon black, and antioxidants. In particular, cross-linking between rubber chains is formed through the vulcanization process, giving them a structure resistant to heat and pressure, which is one of the main reasons why chemical recycling of waste tires is difficult.
Until now, waste tire recycling has mainly relied on pyrolysis or mechanical recycling methods. The pyrolysis method is a technology that decomposes polymer chains at high temperatures of 350-800°C to convert them into fuel oil, but it clearly has limitations such as high energy consumption, low selectivity, and the production of low-quality hydrocarbon mixtures.
To solve these problems, the research team developed a method to convert waste rubber into useful chemicals using dual catalysis. The first catalyst helps to break down rubber molecules by changing their bonding structure, and the second catalyst creates cyclic compounds through a ring-closing reaction.
This process shows high selectivity of up to 92% and a yield of 82%. The produced cyclopentene can be recycled into rubber, and cyclohexene can be used as a raw material for nylon fibers, making them industrially very valuable.
The research team successfully applied the developed system to discarded waste tires, achieving selective conversion into high-purity cyclic alkenes. Unlike the existing pyrolysis method, this is evaluated as a new turning point in the field of waste tire recycling as it can produce high-value chemicals through low-temperature precision catalytic reactions.
In addition, this catalytic platform is compatible with a wide range of synthetic and waste rubbers, positioning it as a promising foundation for scalable, circular solutions in the polymer and materials industries.
< Figure 1. Development of a Catalytic Method for Chemical Recycling of Waste Rubber >
Professor Hong stated, "This research offers an innovative solution for the chemical recycling of waste tires. We aim to develop next-generation high-efficiency catalysts and lay the groundwork for commercialization to enhance economic feasibility. Ultimately, our goal is to contribute to solving the broader waste plastic problem through fundamental chemistry."
This research, in which Beomsoon Park, Kyoungil Cho, and Kyungmin Choi participated, was supported by the National Research Foundation of Korea and was published online in the internationally renowned academic journal ‘Chem’ on June 18th.
※Paper Title: Catalytic and Selective Chemical Recycling of Post-Consumer Rubbers into Cycloalkenes
※DOI: 10.1016/j.chempr.2025.102625
2025.06.26 View 944 -
KAIST to Lead the Way in Nurturing Talent and Driving S&T Innovation for a G3 AI Powerhouse
* Focusing on nurturing talent and dedicating to R&D to become a G3 AI powerhouse (Top 3 AI Nations).
* Leading the realization of an "AI-driven Basic Society for All" and developing technologies that leverage AI to overcome the crisis in Korea's manufacturing sector.
* 50 years ago, South Korea emerged as a scientific and technological powerhouse from the ashes, with KAIST at its core, contributing to the development of scientific and technological talent, innovative technology, national industrial growth, and the creation of a startup innovation ecosystem.
As public interest in AI and science and technology has significantly grown with the inauguration of the new government, KAIST (President Kwang Hyung Lee) announced its plan, on June 24th, to transform into an "AI-centric, Value-Creating Science and Technology University" that leads national innovation based on science and technology and spearheads solutions to global challenges.
At a time when South Korea is undergoing a major transition to a technology-driven society, KAIST, drawing on its half-century of experience as a "Starter Kit" for national development, is preparing to leap beyond being a mere educational and research institution to become a global innovation hub that creates new social value.
In particular, KAIST has presented a vision for realizing an "AI-driven Basic Society" where all citizens can utilize AI without alienation, enabling South Korea to ascend to the top three AI nations (G3). To achieve this, through the "National AI Research Hub" project (headed by Kee Eung Kim), led by KAIST representing South Korea, the institution is dedicated to enhancing industrial competitiveness and effectively solving social problems based on AI technology.
< KAIST President Kwang Hyung Lee >
KAIST's research achievements in the AI field are garnering international attention. In the top three machine learning conferences (ICML, NeurIPS, ICLR), KAIST ranked 5th globally and 1st in Asia over the past five years (2020-2024). During the same period, based on the number of papers published in top conferences in machine learning, natural language processing, and computer vision (ICML, NeurIPS, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV, ECCV), KAIST ranked 5th globally and 4th in Asia. Furthermore, KAIST has consistently demonstrated unparalleled research capabilities, ranking 1st globally in the average number of papers accepted at ISSCC (International Solid-State Circuits Conference), the world's most prestigious academic conference on semiconductor integrated circuits, for 19 years (2006-2024).
KAIST is continuously expanding its research into core AI technologies, including hyper-scale AI models (Korean LLM), neuromorphic semiconductors, and low-power AI processors, as well as various application areas such as autonomous driving, urban air mobility (UAM), precision medicine, and explainable AI (XAI).
In the manufacturing sector, KAIST's AI technologies are also driving on-site innovation. Professor Young Jae Jang's team has enhanced productivity in advanced manufacturing fields like semiconductors and displays through digital twins utilizing manufacturing site data and AI-based prediction technology. Professor Song Min Kim's team developed ultra-low power wireless tag technology capable of tracking locations with sub-centimeter precision, accelerating the implementation of smart factories. Technologies such as industrial process optimization and equipment failure prediction developed by INEEJI Co., Ltd., founded by Professor Jaesik Choi, are being rapidly applied in real industrial settings, yielding results. INEEJI was designated as a national strategic technology in the 'Explainable AI (XAI)' field by the government in March.
< Researchers performing data analysis for AI research >
Practical applications are also emerging in the robotics sector, which is closely linked to AI. Professor Jemin Hwangbo's team from the Department of Mechanical Engineering garnered attention by newly developing RAIBO 2, a quadrupedal robot usable in high-risk environments such as disaster relief and rough terrain exploration. Professor Kyoung Chul Kong's team and Angel Robotics Co., Ltd. developed the WalkOn Suit exoskeleton robot, significantly improving the quality of life for individuals with complete lower body paralysis or walking disabilities.
Additionally, remarkable research is ongoing in future core technology areas such as AI semiconductors, quantum cryptography communication, ultra-small satellites, hydrogen fuel cells, next-generation batteries, and biomimetic sensors. Notably, space exploration technology based on small satellites, asteroid exploration projects, energy harvesting, and high-speed charging technologies are gaining attention.
Particularly in advanced bio and life sciences, KAIST is collaborating with Germany's Merck company on various research initiatives, including synthetic biology and mRNA. KAIST is also contributing to the construction of a 430 billion won Merck Bio-Center in Daejeon, thereby stimulating the local economy and creating jobs.
Based on these cutting-edge research capabilities, KAIST continues to expand its influence not only within the industry but also on the global stage. It has established strategic partnerships with leading universities worldwide, including MIT, Stanford University, and New York University (NYU). Notably, KAIST and NYU have established a joint campus in New York to strengthen human exchange and collaborative research. Active industry-academia collaborations with global companies such as Google, Intel, and TSMC are also ongoing, playing a pivotal role in future technology development and the creation of an innovation ecosystem.
These activities also lead to a strong startup ecosystem that drives South Korean industries. The flow of startups, which began with companies like Qnix Computer, Nexon, and Naver, has expanded to a total of 1,914 companies to date. Their cumulative assets amount to 94 trillion won, with sales reaching 36 trillion won and employing approximately 60,000 people. Over 90% of these are technology-based startups originating from faculty and student labs, demonstrating a model that makes a tangible economic contribution based on science and technology.
< Students at work >
Having consistently generated diverse achievements, KAIST has already produced approximately 80,000 "KAISTians" who have created innovation through challenge and failure, and is currently recruiting new talent to continue driving innovation that transforms South Korea and the world.
President Kwang Hyung Lee emphasized, "KAIST will establish itself as a global leader in science and technology, designing the future of South Korea and humanity and creating tangible value." He added, "We will focus on talent nurturing and research and development to realize the new government's national agenda of becoming a G3 AI powerhouse."
He further stated, "KAIST's vision for the AI field, in which it places particular emphasis, is to strive for a society where everyone can freely utilize AI. We will contribute to significantly boosting productivity by recovering manufacturing competitiveness through AI and actively disseminating physical AI, AI robots, and AI mobility technologies to industrial sites."
2025.06.24 View 814 -
Distinguished Professor Sang Yup Lee Wins 2025 Global Metabolic Engineering Award
< Distinguished Professor Sang Yup Lee (Senior Vice President for Research) from the Department of Chemical & Biomolecular Engineering >
KAIST announced on the 20th that Professor Sang Yup Lee, who serves as the Vice President for Research and a Distinguished Professor at our university, has been awarded the '2025 Gregory N. Stephanopoulos Award for Metabolic Engineering' by the International Metabolic Engineering Society (IMES). Professor Lee delivered his award lecture at the 16th Metabolic Engineering Conference (ME16), held in Copenhagen, Denmark, from June 15th to 19th.
This award was established through contributions from the American Institute of Chemical Engineers (AIChE) Foundation, as well as fellow colleagues and acquaintances, to honor the achievements of Dr. Gregory Stephanopoulos, widely recognized as one of the pioneers of metabolic engineering. Presented biennially, the award recognizes scientists who have successfully commercialized fundamental research in metabolic engineering or have made outstanding contributions to the quantitative analysis, design, and modeling of metabolic pathways.
Professor Sang Yup Lee boasts an impressive record of over 770 journal papers and more than 860 patents. His groundbreaking research in metabolic engineering and biochemical engineering is highly acclaimed globally.
Throughout his 31 years as a professor at KAIST, Professor Lee has developed various metabolic engineering-based technologies and strategies. These advancements have been transferred to industries, facilitating the production of bulk chemicals, polymers, natural products, pharmaceuticals, and health functional foods. He has also founded companies and actively engages in advisory roles with various enterprises.
The International Metabolic Engineering Society (IMES) defines metabolic engineering as the manipulation of metabolic pathways in microorganisms or cells to produce useful substances (such as pharmaceuticals, biofuels, and chemical products). It utilizes tools like systems biology, synthetic biology, and computational modeling with the aim of enhancing the economic viability and sustainability of bio-based processes.
Furthermore, Professor Lee previously received the Merck Metabolic Engineering Award, a prominent international award in the field, in 2008. In 2018, he was honored with the Eni Award, often referred to as the Nobel Prize in energy, presented by the President of Italy.
Professor Sang Yup Lee remarked, "Metabolic engineering is a discipline that leads the current and future of biotechnology. It is a tremendous honor to receive this meaningful award at a time when the transition to a bio-based economy is accelerating. Together with my students and fellow researchers, we have generated numerous patents and transferred technologies to industry, and also established startups in the fields of biofuels, wound healing, and cosmetics. I will continue to pursue research that encompasses both fundamental research and technological commercialization."
The 'International Metabolic Engineering Society (IMES)' is a specialized society under the American Institute of Chemical Engineers. Its mission is to enable the production of various bio-based products, including pharmaceuticals, food additives, chemicals, and fuels, through metabolic engineering. The society hosts the Metabolic Engineering Conference biennially, offering researchers opportunities for knowledge exchange and collaboration.
2025.06.20 View 947 -
KAIST Develops Glare-Free, Heat-Blocking 'Smart Window'... Applicable to Buildings and Vehicles
• Professor Hong Chul Moon of the Department of Chemical and Biomolecular Engineering develops RECM, a next-generation smart window technology, expecting cooling energy savings and effective indoor thermal management.
• When using the developed RECM, a significantly superior temperature reduction effect is observed compared to conventional windows.
• With a 'pedestrian-friendly smart window' design that eliminates glare by suppressing external reflections, it is expected to be adapted in architectural structures, transportation, and more.
< (From left) First author Hoy Jung Jo, Professor Hong Chul Moon >
In the building sector, which accounts for approximately 40% of global energy consumption, heat ingress through windows has been identified as a primary cause of wasted heating and cooling energy. Our research team has successfully developed a 'pedestrian-friendly smart window' technology capable of not only reducing heating and cooling energy in urban buildings but also resolving the persistent issue of 'light pollution' in urban living.
On the 17th of June, Professor Hong Chul Moon's research team at KAIST's Department of Chemical and Biomolecular Engineering announced the development of a 'smart window technology' that allows users to control the light and heat entering through windows according to their intent, and effectively neutralize glare from external sources.
Recently, 'active smart window' technology, which enables free adjustment of light and heat based on user operation, has garnered significant attention. Unlike conventional windows that passively react to changes in temperature or light, this is a next-generation window system that can be controlled in real-time via electrical signals.
The next-generation smart window technology developed by the research team, RECM (Reversible Electrodeposition and Electrochromic Mirror), is a smart window system based on a single-structured *electrochromic device that can actively control the transmittance of visible light and near-infrared (heat).
*Electrochromic device: A device whose optical properties change in response to an electrical signal.
In particular, by effectively suppressing the glare phenomenon caused by external reflected light—a problem previously identified in traditional metal *deposition smart windows—through the combined application of electrochromic materials, a 'pedestrian-friendly smart window' suitable for building facades has been realized.
*Deposition: A process involving the electrochemical reaction to coat metal ions, such as Ag+, onto an electrode surface in solid form.
The RECM system developed in this study operates in three modes depending on voltage control.
Mode I (Transparent Mode) is advantageous for allowing sunlight to enter the indoor space during winter, as it transmits both light and heat like ordinary glass.
In Mode II (Colored Mode), *Prussian Blue (PB) and **DHV+• chemical species are formed through a redox (oxidation-reduction) reaction, causing the window to turn a deep blue color. In this state, light is absorbed, and only a portion of the heat is transmitted, allowing for privacy while enabling appropriate indoor temperature control.
*Prussian Blue: An electrochromic material that transitions between colorless and blue upon electrical stimulation.
**DHV+•: A radical state colored molecule generated upon electrical stimulation.
Mode III (Colored and Deposition Mode) involves the reduction and deposition of silver (Ag+) ions on the electrode surface, reflecting both light and heat. Concurrently, the colored material absorbs the reflected light, effectively blocking glare for external pedestrians.
The research team validated the practical indoor temperature reduction effect of the RECM technology through experiments utilizing a miniature model house. When a conventional glass window was installed, the indoor temperature rose to 58.7°C within 45 minutes. Conversely, when RECM was operated in Mode III, the temperature reached 31.5°C, demonstrating a temperature reduction effect of approximately 27.2°C.
Furthermore, since each state transition is achievable solely by electrical signals, it is regarded as an active smart technology capable of instantaneous response according to season, time, and intended use.
< Figure 1. Operation mechanism of the RECM smart window. The RECM system can switch among three states—transparent, colored, and colored & deposition—via electrical stimulation. At -1.6 V, DHV•+ and Prussian Blue (PB) are formed, blocking visible light to provide privacy protection and heat blocking. At -2.0 V, silver (Ag) is deposited on the electrode surface, reflecting light and heat, while DHV•+ and Prussian Blue absorb reflected light, effectively suppressing external glare. Through this mechanism, it functions as an active smart window that simultaneously controls light, heat, and glare. >
Professor Hong Chul Moon of KAIST, the corresponding author of this study, stated, "This research goes beyond existing smart window technologies limited to visible light control, presenting a truly smart window platform that comprehensively considers not only active indoor thermal control but also the visual safety of pedestrians." He added, "Various applications are anticipated, from urban buildings to vehicles and trains."
< Figure 2. Analysis of glare suppression effect of conventional reflective smart windows and RECM. This figure presents the results comparing the glare phenomenon occurring during silver (Ag) deposition between conventional reflective smart windows and RECM Mode III. Conventional reflective devices resulted in strong reflected light on the desk surface due to their high reflectivity. In contrast, RECM Mode III, where the colored material absorbed reflected light, showed a 33% reduction in reflected light intensity, and no reflected light was observed from outside. This highlights the RECM system's distinctiveness and practicality as a 'pedestrian-friendly smart window' optimized for dense urban environments, extending beyond just heat blocking. >
The findings of this research were published on June 13, 2025, in Volume 10, Issue 6 of 'ACS Energy Letters'. The listed authors for this publication are Hoy Jung Jo, Yeon Jae Jang, Hyeon-Don Kim, Kwang-Seop Kim, and Hong Chul Moon.
※ Paper Title: Glare-Free, Energy-Efficient Smart Windows: A Pedestrian-Friendly System with Dynamically Tunable Light and Heat Regulation
※ DOI: 10.1021/acsenergylett.5c00637
< Figure 3. Temperature reduction performance verification in a miniature model house. The actual heat blocking effect was evaluated by applying RECM devices to a model building. Under identical conditions, the indoor temperature with ordinary glass rose to 58.7°C, whereas with RECM in Mode III, it reached 31.5°C, demonstrating a maximum temperature reduction effect of 27.2°C. The indoor temperature difference was also visually confirmed through thermal images, which proves the potential for indoor temperature control in urban buildings. >
This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and the internal research program of the Korea Institute of Machinery and Materials.
2025.06.20 View 2456 -
Simultaneous Analysis of 21 Chemical Reactions... AI to Transform New Drug Development
< Photo 1. (From left) Professor Hyunwoo Kim and students Donghun Kim and Gyeongseon Choi in the Integrated M.S./Ph.D. program of the Department of Chemistry >
Thalidomide, a drug once used to alleviate morning sickness in pregnant women, exhibits distinct properties due to its optical isomers* in the body: one isomer has a sedative effect, while the other causes severe side effects like birth defects. As this example illustrates, precise organic synthesis techniques, which selectively synthesize only the desired optical isomer, are crucial in new drug development. Overcoming the traditional methods that struggled with simultaneously analyzing multiple reactants, our research team has developed the world's first technology to precisely analyze 21 types of reactants simultaneously. This breakthrough is expected to make a significant contribution to new drug development utilizing AI and robots.
*Optical Isomers: A pair of molecules with the same chemical formula that are mirror images of each other and cannot be superimposed due to their asymmetric structure. This is analogous to a left and right hand, which are similar in form but cannot be perfectly overlaid.
KAIST's Professor Hyunwoo Kim's research team in the Department of Chemistry announced on the 16th that they have developed an innovative optical isomer analysis technology suitable for the era of AI-driven autonomous synthesis*. This research is the world's first technology to precisely analyze asymmetric catalytic reactions involving multiple reactants simultaneously using high-resolution fluorine nuclear magnetic resonance spectroscopy (19F NMR). It is expected to make groundbreaking contributions to various fields, including new drug development and catalyst optimization.
*AI-driven Autonomous Synthesis: An advanced technology that automates and optimizes chemical substance synthesis processes using artificial intelligence (AI). It is gaining attention as a core element for realizing automated and intelligent research environments in future laboratories. AI predicts and adjusts experimental conditions, interprets results, and designs subsequent experiments independently, minimizing human intervention in repetitive experiments and significantly increasing research efficiency and innovativeness.
Currently, while autonomous synthesis systems can automate everything from reaction design to execution, reaction analysis still relies on individual processing using traditional equipment. This leads to slower speeds and bottlenecks, making it unsuitable for high-speed repetitive experiments.
Furthermore, multi-substrate simultaneous screening techniques proposed in the 1990s garnered attention as a strategy to maximize reaction analysis efficiency. However, limitations of existing chromatography-based analysis methods restricted the number of applicable substrates. In asymmetric synthesis reactions, which selectively synthesize only the desired optical isomer, simultaneously analyzing more than 10 types of substrates was nearly impossible.
< Figure 1. Conventional organic reaction evaluation methods follow a process of deriving optimal reaction conditions using a single substrate, then expanding the substrate scope one by one under those conditions, leaving potential reaction areas unexplored. To overcome this, high-throughput screening is introduced to broadly explore catalyst reactivity for various substrates. When combined with multi-substrate screening, this approach allows for a much broader and more systematic understanding of reaction scope and trends. >
To overcome these limitations, the research team developed a 19F NMR-based multi-substrate simultaneous screening technology. This method involves performing asymmetric catalytic reactions with multiple reactants in a single reaction vessel, introducing a fluorine functional group into the products, and then applying their self-developed chiral cobalt reagent to clearly quantify all optical isomers using 19F NMR.
Utilizing the excellent resolution and sensitivity of 19F NMR, the research team successfully performed asymmetric synthesis reactions of 21 substrates simultaneously in a single reaction vessel and quantitatively measured the product yield and optical isomer ratio without any separate purification steps.
Professor Hyunwoo Kim stated, "While anyone can perform asymmetric synthesis reactions with multiple substrates in one reactor, accurately analyzing all the products has been a challenging problem to solve until now. We expect that achieving world-class multi-substrate screening analysis technology will greatly contribute to enhancing the analytical capabilities of AI-driven autonomous synthesis platforms."
< Figure 2. A method for analyzing multi-substrate asymmetric catalytic reactions, where different substrates react simultaneously in a single reactor, using fluorine nuclear magnetic resonance has been implemented. By utilizing the characteristics of fluorine nuclear magnetic resonance, which has a clean background signal and a wide chemical shift range, the reactivity of each substrate can be quantitatively analyzed. It is also shown that the optical activity of all reactants can be simultaneously measured using a cobalt metal complex. >
He further added, "This research provides a technology that can rapidly verify the efficiency and selectivity of asymmetric catalytic reactions essential for new drug development, and it is expected to be utilized as a core analytical tool for AI-driven autonomous research."
< Figure 3. It can be seen that in a multi-substrate reductive amination reaction using a total of 21 substrates, the yield and optical activity of the reactants according to the catalyst system were simultaneously measured using a fluorine nuclear magnetic resonance-based analysis platform. The yield of each reactant is indicated by color saturation, and the optical activity by numbers. >
Donghun Kim (first author, Integrated M.S./Ph.D. program) and Gyeongseon Choi (second author, Integrated M.S./Ph.D. program) from the KAIST Department of Chemistry participated in this research. The study was published online in the Journal of the American Chemical Society on May 27, 2025.※ Paper Title: One-pot Multisubstrate Screening for Asymmetric Catalysis Enabled by 19F NMR-based Simultaneous Chiral Analysis※ DOI: 10.1021/jacs.5c03446
This research was supported by the National Research Foundation of Korea's Mid-Career Researcher Program, the Asymmetric Catalytic Reaction Design Center, and the KAIST KC30 Project.
< Figure 4. Conceptual diagram of performing multi-substrate screening reactions and utilizing fluorine nuclear magnetic resonance spectroscopy. >
2025.06.16 View 1476 -
KAIST Holds a Ceremony to Declare their Renewed Commitment for Ethical Management
KAIST held a ceremony to declare their renewed "Commitment for Ethical Management" to raise awareness and solidify the commitment its members to faithfully fulfill ethical responsibilities and duties.
Last March, the university established the 'Special Committee for Ethical Management,' chaired by the Provost, and under the leadership of this committee, a new 'Code of Ethics' and 'Code of Conduct' were prepared, containing ethical standards that members must adhere to across all areas of education, research, and administration.
< Photo 1. Attendees pledge to practice ethics during the declaration for the ethical management. >
This ceremony was arranged as an occasion for the president, key executives, and representatives from each university constituent to share the purpose and direction of the newly established ethical standards and to pledge their commitment to practicing them.
The Ethical Management Declaration consisted of: ▲ a progress report by the KAIST Special Committee for Ethical Management, ▲ a commemorative address by the president, ▲ an oath of the Code of Ethics and Code of Conduct, and ▲ the presentation of the 'Excellent Ethics Professor Award' organized by the Graduate Student Human Rights Center. Attendees shared the values and meaning of ethical management pursued by KAIST.
Particularly at this ceremony, six representatives – faculty, staff, and students – selected to reflect KAIST's values encompassing diversity in position, role, gender, and future generations, took the oath for the Code of Ethics and Code of Conduct.
< Photo 2. Attendees pledge to practice ethics during the Ethical Management Declaration. >
Also introduced at the ceremony was the "Ethical Excellence Award for Professors". It is an award that was organized by the Graduate Student Human Rights Center under the KAIST Student Council to recognize the faculty members for their outstanding ethical conduct in the laboratory setting. The 2025 recipients of the newly established award were the honored at the declaration ceremony for added significance.
Taking this declaration ceremony as an example, KAIST plans to actively encourage each departments, divisions and offices to also hold ethical management declarations of their own to establish a trustworthy, healthy, and transparent organizational culture through the daily practice of ethical responsibilities, and to continuously spread the practice of ethical management among all members.
President Kwang Hyung Lee emphasized, "Adhering to research and social ethics must be the foundation for KAIST to become a university trusted globally," and expressed, "I hope this ceremony serves as a turning point for all members to more faithfully practice their ethical responsibilities and duties."
2025.06.16 View 801 -
“One Experiment Is All It Takes”: KAIST Team Revolutionizes Drug Interaction Testing, Replacing 60,000 Studies
A groundbreaking new method developed by researchers at KAIST and Chungnam National University could drastically streamline drug interaction testing — replacing dozens of traditional experiments with just one.
The research, led by Professor Jae Kyoung Kim of KAIST Department of Mathematical Sciences & IBS Biomedical Mathematics Group and Professor Sang Kyum Kim of Chungnam National University's College of Pharmacy, introduces a novel analysis technique called 50-BOA, published in Nature Communications on June 5, 2025.
< Photo 1. (From left) Professor Sang Kyum Kim (Chungnam National University College of Pharmacy, co-corresponding author), Dr. Yun Min Song (IBS Biomedical Mathematics Group, formerly KAIST Department of Mathematical Sciences, co-first author), undergraduate student Hyeong Jun Jang (KAIST, co-first author), Professor Jae Kyoung Kim (KAIST and IBS Biomedical Mathematics Group, co-corresponding author) (Top left in the bubble) Professor Hwi-yeol Yun (Chungnam National University College of Pharmacy, co-author) >
For decades, scientists have had to repeat drug inhibition experiments across a wide range of concentrations to estimate inhibition constants — a process seen in over 60,000 scientific publications. But the KAIST-led team discovered that a single, well-chosen inhibitor concentration can yield even more accurate results.
< Figure 1. Graphical summary of 50-BOA. 50-BOA improves the accuracy and efficiency of inhibition constant estimation by using only a single inhibitor concentration instead of the traditionally used method of employing multiple inhibitor concentrations. >
“This approach challenges long-standing assumptions in experimental pharmacology,” says Prof. Kim. “It shows how mathematics can fundamentally redesign life science experiments.”
By mathematically analyzing the sources of error in conventional methods, the team found that over half the data typically collected adds no value or even skews results. Their new method not only cuts experimental effort by over 75%, but also enhances reproducibility and accuracy.
To help researchers adopt the method quickly, the team developed a user-friendly tool that takes simple Excel files as input, now freely available on GitHub:
☞ https://github.com/Mathbiomed/50-BOA
< Figure 2. The MATLAB and R package of 50-BOA at GitHub >
The work holds promise for faster and more reliable drug development, especially in assessing potential interactions in combination therapies. The U.S. FDA already emphasizes the importance of accurate enzyme inhibition assessment during early-stage drug evaluation — and this method could soon become a new gold standard.
2025.06.16 View 1665