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KAIST Takes the Lead in Developing Core Technologies for Generative AI National R&D Project
KAIST (President Kwang Hyung Lee) is leading the transition to AI Transformation (AX) by advancing research topics based on the practical technological demands of industries, fostering AI talent, and demonstrating research outcomes in industrial settings. In this context, KAIST announced on the 13th of August that it is at the forefront of strengthening the nation's AI technology competitiveness by developing core AI technologies via national R&D projects for generative AI led by the Ministry of Science and ICT.
In the 'Generative AI Leading Talent Cultivation Project,' KAIST was selected as a joint research institution for all three projects—two led by industry partners and one by a research institution—and will thus be tasked with the dual challenge of developing core generative AI technologies and cultivating practical, core talent through industry-academia collaborations.
Moreover, in the 'Development of a Proprietary AI Foundation Model' project, KAIST faculty members are participating as key researchers in four out of five consortia, establishing the university as a central hub for domestic generative AI research.
Each project in the Generative AI Leading Talent Cultivation Project will receive 6.7 billion won, while each consortium in the proprietary AI foundation model development project will receive a total of 200 billion won in government support, including GPU infrastructure.
As part of the 'Generative AI Leading Talent Cultivation Project,' which runs until the end of 2028, KAIST is collaborating with LG AI Research. Professor Noseong Park from the School of Computing will participate as the principal investigator for KAIST, conducting research in the field of physics-based generative AI (Physical AI). This project focuses on developing image and video generation technologies based on physical laws and developing a 'World Model.'
<(From Left) Professor Noseong Park, Professor Jae-gil Lee, Professor Jiyoung Whang, Professor Sung-Eui Yoon, Professor Hyunwoo Kim>
In particular, research being conducted by Professor Noseong Park's team and Professor Sung-Eui Yoon's team proposes a model structure designed to help AI learn the real-world rules of the physical world more precisely. This is considered a core technology for Physical AI.
Professors Noseong Park, Jae-gil Lee, Jiyoung Hwang, Sung-Eui Yoon, and Hyun-Woo Kim from the School of Computing, who have been globally recognized for their achievements in the AI field, are jointly participating in this project. This year, they have presented work at top AI conferences such as ICLR, ICRA, ICCV, and ICML, including: ▲ Research on physics-based Ollivier Ricci-flow (ICLR 2025, Prof. Noseong Park) ▲ Technology to improve the navigation efficiency of quadruped robots (ICRA 2025, Prof. Sung-Eui Yoon) ▲ A multimodal large language model for text-video retrieval (ICCV 2025, Prof. Hyun-Woo Kim) ▲ Structured representation learning for knowledge generation (ICML 2025, Prof. Jiyoung Whang).
In the collaboration with NC AI, Professor Tae-Kyun Kim from the School of Computing is participating as the principal investigator to develop multimodal AI agent technology. The research will explore technologies applicable to the entire gaming industry, such as 3D modeling, animation, avatar expression generation, and character AI. It is expected to contribute to training practical AI talents by giving them hands-on experience in the industrial field and making the game production pipeline more efficient.
As the principal investigator, Professor Tae-Kyun Kim, a renowned scholar in 3D computer vision and generative AI, is developing key technologies for creating immersive avatars in the virtual and gaming industries. He will apply a first-person full-body motion diffusion model, which he developed through a joint research project with Meta, to VR and AR environments.
<Professor Tae-Kyun Kim, Minhyeok Seong, and Tae-Hyun Oh from the School of Computing, and Professor Sung-Hee Lee, Woon-Tack Woo, Jun-Yong Noh, and Kyung-Tae Lim from the Graduate School of Culture Technology, Professor Ki-min Lee, Seungryong Kim from the Kim Jae-chul Graduate School of AI>
Professor Tae-Kyun Kim, Minhyeok Seong, and Tae-Hyun Oh from the School of Computing, and Professors Sung-Hee Lee, Woon-Tack Woo, Jun-Yong Noh, and Kyung-Tae Lim from the Graduate School of Culture Technology, are participating in the NC AI project. They have presented globally recognized work at CVPR 2025 and ICLR 2025, including: ▲ A first-person full-body motion diffusion model (CVPR 2025, Prof. Tae-Kyun Kim) ▲ Stochastic diffusion synchronization technology for image generation (ICLR 2025, Prof. Minhyeok Seong) ▲ The creation of a large-scale 3D facial mesh video dataset (ICLR 2025, Prof. Tae-Hyun Oh) ▲ Object-adaptive agent motion generation technology, InterFaceRays (Eurographics 2025, Prof. Sung-Hee Lee) ▲ 3D neural face editing technology (CVPR 2025, Prof. Jun-Yong Noh) ▲ Research on selective search augmentation for multilingual vision-language models (COLING 2025, Prof. Kyung-Tae Lim).
In the project led by the Korea Electronics Technology Institute (KETI), Professor Seungryong Kim from the Kim Jae-chul Graduate School of AI is participating in generative AI technology development. His team recently developed new technology for extracting robust point-tracking information from video data in collaboration with Adobe Research and Google DeepMind, proposing a key technology for clearly understanding and generating videos.
Each industry partner will open joint courses with KAIST and provide their generative AI foundation models for education and research. Selected outstanding students will be dispatched to these companies to conduct practical research, and KAIST faculty will also serve as adjunct professors at the in-house AI graduate school established by LG AI Research.
<Egocentric Whole-Body Motion Diffusion (CVPR 2025, Prof. Taekyun Kim's Lab), Stochastic Diffusion Synchronization for Image Generation (ICLR 2025, Prof. Minhyuk Sung's Lab), A Large-Scale 3D Face Mesh Video Dataset (ICLR 2025, Prof. Taehyun Oh's Lab), InterFaceRays: Object-Adaptive Agent Action Generation (Eurographics 2025, Prof. Sunghee Lee's Lab), 3D Neural Face Editing (CVPR 2025, Prof. Junyong Noh's Lab), and Selective Retrieval Augmentation for Multilingual Vision-Language Models (COLING 2025, Prof. Kyeong-tae Lim's Lab)>
Meanwhile, KAIST showed an unrivaled presence by participating in four consortia for the Ministry of Science and ICT's 'Proprietary AI Foundation Model Development' project.
In the NC AI Consortium, Professors Tae-Kyun Kim, Sung-Eui Yoon, Noseong Park, Jiyoung Hwang, and Minhyeok Seong from the School of Computing are participating, focusing on the development of multimodal foundation models (LMMs) and robot-based models. They are particularly concentrating on developing LMMs that learn common sense about space, physics, and time. They have formed a research team optimized for developing next-generation, multimodal AI models that can understand and interact with the physical world, equipped with an 'all-purpose AI brain' capable of simultaneously understanding and processing diverse information such as text, images, video, and sound.
In the Upstage Consortium, Professors Jae-gil Lee and Hyeon-eon Oh from the School of Computing, both renowned scholars in data AI and NLP (natural language processing), along with Professor Kyung-Tae Lim from the Graduate School of Culture Technology, an LLM expert, are responsible for developing vertical models for industries such as finance, law, and manufacturing. The KAIST researchers will concentrate on developing practical AI models that are directly applicable to industrial settings and tailored to each specific industry.
The Naver Consortium includes Professor Tae-Hyun Oh from the School of Computing, who has developed key technology for multimodal learning and compositional language-vision models, Professor Hyun-Woo Kim, who has proposed video reasoning and generation methods using language models, and faculty from the Kim Jae-chul Graduate School of AI and the Department of Electrical Engineering.
In the SKT Consortium, Professor Ki-min Lee from the Kim Jae-chul Graduate School of AI, who has achieved outstanding results in text-to-image generation, human preference modeling, and visual robotic manipulation technology development, is participating. This technology is expected to play a key role in developing personalized services and customized AI solutions for telecommunications companies.
This outcome is considered a successful culmination of KAIST's strategy for developing AI technology based on industry demand and centered on on-site demonstrations.
KAIST President Kwang Hyung Lee said, "For AI technology to go beyond academic achievements and be connected to and practical for industry, continuous government support, research, and education centered on industry-academia collaboration are essential. KAIST will continue to strive to solve problems in industrial settings and make a real contribution to enhancing the competitiveness of the AI ecosystem."
He added that while the project led by Professor Sung-Ju Hwang from the Kim Jae-chul Graduate School of AI, which had applied as a lead institution for the proprietary foundation model development project, was unfortunately not selected, it was a meaningful challenge that stood out for its original approach and bold attempts. President Lee further commented, "Regardless of whether it was selected or not, such attempts will accumulate and make the Korean AI ecosystem even richer."
2025.08.13 View 50 -
KAIST Develops AI That Automatically Designs Optimal Drug Candidates for Cancer-Targeting Mutations
< (From left) Ph.D candidate Wonho Zhung, Ph.D cadidate Joongwon Lee , Prof. Woo Young Kim , Ph.D candidate Jisu Seo >
Traditional drug development methods involve identifying a target protin (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate. KAIST researchers have developed an AI model that, using only information about the target protein, can design optimal drug candidates without any prior molecular data—opening up new possibilities for drug discovery.
KAIST (President Kwang Hyung Lee) announced on the 10th that a research team led by Professor Woo Youn Kim in the Department of Chemistry has developed an AI model named BInD (Bond and Interaction-generating Diffusion model), which can design and optimize drug candidate molecules tailored to a protein’s structure alone—without needing prior information about binding molecules. The model also predicts the binding mechanism (non-covalent interactions) between the drug and the target protein.
The core innovation of this technology lies in its “simultaneous design” approach. Previous AI models either focused on generating molecules or separately evaluating whether the generated molecule could bind to the target protein. In contrast, this new model considers the binding mechanism between the molecule and the protein during the generation process, enabling comprehensive design in one step. Since it pre-accounts for critical factors in protein-ligand binding, it has a much higher likelihood of generating effective and stable molecules. The generation process visually demonstrates how types and positions of atoms, covalent bonds, and interactions are created simultaneously to fit the protein’s binding site.
<Figure 1. Schematic of the diffusion model developed by the research team, which generates molecular structures and non-covalent interactions based on protein structures. Starting from a noise distribution, the model gradually removes noise (via reverse diffusion) to restore the atom positions, types, covalent bond types, and interaction types, thereby generating molecules. Interacting patterns are extracted from prior knowledge of known binding molecules or proteins, and through an inpainting technique, these patterns are kept fixed during the reverse diffusion process to guide the molecular generation.>
Moreover, this model is designed to meet multiple essential drug design criteria simultaneously—such as target binding affinity, drug-like properties, and structural stability. Traditional models often optimized for only one or two goals at the expense of others, but this new model balances various objectives, significantly enhancing its practical applicability.
The research team explained that the AI operates based on a “diffusion model”—a generative approach where a structure becomes increasingly refined from a random state. This is the same type of model used in AlphaFold 3, the 2024 Nobel Chemistry Prize-winning tool for protein-ligand structure generation, which has already demonstrated high efficiency.
Unlike AlphaFold 3, which provides spatial coordinates for atom positions, this study introduced a knowledge-based guide grounded in actual chemical laws—such as bond lengths and protein-ligand distances—enabling more chemically realistic structure generation.
<Figure 2. (Left) Target protein and the original bound molecule; (Right) Examples of molecules designed using the model developed in this study. The values for protein binding affinity (Vina), drug-likeness (QED), and synthetic accessibility (SA) are shown at the bottom.>
Additionally, the team applied an optimization strategy where outstanding binding patterns from prior results are reused. This allowed the model to generate even better drug candidates without additional training. Notably, the AI successfully produced molecules that selectively bind to the mutated residues of EGFR, a cancer-related target protein.
This study is also meaningful because it advances beyond the team’s previous research, which required prior input about the molecular conditions for the interaction pattern of protein binding.
Professor Woo Youn Kim commented that “the newly developed AI can learn and understand the key features required for strong binding to a target protein, and design optimal drug candidate molecules—even without any prior input. This could significantly shift the paradigm of drug development.” He added, “Since this technology generates molecular structures based on principles of chemical interactions, it is expected to enable faster and more reliable drug development.”
Joongwon Lee and Wonho Zhung, PhD students in the Department of Chemistry, participated as co-first authors of this study. The research results were published in the international journal Advanced Science (IF = 14.1) on July 11.
● Paper Title: BInD: Bond and Interaction-Generating Diffusion Model for Multi-Objective Structure-Based Drug Design
● DOI: 10.1002/advs.202502702
This research was supported by the National Research Foundation of Korea and the Ministry of Health and Welfare.
2025.08.12 View 157 -
KAIST Develops Bioelectrosynthesis Platform for Switch-Like Precision Control of Cell Signaling
<(From left)Professor Jimin Park, Ph.D candidate Myeongeun Lee, Ph.D cadidate Jaewoong Lee,Professor Jihan Kim>
Cells use various signaling molecules to regulate the nervous, immune, and vascular systems. Among these, nitric oxide (NO) and ammonia (NH₃) play important roles, but their chemical instability and gaseous nature make them difficult to generate or control externally. A KAIST research team has developed a platform that generates specific signaling molecules in situ from a single precursor under an applied electrical signal, enabling switch-like, precise spatiotemporal control of cellular responses. This approach could provide a foundation for future medical technologies such as electroceuticals, electrogenetics, and personalized cell therapies.
KAIST (President Kwang Hyung Lee) announced on August 11 that a research team led by Professor Jimin Park from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Jihan Kim's group, has developed a 'Bioelectrosynthesis Platform' capable of producing either nitric oxide or ammonia on demand using only an electrical signal. The platform allows control over the timing, spatial range, and duration of cell responses.
Inspired by enzymes involved in nitrite reduction, the researchers implemented an electrochemical strategy that selectively produces nitric oxide or ammonia from a single precursor, nitrite (NO₂⁻). By changing the catalyst, the team generated ammonia or nitric oxide from nitrite using a copper-molybdenum-sulfur catalyst (Cu2MoS4) and an iron-incorporated catalyst (FeCuMS4), respectively.
Through electrochemical measurements and computer simulations, the team revealed that Fe sites in the FeCuMoS4 catalyst bind nitric oxide intermediates more strongly, shifting product selectivity toward nitric oxide. Under the same electrical conditions, the Fe-containing catalyst preferentially produces nitric oxide, whereas the Cu2MoS4 catalyst favors ammonia production.
<Figure 1. Schematic diagram of a bio-electrosynthesis platform that synthesizes a desired signaling substance with an electrical signal (left) and the results of precise cell control using it (right)>
The research team demonstrated biological functionality by using the platform to activate ion channels in human cells. Specifically, electrochemically produced nitric oxide activated TRPV1 channels (responsive to heat and chemical stimuli), while electrochemically produced ammonia induced intracellular alkalinization and activated OTOP1 proton channels. By tuning the applied voltage and electrolysis duration, the team modulated the onset time, spatial extent, and termination of cellular responses, which effectively turned cellular signaling on and off like a switch.
<Figure 2. Experimental results showing the change in the production ratio of nitric oxide and ammonia signaling substances according to the type of catalyst (left) and computational simulation results showing the strong bond between iron and nitric oxide (right)>
Professor Jimin Park said, "This work is significant because it enables precise cellular control by selectively producing signaling molecules with electricity. We believe it has strong potential for applications in electroceutical technologies targeting the nervous system or metabolic disorders."
Myeongeun Lee and Jaewoong Lee, Ph.D. students in the Department of Chemical and Biomolecular Engineering at KAIST, served as the co-first authors. Professor Jihan Kim is a co-author. The paper was published online in 'Angewandte Chemie International Edition' on July 8, 2025 (DOI: 10.1002/ange.202508192).
Reference: https://doi.org/10.1002/ange.202508192
Authors: Myeongeun Lee†, Jaewoong Lee†, Yongha Kim, Changho Lee, Sang Yeon Oh, Prof. Jihan Kim, Prof. Jimin Park*
†These authors contributed equally. *Corresponding author.
2025.08.12 View 83 -
2025 APEC Youth STEM Science Exchange Program Successfully Completed
<Photo1. Group photo at the end of the program>
KAIST (President Kwang Hyung Lee) announced on the 11thof August that it successfully hosted the 'APEC Youth STEM Conference KAIST Academic Program,' a global science exchange program for 28 youth researchers from 10 countries and over 30 experts who participated in the '2025 APEC Youth STEM* Collaborative Research and Competition.' The event was held at the main campus in Daejeon on Saturday, August 9.
STEM (Science, Technology, Engineering, Math) refers to the fields of science and engineering.
The competition was hosted by the Ministry of Science and ICT and organized by the APEC Science Gifted Mentoring Center. It took place from Wednesday, August 6, to Saturday, August 9, 2025, at KAIST in Daejeon and the Korea Science Academy of KAIST in Busan. The KAIST program was organized by the APEC Science Gifted Mentoring Center and supported by the KAIST Institute for the Gifted and Talented in Science Education.
Participants had the opportunity to experience Korea's cutting-edge research infrastructure firsthand, broaden their horizons in science and technology, and collaborate and exchange ideas with future science talents from the APEC region.
As the 2025 APEC chair, Korea is promoting various international collaborations to discover and nurture the next generation of talent in the STEM fields. The KAIST academic exchange program was particularly meaningful as it was designed with the international goal of revitalizing science gifted exchanges and expanding the basis for cooperation among APEC member countries. It moved beyond the traditional online-centric research collaboration model to focus on hands-on, on-site, and convergence research experiences.
The global science exchange program at KAIST introduced participants to KAIST's world-class educational and research environment and provided various academic content to allow them to experience real-world examples of convergence technology-based research.
<Photo2. Program Activities>
First, the KAIST Admissions Office participated, introducing KAIST's admissions system and its educational and research environment to outstanding international students, providing an opportunity to attract global talent. Following this, Dr. Tae-kyun Kwon of the Music and Audio Computing Lab at the Graduate School of Culture Technology presented a convergence art project based on musical artificial intelligence data, including a research demonstration in an anechoic chamber.
<Photo3. Participation in a music AI research demonstration>
Furthermore, a Climate Talk Concert program was organized under the leadership of the Graduate School of Green Growth and Sustainability, in connection with the theme of the APEC Youth STEM Collaborative Research: 'Youth-led STEM Solutions: Enhancing Climate Resilience.'
The program was planned and hosted by Dean Jiyong Eom. It provided a platform for young people to explore creative and practical STEM-based solutions to the climate crisis and seek opportunities for international cooperation.
<Photo4. Participation in Music AI Research Demonstration >
The program was a meaningful time for APEC youth researchers, offering practical support for their research through special lectures and Q&A sessions on:
Interdisciplinary Research and Education in the Era of Climate Crisis (Dean Jiyong Eom)
Energy Transition Technology in the Carbon Neutral Era (Professor Jeongrak Son)
Policies for Energy System Change (Professor Jihyo Kim)
Carbon Neutral Bio-technology (Professor Gyeongrok Choi)
After the afternoon talk concert, Lee Jing Jing, a student from Brunei, shared her thoughts, saying, "The lectures by the four professors were very meaningful and insightful. I was able to think about energy transition plans to solve climate change from various perspectives."
Si-jong Kwak, Director of the KAIST Global Institute for Talented Education, stated, "I hope that young people from all over the world will directly experience KAIST's research areas and environment, expand their interest in KAIST, and continue to grow as outstanding talents in the fields of science and engineering."
KAIST President Kwang Hyung Lee said, "KAIST will be at the center of science and technology-based international cooperation and will spare no effort to support future talents in developing creative and practical problem-solving skills. I hope this event served as an opportunity for young people to understand the value of global cooperation and grow into future science leaders."
2025.08.11 View 102 -
Prof. Seungbum Koo’s Team Receives Clinical Biomechanics Award at the 30th International Society of Biomechanics Conference
<(From Left) Ph.D candidate Jeongseok Oh from KAIST, Dr. Seungwoo Yoon from KAIST, Prof.Joon-Ho Wang from Samsung Medical Center, Prof.Seungbum Koo from KAIST>
Professor Seungbum Koo’s research team received the Clinical Biomechanics Award at the 30th International Society of Biomechanics (ISB) Conference, held in July 2025 in Stockholm, Sweden. The Plenary Lecture was delivered by first author and Ph.D. candidate Jeongseok Oh. This research was conducted in collaboration with Professor Joon-Ho Wang’s team at Samsung Medical Center.
Residual Translational and Rotational Kinematics After Combined ACL and Anterolateral Ligament Reconstruction During Walking
Jeongseok Oh, Seungwoo Yoon, Joon-Ho Wang, Seungbum Koo
The study analyzed gait-related knee joint motion using high-speed biplane X-ray imaging and three-dimensional kinematic reconstruction in 10 healthy individuals and 10 patients who underwent ACL reconstruction with ALL augmentation. The patient group showed excessive anterior translation and internal rotation, suggesting incomplete restoration of normal joint kinematics post-surgery. These findings provide mechanistic insight into the early onset of knee osteoarthritis often reported in this population.'
The ISB conference, held biennially for over 60 years, is the largest international biomechanics meeting. This year, it hosted 1,600 researchers from 46 countries and featured over 1,400 presentations. The Clinical Biomechanics Award is given to one outstanding study selected from five top-rated abstracts invited for full manuscript review. The winning paper is published in Clinical Biomechanics, and the award includes a monetary prize and a Plenary Lecture opportunity.
From 2019 to 2023, Koo and Wang’s teams developed a system with support from the Samsung Future Technology Development Program to track knee motion in real time during treadmill walking, using high-speed biplane X-rays and custom three-dimensional reconstruction software. This system, along with proprietary software that precisely reconstructs the three-dimensional motion of joints, was approved for clinical trials by the Ministry of Food and Drug Safety and installed at Samsung Medical Center. It is being used to quantitatively analyze abnormal joint motion patterns in patients with knee ligament injuries and those who have undergone knee surgery.
Additionally, Jeongseok Oh was named one of five finalists for the David Winter Young Investigator Award, presenting his work during the award session. This award recognizes promising young researchers in biomechanics worldwide.
2025.08.10 View 143 -
Key Figures in the Establishment of KAIST, Specially Invited to the Presidential Office’s National Appointment Ceremony
KAIST announced on August 6 that Professor Emeritus Jung-Woong Ra from the Department of Electrical Engineering and Won-ki Kwon, former Vice Minister of the Ministry of Science and Technology, who played pivotal roles in the establishment of KAIST, were selected as special guests for the 'National Appointment Ceremony' hosted by the Presidential Office on August 15th.
The Presidential Office selected special invitees across eight categories for the ceremony. These include individuals born in 1945 (referred to as 'Liberation Babies'), those involved in the founding of KAIST in 1971, independence activists and national patriots, overseas workers in Germany and the Middle East, AI industry professionals, residents from regions facing depopulation, leading figures in K-culture, military personnel, firefighters, police officers, families of fallen public servants and victims of social disasters, as well as promising talents in economics, science, culture, and the arts.
Considering the historical significance of its establishment and its symbolic meaning for the development of national science and technology, KAIST Professor Emeritus Jung-Woong Ra, who was a key figure in the establishment of the Department of Electrical Engineering after being appointed as a professor in 1971, and former Vice Minister Kwon Won-ki, who was the first practical leader of the establishment project. Both were officially included on the special invitation list.
Briefing from the Presidential Office regarding the 'National Appointment Ceremony' (2025.07.28) https://www.president.go.kr/newsroom/briefing/grehGMuP
2025.08.06 View 217 -
Material Innovation Realized with Robotic Arms and AI, Without Human Researchers
<(From Left) M.S candidate Dongwoo Kim from KAIST, Ph.D candidate Hyun-Gi Lee from KAIST, Intern Yeham Kang from KAIST, M.S candidate Seongjae Bae from KAIST, Professor Dong-Hwa Seo from KAIST, (From top right, from left) Senior Researcher Inchul Park from POSCO Holdings, Senior Researcher Jung Woo Park, senior researcher from POSCO Holdings>
A joint research team from industry and academia in Korea has successfully developed an autonomous lab that uses AI and automation to create new cathode materials for secondary batteries. This system operates without human intervention, drastically reducing researcher labor and cutting the material discovery period by 93%.
* Autonomous Lab: A platform that autonomously designs, conducts, and analyzes experiments to find the optimal material.
KAIST (President Kwang Hyung Lee) announced on the 3rd of August that the research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, in collaboration with the team of LIB Materials Research Center in Energy Materials R&D Laboratories at POSCO Holdings' POSCO N.EX.T Hub (Director Ki Soo Kim), built the lab to explore cathode materials using AI and automation technology.
Developing secondary battery cathode materials is a labor-intensive and time-consuming process for skilled researchers. It involves extensive exploration of various compositions and experimental variables through weighing, transporting, mixing, sintering*, and analyzing samples.
* Sintering: A process in which powder particles are heated to form a single solid mass through thermal activation.
The research team's autonomous lab combines an automated system with an AI model. The system handles all experimental steps—weighing, mixing, pelletizing, sintering, and analysis—without human interference. The AI model then interprets the data, learns from it, and selects the best candidates for the next experiment.
<Figure 1. Outline of the Anode Material Autonomous Exploration Laboratory>
To increase efficiency, the team designed the automation system with separate modules for each process, which are managed by a central robotic arm. This modular approach reduces the system's reliance on the robotic arm.
The team also significantly improved the synthesis speed by using a new high-speed sintering method, which is 50 times faster than the conventional low-speed method. This allows the autonomous lab to acquire 12 times more material data compared to traditional, researcher-led experiments.
<Figure 2. Synthesis of Cathode Material Using a High-Speed Sintering Device>
The vast amount of data collected is automatically interpreted by the AI model to extract information such as synthesized phases and impurity ratios. This data is systematically stored to create a high-quality database, which then serves as training data for an optimization AI model. This creates a closed-loop experimental system that recommends the next cathode composition and synthesis conditions for the automated system.
* Closed-loop experimental system: A system that independently performs all experimental processes without researcher intervention.
Operating this intelligent automation system 24 hours a day can secure more than 12 times the experimental data and shorten material discovery time by 93%. For a project requiring 500 experiments, the system can complete the work in about 6 days, whereas a traditional researcher-led approach would take 84 days.
During development, POSCO Holdings team managed the overall project planning, reviewed the platform design, and co-developed the partial module design and AI-based experimental model. The KAIST team, led by Professor Dong-hwa Seo, was responsible for the actual system implementation and operation, including platform design, module fabrication, algorithm creation, and system verification and improvement.
Professor Dong-Hwa Seo of KAIST stated that this system is a solution to the decrease in research personnel due to the low birth rate in Korea. He expects it will enhance global competitiveness by accelerating secondary battery material development through the acquisition of high-quality data.
<Figure 3. Exterior View (Side) of the Cathode Material Autonomous Exploration Laboratory>
POSCO N.EX.T Hub plans to apply an upgraded version of this autonomous lab to its own research facilities after 2026 to dramatically speed up next-generation secondary battery material development. They are planning further developments to enhance the system's stability and scalability, and hope this industry-academia collaboration will serve as a model for using innovative technology in real-world R&D.
<Figure 4. Exterior View (Front) of the Cathode Material Autonomous Exploration Laboratory>
The research was spearheaded by Ph.D. student Hyun-Gi Lee, along with master's students Seongjae Bae and Dongwoo Kim from Professor Dong-Hwa Seo’s lab at KAIST. Senior researchers Jung Woo Park and Inchul Park from LIB Materials Research Center of POSCO N.EX.T Hub's Energy Materials R&D Laboratories (Director Jeongjin Hong) also participated.
2025.08.06 View 261 -
Anti-Neuroinflammatory Natural Products from Isopod-Related Fungus Now Accessible via Chemical Synthesis
<(From left) Professor Sunkyu Han, Ph.D candidate Yoojin Lee, Ph.D candidate Taewan Kim>
"Herpotrichone" is a natural substance that has been evaluated highly for its excellent ability to suppress inflammation in the brain and protect nerve cells, displaying significant potential to be developed as a therapeutic agent for neurodegenerative brain diseases such as Alzheimer's disease and Parkinson's disease. This substance could only be obtained in minute quantities from fungi that are symbiotic with isopods. However, KAIST researchers have succeeded in chemically synthesizing this rare natural product, thereby presenting the possibility for the development of next-generation drugs for neurodegenerative diseases.
*Chemical Synthesis: A process of creating desired substances using chemical reactions.
KAIST (President Kwang Hyung Lee) announced on the 31st of July that a research team led by Professor Sunkyu Han of the Department of Chemistry successfully synthesized the natural anti-neuroinflammatory substances 'herpotrichones A, B, and C' for the first time.
Herpotrichone natural products are substances obtainable only in minute quantities from 'Herpotrichia sp. SF09', a symbiotic pill bug fungus, and possess a unique 6/6/6/6/3 pentacyclic framework consisting of five fused rings (four six-membered and one three-membered ring).
Interestingly, this substance exhibits excellent anti-neuroinflammatory effects that suppress brain inflammatory reactions. Recently, its mechanism of action to protect nerve cells by inhibiting ferroptosis (iron-mediated cell death) was also reported, raising expectations for its potential as a therapeutic drug for brain diseases.
Professor Han's research team devised a biosynthetically inspired strategy to chemically synthesize herpotrichoneS. The key to success was a named chemical reaction "Diels-Alder (DA) reaction". This reaction forms a six-membered ring by creating new bonds between carbon-based partners, much like two puzzle pieces interlocking to form a single ring.
<Figure 2. Key Synthetic Strategy for Hypotricon A, B, and C Based on Hydrogen Bonding>
Furthermore, the research team focused on a weak attractive phenomenon between molecules called "hydrogen bonding". By delicately designing and controlling this hydrogen bond, they were able to precisely induce the reaction to occur chemo-, regio- and stereoselectively, thereby synthesizing herpotrichone. Notably, without the pivotal hydrogen bond, only a small amount of the target natural product was formed or only undesirable byproducts were generated.
The configuration of the C2’ hydroxyl moiety was essential in directing the desired transition states leading to the target natural products.
Thanks to this induced hydrogen bonding, the reacting molecules approached the correct positions and went through an ideal transition state, allowing for the synthesis of herpotrichone C. This reaction principle was also successfully applied to herpotrichone A and B, enabling the successful synthesis of these natural products.
During the key Diels-Alder reaction conducted in the laboratory, new molecular structures not yet discovered in nature were also formed. Some of these have a high probability of being novel natural products with excellent pharmacological activity, thus doubling the significance of this research for anticipating natural products through synthesis.
Indeed, while Professor Han's research team conducted synthetic studies on herpotrichone A and B based on a 2019 paper by Chinese researchers who discovered and elucidated their structures, the research team observed the formation of undesired byproducts.
Interestingly, in 2024, the same Chinese research team that discovered herpotrichones A and bn reported the discovery of a new natural product called herpotrichone C, which turned out to be the same substance as the major byproduct previously obtained by Professor Han's team en route to herpotrichones A and B.
Professor Han stated, "This is the first total synthesis of a rare natural product with pharmacological activity related to neurodegenerative diseases and systematically presents the principle of biomimetic synthesis of complex natural products." He added, "It is expected to contribute to the development of novel natural product-based anti-neuroinflammatory therapeutics and biosynthesis research of this group of natural products."
This research outcome, with Yoojin Lee, a master's and Ph.D. integrated course student in the Department of Chemistry, as the first author, was published on July 16th in the Journal of the American Chemical Society (JACS), one of the most prestigious academic journals in the field of chemistry.
This research was supported by the National Research Foundation of Korea (NRF) Mid-career Researcher Support Program, the KAIST UP Project, the KAIST Grand Challenge 30 Project, and the KAIST Trans-Generational Collaborative Research Laboratory Project.
2025.08.04 View 383 -
KAIST Successfully Presents the Future of AI Transformation and Physical AI Strategy at the 1st National Strategic Technology Forum
<(Front row, fourth from the right) President Kwang Hyung Lee of KAIST, (back row, fifth from the right) Forum co-host Representative Hyung-Doo Choi, (back row, sixth from the left) Forum co-host Representative Han-Kyu Kim, along with ruling and opposition party members of the Science, ICT, Broadcasting, and Communications Committee and the Trade, Industry, Energy, SMEs, and Startups Committee, as well as Professors Hoe-Jun Yoo and Jung Kim from KAIST)>
KAIST (President Kwang Hyung Lee) announced on July that it had successfully held the “1st National Strategic Technology Forum” at the National Assembly Members' Office Building that day under the theme “The Future of Artificial Intelligence Transformation (AX): Physical AI.” This bipartisan policy forum aimed to discuss strategies for technology hegemony by leveraging Korea’s strengths in AI semiconductors and manufacturing.
The forum was hosted by KAIST and co-organized by Representative Hyung-Du Choi (People Power Party), the secretary of the National Assembly's Science, ICT, Broadcasting, and Communications Committee, and Representative Han-Kyu Kim (Democratic Party), a member of the Trade, Industry, Energy, SMEs, and Startups Committee. It marks the beginning of a five-part forum series, scheduled monthly through the rest of the year except for October.
The overarching theme, “Artificial Intelligence Transformation (AX),” was designed to address the structural changes reshaping industry, the economy, and society due to the spread of generative AI.
< KAIST President Kwang Hyung Lee delivering his remarks >
The first session focused on “Physical AI,” reflecting how AI innovation—sparked by the proliferation of large language models (LLMs)—is rapidly expanding into the physical realm through ultra-low-power, ultra-lightweight semiconductors. This includes applications in robotics, sensors, and edge devices. Physical AI refers to technologies that interact directly with the real world through AI integration with robotics, autonomous driving, and smart factories. It is drawing attention as a promising next-generation field where Korea can secure a strategic edge, given its strengths in semiconductors and manufacturing.
<Hoi-Jun Yoo, Dean of the KAIST Graduate School of AI Semiconductor>
Hoi-Jun Yoo, Dean of the KAIST Graduate School of AI Semiconductor, gave a presentation titled “The Second AI Innovation Enabled by Ultra-Low-Power AI Semiconductors and Lightweight AI Models,” covering semiconductor trends for implementing Physical AI, academic and industrial strategies for robotics and semiconductors, and Korea’s development direction for “K-Physical AI.”
<Professor Jung Kim, the head of KAIST’s Department of Mechanical and Aerospace Engineering>
Following that, Professor Jung Kim, the head of KAIST’s Department of Mechanical and Aerospace Engineering gave a talk on “Trends in Physical AI and Humanoid Robots,” predicting a new industrial paradigm shaped by AI-robot convergence. He presented global trends, Korea’s development trajectory, and survival strategies for humanoid robots that can supplement or replace human intellectual and physical functions.
During the open discussion that followed, participating lawmakers and experts engaged in in-depth conversations about the need for bipartisan strategies and collaboration.
Representative Hyung-Du Choi (People Power Party) stated, “Through this forum as a platform for public discourse, I will work to ensure that legislation and policy align with the direction of the science and technology field, and that necessary measures are taken promptly to strengthen national competitiveness.”
Representative Han-Kyu Kim (Democratic Party) emphasized, “As strategic planning in science and technology accelerates, it becomes more difficult to coordinate policies involving multiple ministries. Forums like this, which enable ongoing communication among stakeholders, are instrumental in finding effective solutions.”
KAIST President Kwang Hyung Lee remarked, “Although Korea is a latecomer in the generative AI field, we have a unique opportunity to gain strategic superiority in Physical AI, thanks to our technological capabilities in manufacturing, semiconductors, and robotics.” He added, “I hope lawmakers from both the ruling and opposition parties, along with experts, will come together regularly to devise practical policies and contribute to the advancement of Korea’s science and technology.”
<Poster of National Strategic Technology Forum>
This forum series aims to explore policy and institutional solutions to help Korea gain technological leadership in a global context where strategic technologies—such as AI, semiconductors, biotechnology, and energy—directly influence national security and economic sovereignty. Lawmakers from both the Science, ICT, Broadcasting, and Communications Committee and the Trade, Industry, Energy, SMEs, and Startups Committee will continue to participate, fostering bipartisan dialogue. The forums are coordinated by the KAIST Policy Research Institute for National Strategic Technologies.
2025.07.31 View 289 -
Is 24-hour health monitoring possible with ambient light energy?
<(From left) Ph.D candidate Youngmin Sim, Ph.D candidate Do Yun Park, Dr. Chanho Park, Professor Kyeongha Kwon>
Miniaturization and weight reduction of medical wearable devices for continuous health monitoring such as heart rate, blood oxygen saturation, and sweat component analysis remain major challenges. In particular, optical sensors consume a significant amount of power for LED operation and wireless transmission, requiring heavy and bulky batteries. To overcome these limitations, KAIST researchers have developed a next-generation wearable platform that enables 24-hour continuous measurement by using ambient light as an energy source and optimizing power management according to the power environment.
KAIST (President Kwang Hyung Lee) announced on the 30th that Professor Kyeongha Kwon's team from the School of Electrical Engineering, in collaboration with Dr. Chanho Park’s team at Northwestern University in the U.S., has developed an adaptive wireless wearable platform that reduces battery load by utilizing ambient light.
To address the battery issue of medical wearable devices, Professor Kyeongha Kwon’s research team developed an innovative platform that utilizes ambient natural light as an energy source. This platform integrates three complementary light energy technologies.
<Figure1.The wireless wearable platform minimizes the energy required for light sources through i) Photometric system that directly utilizes ambient light passing through windows for measurements, ii) Photovoltaic system that receives power from high-efficiency photovoltaic cells and wireless power receiver coils, and iii) Photoluminescent system that stores light using photoluminescent materials and emits light in dark conditions to support the two aforementioned systems. In-sensor computing minimizes power consumption by wirelessly transmitting only essential data. The adaptive power management system efficiently manages power by automatically selecting the optimal mode among 11 different power modes through a power selector based on the power supply level from the photovoltaic system and battery charge status.>
The first core technology, the Photometric Method, is a technique that adaptively adjusts LED brightness depending on the intensity of the ambient light source. By combining ambient natural light with LED light to maintain a constant total illumination level, it automatically dims the LED when natural light is strong and brightens it when natural light is weak.
Whereas conventional sensors had to keep the LED on at a fixed brightness regardless of the environment, this technology optimizes LED power in real time according to the surrounding environment. Experimental results showed that it reduced power consumption by as much as 86.22% under sufficient lighting conditions.
The second is the Photovoltaic Method using high-efficiency multijunction solar cells. This goes beyond simple solar power generation to convert light in both indoor and outdoor environments into electricity. In particular, the adaptive power management system automatically switches among 11 different power configurations based on ambient conditions and battery status to achieve optimal energy efficiency.
The third innovative technology is the Photoluminescent Method. By mixing strontium aluminate microparticles* into the sensor’s silicone encapsulation structure, light from the surroundings is absorbed and stored during the day and slowly released in the dark. As a result, after being exposed to 500W/m² of sunlight for 10 minutes, continuous measurement is possible for 2.5 minutes even in complete darkness.
*Strontium aluminate microparticles: A photoluminescent material used in glow-in-the-dark paint or safety signs, which absorbs light and emits it in the dark for an extended time.
These three technologies work complementarily—during bright conditions, the first and second methods are active, and in dark conditions, the third method provides additional support—enabling 24-hour continuous operation.
The research team applied this platform to various medical sensors to verify its practicality. The photoplethysmography sensor monitors heart rate and blood oxygen saturation in real time, allowing early detection of cardiovascular diseases. The blue light dosimeter accurately measures blue light, which causes skin aging and damage, and provides personalized skin protection guidance. The sweat analysis sensor uses microfluidic technology to simultaneously analyze salt, glucose, and pH in sweat, enabling real-time detection of dehydration and electrolyte imbalances.
Additionally, introducing in-sensor data computing significantly reduced wireless communication power consumption. Previously, all raw data had to be transmitted externally, but now only the necessary results are calculated and transmitted within the sensor, reducing data transmission requirements from 400B/s to 4B/s—a 100-fold decrease.
To validate performance, the research tested the device on healthy adult subjects in four different environments: bright indoor lighting, dim lighting, infrared lighting, and complete darkness. The results showed measurement accuracy equivalent to that of commercial medical devices in all conditions A mouse model experiment confirmed accurate blood oxygen saturation measurement in hypoxic conditions.
<Frigure2.The multimodal device applying the energy harvesting and power management platform consists of i) photoplethysmography (PPG) sensor, ii) blue light dosimeter, iii) photoluminescent microfluidic channel for sweat analysis and biomarker sensors (chloride ion, glucose, and pH), and iv) temperature sensor. This device was implemented with flexible printed circuit board (fPCB) to enable attachment to the skin. A silicon substrate with a window that allows ambient light and measurement light to pass through, along with photoluminescent encapsulation layer, encapsulates the PPG, blue light dosimeter, and temperature sensors, while the photoluminescent microfluidic channel is attached below the photoluminescent encapsulation layer to collect sweat>
Professor Kyeongha Kwon of KAIST, who led the research, stated, “This technology will enable 24-hour continuous health monitoring, shifting the medical paradigm from treatment-centered to prevention-centered shifting the medical paradigm from treatment-centered to prevention-centered,” further stating that “cost savings through early diagnosis as well as strengthened technological competitiveness in the next-generation wearable healthcare market are anticipated.”
This research was published on July 1 in the international journal Nature Communications, with Do Yun Park, a doctoral student in the AI Semiconductor Graduate Program, as co–first author.
※ Paper title: Adaptive Electronics for Photovoltaic, Photoluminescent and Photometric Methods in Power Harvesting for Wireless and Wearable Sensors ※ DOI: https://doi.org/10.1038/s41467-025-60911-1 ※ URL: https://www.nature.com/articles/s41467-025-60911-1
This research was supported by the National Research Foundation of Korea (Outstanding Young Researcher Program and Regional Innovation Leading Research Center Project), the Ministry of Science and ICT and Institute of Information & Communications Technology Planning & Evaluation (IITP) AI Semiconductor Graduate Program, and the BK FOUR Program (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE).
2025.07.30 View 530 -
Better Sleep, Better Life — KAIST’s Sleep Algorithm Comes to Samsung Galaxy Watches
<Professor Jae Kyoung Kim of KAIST's Department of Mathematical Sciences>
Did you know that over 80% of people worldwide have irregular sleep habits? These sleep issues don’t just leave us feeling tired — they affect our health, focus, and quality of life. Now, a new sleep algorithm developed by a team of Korean researchers is aiming to change that. And it’s available on Samsung Galaxy smartwatches around the world, including the newly launched Galaxy Watch8 series.
The personalized sleep guide, created by Professor Jae Kyoung Kim’s research team at KAIST and the Institute for Basic Science (IBS), doesn’t just tell you how long you slept. It actually recommends the best time for you to go to bed — helping you build healthy sleep habits and feel more refreshed every day.
What makes it special? Unlike most sleep features that focus only on the past (“You slept six hours last night”), this algorithm looks ahead. Using mathematical models and your body’s circadian rhythm, it suggests a personalized “sleep window” — like “Going to bed between 11:10 PM and 11:40 PM is ideal for you tonight.”
“It’s kind of like a weather forecast,” said Professor Kim. “Instead of just telling you what happened yesterday, it helps you prepare for tomorrow — so you can sleep better and feel better.”
<Conceptual Diagram of a Smart Sleep Algorithm>
The algorithm was developed over three years by a small team of mathematicians, not professional app developers. “We faced a lot of challenges trying to turn our research into a real product,” Kim admitted. “People kept asking us when they could try the algorithm, and we always felt bad that we couldn’t release it properly. Now, thanks to the support of KAIST’s Technology Commercialization Center and our partnership with Samsung, our work will finally reach people around the world.”
The academic world is paying attention, too. Professor Kim’s presentation on the algorithm was selected for the Hot Topics session at SLEEP 2025, the world’s largest sleep conference held in the U.S., and will also be featured at World Sleep 2025 in Singapore this fall.
Professor Kim is also working with Professor Eun Yeon Joo’s team at Samsung Medical Center to develop even more advanced sleep recommendation technology. Together, they created “SLEEPS,” an algorithm that predicts sleep disorders (available at sleep-math.com). Meanwhile, development continues on their own sleep app — with the hope of bringing math-powered sleep science into more people’s everyday lives.
Professor Kim is a world-renowned expert in mathematical biology. In 2025, he became the first Korean scientist to give a keynote speech at the SIAM Annual Meeting, and the first Korean to join the editorial board of SIAM Review, one of the most prestigious journals in applied mathematics. His work shows how basic science and mathematics can lead to real solutions that help people live healthier, better lives.
2025.07.28 View 487 -
Immune Signals Directly Modulate Brain's Emotional Circuits: Unraveling the Mechanism Behind Anxiety-Inducing Behaviors
KAIST's Department of Brain and Cognitive Sciences, led by Professor Jeong-Tae Kwon, has collaborated with MIT and Harvard Medical School to make a groundbreaking discovery. For the first time globally, their joint research has revealed that cytokines, released during immune responses, directly influence the brain's emotional circuits to regulate anxiety behavior.
The study provided experimental evidence for a bidirectional regulatory mechanism: inflammatory cytokines IL-17A and IL-17C act on specific neurons in the amygdala, a region known for emotional regulation, increasing their excitability and consequently inducing anxiety. Conversely, the anti-inflammatory cytokine IL-10 was found to suppress excitability in these very same neurons, thereby contributing to anxiety alleviation.
In a mouse model, the research team observed that while skin inflammation was mitigated by immunotherapy (IL-17RA antibody), anxiety levels paradoxically rose. This was attributed to elevated circulating IL-17 family cytokines leading to the overactivation of amygdala neurons.
Key finding: Inflammatory cytokines IL-17A/17C promote anxiety by acting on excitable amygdala neurons (via IL-17RA/RE receptors), whereas anti-inflammatory cytokine IL-10 alleviates anxiety by suppressing excitability through IL-10RA receptors on the same neurons.
The researchers further elucidated that the anti-inflammatory cytokine IL-10 works to reduce the excitability of these amygdala neurons, thereby mitigating anxiety responses.
This research marks the first instance of demonstrating that immune responses, such as infections or inflammation, directly impact emotional regulation at the level of brain circuits, extending beyond simple physical reactions. This is a profoundly significant achievement, as it proposes a crucial biological mechanism that interlinks immunity, emotion, and behavior through identical neurons within the brain.
The findings of this research were published in the esteemed international journal Cell on April 17th of this year.
Paper Information:
Title: Inflammatory and anti-inflammatory cytokines bidirectionally modulate amygdala circuits regulating anxiety
Journal: Cell (Vol. 188, 2190–2220), April 17, 2025
DOI: https://doi.org/10.1016/j.cell.2025.03.005
Corresponding Authors: Professor Gloria Choi (MIT), Professor Jun R. Huh (Harvard Medical School)
2025.07.24 View 466