Kaist

KAIST K HERO Rides Nuri Rocket, Next Generation Mi..

  < (From left) Ph.D candidate Jaehong Park, COSMOVY researcher Yoonsoo Kim, Professor Wonho Choe, Ph.D candidate Dongha Park, M.S candidate Seungbeom Heo >   KAIST announced on the November 26th that the CubeSat 'K-HERO (KAIST Hall Effect Rocket Orbiter)', developed by the research team of Professor Wonho Choe from the Department of Nuclear and Quantum Engineering, is scheduled to launch into space aboard the 4th Nuri rocket launch vehicle on November 27th from the Naro Space Center in Goheung, Jeollanam-do. This 4th Nuri launch is the first to be managed by the private company Hanwha Aerospace, which received technology transfer from the Korea Aerospace Research Institute (KARI), marking a significant milestone in the transformation of the domestic space industry. Along with the main payload, the Next-Generation Medium Satellite 3, twelve CubeSats developed by industry, academia, and research institutions will be onboard, with K-HERO being one of them. The development of K-HERO was officially initiated when Professor Wonho Choe's research team was selected as the basic satellite development team in the '2022 CubeSat Competition' organized by KARI. The basic satellite is a technology verification satellite designed to confirm whether the design and core components operate normally in the space environment before proceeding with the flight model (FM) production. K-HERO is a 3U standard CubeSat with dimensions of ＄10＼text{ cm}＄ (width) ＄＼times＄ ＄10＼text{ cm}＄ (length) ＄＼times＄ ＄30＼text{ cm}＄ (height) and a weight of ＄3.9＼text{ kg}＄. It was designed to satisfy all stability, electrical specifications, and interface conditions with the launch vehicle. The core mission of K-HERO is to directly verify the in-space operation of the 150 W class micro-satellite Hall thruster developed by the research team. The Hall thruster can be simply described as a 'space engine powered by electricity'. It is an electric propulsion engine that moves the satellite slowly but very efficiently using electricity. Instead of burning a lot of fuel to generate instantaneous thrust, like a rocket, it works by using electricity to turn gas (Xenon) into a plasma state and rapidly accelerating it backward to push the satellite forward. Hall thrusters are considered a core technology for the era of small and constellation satellites due to their high fuel efficiency. < Image of plasma generation in the micro-satellite Hall thruster mounted on the K-HERO CubeSat > Hall thrusters are already a proven technology, having been used in large satellites and deep-space probes for over 20-30 years. However, their size and power requirements were large, so in the past, they were mainly operated on large geostationary (GEO) communication/broadcasting satellites and used by NASA and ESA deep-space probes for long-distance flights. Recently, the emergence of the SpaceX Starlink satellite constellation has led to a surge in demand for small and micro electric thrusters. As the global space industry shifts towards satellite constellations, 'small and efficient thrusters' have become essential technology. K-HERO is the first case of direct in-space demonstration of a micro Hall thruster made with domestic technology, and it is expected to be an important milestone in enhancing domestic technological competitiveness. Professor Wonho Choe's research team began research on Hall thrusters in Korea in 2003, securing original technology based on plasma physics. In 2013, they successfully mounted a 200 W class Hall thruster on the 'KAIST Science and Technology Satellite 3,' proving its practical utility. This time, they have improved the design to operate even at a lower power of 30 W, developing a next-generation model aimed at micro-satellites. COSMOVY Inc, a laboratory startup founded by Professor Wonho Choe's research team, also participated in the development of K-HERO, further strengthening the foundation for technology commercialization. < K-HERO CubeSat being loaded into the Nuri rocket's CubeSat dispenser (Photo source: Korea Aerospace Research Institute) > Professor Wonho Choe stated, "Starting with K-HERO, the number of small satellites equipped with electric thrusters will increase significantly in Korea. The Hall thruster being verified this time can be utilized for various missions, including low-Earth orbit constellation surveillance and reconnaissance satellites, 6G communication satellites, very-low-Earth orbit high-resolution satellites, and asteroid probes." President Kwang Hyung Lee stated, "The launch of K-HERO is a significant opportunity to directly verify KAIST's electric propulsion technology on a micro-satellite platform once again in space, and it will be an important turning point that will further enhance the technological competitiveness of small satellites in Korea. KAIST will continue to contribute to the development of our country's space technology.    

How Does AI Think？ KAIST Achieves First Visualizat..

  <(From Left) Ph.D candidate Daehee Kwon, Ph.D candidate Sehyun lee, Professor Jaesik Choi>   Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge. KAIST (President Kwang Hyung Lee) announced on the 26th of November that Professor Jaesik Choi’s research team at the Kim Jaechul Graduate School of AI has developed a new explainable AI (XAI) technology that visualizes the concept-formation process inside a model at the level of circuits, enabling humans to understand the basis on which AI makes decisions. The study is evaluated as a significant step forward that allows researchers to structurally examine “how AI thinks.” Inside deep learning models, there exist basic computational units called neurons, which function similarly to those in the human brain. Neurons detect small features within an image—such as the shape of an ear, a specific color, or an outline—and compute a value (signal) that is transmitted to the next layer. In contrast, a circuit refers to a structure in which multiple neurons are connected to jointly recognize a single meaning (concept). For example, to recognize the concept of cat ear, neurons detecting outline shapes, neurons detecting triangular forms, and neurons detecting fur-color patterns must activate in sequence, forming a functional unit (circuit). Up until now, most explanation techniques have taken a neuron-centric approach based on the idea that “a specific neuron detects a specific concept.” However, in reality, deep learning models form concepts through cooperative circuit structures involving many neurons. Based on this observation, the KAIST research team proposed a technique that expands the unit of concept representation from “neuron → circuit.” The research team’s newly developed technology, Granular Concept Circuits (GCC), is a novel method that analyzes and visualizes how an image-classification model internally forms concepts at the circuit level. GCC automatically traces circuits by computing Neuron Sensitivity and Semantic Flow. Neuron Sensitivity indicates how strongly a neuron responds to a particular feature, while Semantic Flow measures how strongly that feature is passed on to the next concept. Using these metrics, the system can visualize, step-by-step, how basic features such as color and texture are assembled into higher-level concepts. The team conducted experiments in which specific circuits were temporarily disabled (ablation). As a result, when the circuit responsible for a concept was deactivated, the AI’s predictions actually changed. In other words, the experiment directly demonstrated that the corresponding circuit indeed performs the function of recognizing that concept. This study is regarded as the first to reveal, at a fine-grained circuit level, the actual structural process by which concepts are formed inside complex deep learning models. Through this, the research suggests practical applicability across the entire explainable AI (XAI) domain—including strengthening transparency in AI decision-making, analyzing the causes of misclassification, detecting bias, improving model debugging and architecture, and enhancing safety and accountability. The research team stated, “This technology shows the concept structures that AI forms internally in a way that humans can understand,” adding that “this study provides a scientific starting point for researching how AI thinks.” Professor Jaesik Choi emphasized, “Unlike previous approaches that simplified complex models for explanation, this is the first approach to precisely interpret the model’s interior at the level of fine-grained circuits,” and added, “We demonstrated that the concepts learned by AI can be automatically traced and visualized.” < external_image >   < Overview of the Conceptual Circuit Proposed by the Research Team > This study, with Ph.D. candidates Dahee Kwon and Sehyun Lee from KAIST Kim Jaechul Graduate School of AI as co–first authors, was presented on October 21 at the International Conference on Computer Vision (ICCV). Paper title: Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations Paper link: https://openaccess.thecvf.com/content/ICCV2025/papers/Kwon_Granular_Concept_Circuits_Toward_a_Fine-Grained_Circuit_Discovery_for_Concept_ICCV_2025_paper.pdf This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP) under the “Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation” project, the AI Research Hub Project, and the KAIST AI Graduate School Program, and was carried out with support from the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD) at the KAIST Center for Applied Research in Artificial Intelligence.        

KAIST Confirms Reduction of Amyloid-β Using Red OL..

  <Professor Kyung Cheol Choi, Dr. Byeongju Noh, Ph.D candidate Young-Hun Jung, Ph.D candidate Minwoo Park, Dr.Ja Wook Koo, Researcher Jiyun Lee, Researcher Ji-Eun Lee, Dr. Hyang Sook Hoe, Dr.  Hyun-Ju Lee, Dr. Sora Kang, Researcher Seokjun Oh>     A Korean research team, raising the question “Which OLED light color can actually improve memory and pathological markers in Alzheimer’s patients?”, has identified the most effective OLED color capable of enhancing cognitive function using only light—with no drugs involved. The OLED platform developed for this study can precisely control color, brightness, flicker frequency, and exposure duration, suggesting potential future development into personalized OLED-based electroceuticals. On the 24th, KAIST (President Kwang Hyung Lee) announced that a joint research team led by Professor Kyung Cheol Choi from the School of Electrical Engineering at KAIST and Dr. Ja Wook Koo and Dr. Hyang Sook Hoe from the Korea Brain Research Institute (KBRI) developed a uniform-illuminance, three-color OLED photostimulation technology and confirmed that “red 40-Hz light” was the most effective among blue, green, and red in improving Alzheimer's pathology and memory function. To overcome the structural limitations of conventional LEDs—such as brightness imbalance, heat generation risk, and variability caused by animal movement—the researchers developed an OLED-based photostimulation platform that emits light uniformly. Using this platform, they compared white, red, green, and blue light under identical conditions (40-Hz frequency, brightness, and exposure time) and found that red 40-Hz light produced the most significant improvement. In an early-stage (3-month-old) Alzheimer’s animal model, improvement in pathology and memory was observed after only two days of stimulation. When early Alzheimer’s model mice were exposed to one hour of light per day for two days, both white and red light improved long-term memory. Additionally, the amount of amyloid-β (Aβ) plaques—protein aggregates known as a major factor in Alzheimer’s disease—was reduced in key brain regions such as the hippocampus, and levels of the plaque-clearing enzyme ADAM17 increased. This indicates that even very short periods of light stimulation can reduce harmful proteins in the brain and improve memory function. In particular, with red light, the inflammatory cytokine IL-1β, known to exacerbate inflammation and contribute to Alzheimer’s progression, decreased significantly, demonstrating an anti-inflammatory effect. Moreover, the more plaque was reduced, the greater the improvement in memory—direct evidence that pathological improvement leads to cognitive enhancement. In the mid-stage (6-month-old) Alzheimer’s model, statistically significant pathological improvement was seen only with red light. In a two-week long-term stimulation experiment under the same conditions, both white and red light improved memory, but a statistically meaningful reduction in plaques appeared only under red light. < The mechanism by which red OLED stimulation of neurons reduces amyloid-β in Alzheimer’s model mice > Differences at the molecular level were also clear. Under red light, levels of ADAM17 (which helps remove plaques) increased, while levels of BACE1, an enzyme responsible for producing plaques, decreased—demonstrating a dual effect of both inhibiting plaque formation and promoting plaque removal. In contrast, white light only lowered BACE1, showing more limited therapeutic effects compared to red light. This scientifically identifies that the color of light is a key factor determining therapeutic efficacy. To determine which neural circuits were activated by light stimulation, the team analyzed the expression of c-Fos, an immediate-early gene that is activated when neurons fire. They found activation throughout the visual–memory circuit, extending from the visual cortex → thalamus → hippocampus, providing direct neurological evidence that light stimulation awakens the visual pathway, enhancing hippocampal function and memory. Thanks to the uniform-illuminance OLED platform, light was evenly delivered regardless of animal movement, ensuring stable experimental results and high reproducibility across repeated tests. This study is the first to demonstrate that cognitive function can be improved using only light, without drugs, and that Alzheimer’s pathological markers can be regulated through combinations of light color, frequency, and duration. The OLED platform developed in this study allows fine control over color, brightness, flicker ratio, and exposure time, making it suitable for personalized stimulation design in future human clinical research. The research team plans to expand conditions such as stimulation intensity, energy, duration, and combined visual–auditory stimulation, aiming toward clinical-stage development. < Graphical abstract for the journal ACS Biomaterials Science & Engineering – Illustration of the mechanism by which red OLED stimulation reduces amyloid-β > Dr. Byeongju Noh (from Professor Kyung Cheol Choi’s research team) said, “This study experimentally demonstrates the importance of color standardization and confirms that red OLED is the key color that activates ADAM17 and suppresses BACE1 across disease stages.” Professor Kyung Cheol Choi emphasized, “Our uniform-illuminance OLED platform overcomes the structural limitations of traditional LEDs and enables high reproducibility and safe evaluation. We expect wearable RED OLED electroceuticals for everyday use to present a new therapeutic paradigm for Alzheimer’s disease.” The research findings were published online on October 25 in ACS Biomaterials Science & Engineering, a leading international journal in biomedical and materials science. Paper Title: Color Dependence of OLED Phototherapy for Cognitive Function and Beta-Amyloid Reduction through ADAM17 and BACE1 DOI: https://pubs.acs.org/doi/full/10.1021/acsbiomaterials.5c01162 Co-authors: Byeongju Noh, Hyun-Ju Lee, Jiyun Lee, Jiyun Lee, Ji-Eun Lee, Bitna Joo, Young-Hun Jung, Minwoo Park, Sora Kang, Seokjun Oh, Jeong-Woo Hwang, Dae-Si Kang, Yongmin Jeon, So-Min Lee, Hyang Sook Hoe, Ja Wook Koo, Kyung Cheol Choi This research was supported by the National Research Foundation of Korea and the National IT Industry Promotion Agency under the Ministry of Science and ICT, and the Korea Brain Research Institute Basic Research Program. (2017R1A5A1014708, 2022M3E5E9018226, H0501-25-1001, 25-BR-02-02, 25-BR-02-04)    

A KAIST team develops the world＇s first modular co..

  <(From Left) Distinguished Professor Sang Yup Lee, Ph.D candidate Pingxin Lin, Ph.D candiate Zhou Hengrui>       The integration of systems metabolic engineering with co-culture strategies that couples bacterial cellulose production with natural colorant biosynthesis enabled the one-pot generation of rainbow-colored bacterial cellulose, establishing a sustainable biomanufacturing platform that can replace petroleum-based textiles and eliminate chemical dyeing processes.   A research group at KAIST has successfully developed a modular co-culture platform for the one-pot production of rainbow-colored bacterial cellulose. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, engineered Komagataeibacter  xylinus for bacterial cellulose synthesis and Escherichia coli for natural colorants overproduction. A co-culture of these engineered strains enabled the in situ coloration of bacterial cellulose. This research offers a versatile platform for producing living materials in multiple colors, and provides new opportunities for sustainable textiles, wearable biomaterials, and functional living materials that combine optical and structural properties beyond the reach of conventional textile technologies.   Bacterial cellulose is an attractive and biodegradable alternative to petroleum-derived fabrics due to its high purity, mechanical strength, and water-retention properties. However, the limited color range of bacterial cellulose, which is typically white, has limited its broader application in the textile industry, where more vibrant colored fabrics are increasingly desired. Conventional dyeing methods rely on petroleum-based colorants and toxic reagents, creating environmental and processing challenges. These challenges have driven the demand for alternative production methods.   To address these issues, KAIST researchers, including Ph.D. Candidate Hengrui Zhou, Ph.D. Candidate Pingxin Lin, Professor Ki Jun Jeong, and Distinguished Professor Sang Yup Lee, combined systems metabolic engineering with co-culture strategies to develop a bio-based route that integrates bacterial cellulose formation with natural pigment synthesis, enabling the production of colored living materials in a single step without additional chemical processing.   The team’s work, entitled “One-pot production of colored bacterial cellulose,” was published in Trends in Biotechnology on November 12,2025. This research details the one-pot production of multicolored bacterial cellulose using a modular co-culture platform that integrates a bacterial cellulose-overproducing K. xylinus strain with natural colorant-producing E. coli strains. The team focused on addressing the limitations in bacterial cellulose coloration caused by environmental challenges and complex processing requirements. By employing vesicle engineering and optimizing co-culture parameters, the researchers achieved one-pot production of red, orange, yellow, green, blue, navy, and purple bacterial cellulose, eliminating the need for external dyes and toxic chemical treatments.   To enhance dyeing efficiency, E. coli strains were engineered for the overproduction and secretion of natural colorants. It was determined that the intracellular accumulation of these pigments disrupts cellular metabolism and physiology, thereby inhibiting their production. To overcome this limitation, vesicle engineering has emerged as a key strategy to mitigate these cytotoxic effects, including the induction of inner- and outer-membrane vesicles and the modulation of cell morphology, enabling the more efficient secretion of colorants and increased overall production. The engineered E. coli strains were optimized in fed-batch fermentation, achieving record-breaking production of 16.92 ± 0.10 g/L of deoxyviolacein, 8.09 ± 0.17 g/L of violacein, 1.82 ± 0.07 g/L of proviolacein, and 936.25 ± 9.70 mg/L of prodeoxyviolacein, the highest reported titers to date for all four violacein derivatives. < Figure 1. Rainbow-colored bacterial cellulose (microbial fiber) with applied color >   A co-culture platform combining the K. xylinus with E. coli strains was further developed and optimized, enabling the in situ one-pot coloration of bacterial cellulose in vibrant green, blue, navy, and purple. Fed-batch fermentation further improved the performance of the platform, achieving the world-first one-pot production of multicolored bacterial cellulose on a larger scale. To expand the bacterial cellulose color palette, engineered carotenoid-producing E. coli strains were incorporated, enabling the successful synthesis of red, orange, and yellow bacterial cellulose. This milestone demonstrates the potential of microbial fermentation as a sustainable alternative to petroleum-based textile processes.   “We can anticipate that this microbial cell factory-based one-pot production of rainbow-colored bacterial cellulose has the potential to replace current petroleum-based textile processes,” said Ph.D. Candidate Hengrui Zhou. “The systems metabolic engineering strategies developed in this study could be broadly applied for the production of diverse sustainable textiles, wearable biomaterials, and functional living materials that combine optical and structural properties beyond the capabilities of conventional textile technologies.” He added, “This platform reduces the environmental impact while greatly expanding design possibilities. Beyond serving as a proof-of-concept, this technology offers a promising route toward scalable, eco-friendly fabrics with in situ coloration. Its modular design allows the incorporation of diverse natural colorant pathways, enabling the creation of living materials in multiple colors.”   < Figure 2. Schematic of a microbe-based platform for one-step production of rainbow-colored bacterial cellulose > “As demand for sustainable textiles and living materials continues to grow, we expect that the integrated biomanufacturing platform developed here will play a pivotal role in producing diverse functional biomaterials with additional design possibilities in a single step, without additional chemical processing,” explained Distinguished Professor Sang Yup Lee.   This work was supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project (2022M3J5A1056072) and the Development of Platform Technologies of Microbial Cell Factories for the Next-generation Biorefineries project (2022M3J5A1056117) from the National Research Foundation supported by the Korean Ministry of Science and ICT.     Source: Hengrui Zhou (1st), Pingxin Lin (2nd), Ki Jun Jeong (3rd), and Sang Yup Lee (Corresponding). “One-pot production of colored bacterial cellulose”. Trends in Biotechnology (Published) doi: 10.1016/j.tibtech.2025.09.019      

Makes Summer Cooler and Winter Warmer Without Powe..

<(Front row from left)Professor Young Min Song, Ph.D candidate Hyung Rae Kim, M.S candidate Hyunkyu Kwak, (Back row from left)Ph.D candidate Hyo Eun Jeong, Dr. Sehui Chang, Ph.D candidate Do Hyeon Kim, (Circle from left) Professor Dae-Hyeong Kim, Dr. Yoonsoo Shin, Dr. Se-Yeon Heo>     The poplar (Populus alba) has a unique survival strategy: when exposed to hot and dry conditions, it curls its leaves to expose the ventral surface, reflecting sunlight, and at night, the moisture condensed on the leaf surface releases latent heat to prevent frost damage. Plants have evolved such intricate mechanisms in response to dynamic environmental fluctuations in diurnal and seasonal temperature cycles, light intensity, and humidity, but there have been few instances of realizing such a sophisticated thermal management system with artificial materials. Through this research, the KAIST research team has developed an artificial material that mimics the thermal management strategy of the poplar leaf, significantly increasing the applicability of power-free, self-regulating thermal management technology in applications such as building facades, roofs, and temporary shelters. KAIST announced on November 18 that the research team led by Professor Young Min Song of the School of Electrical Engineering, in collaboration with Professor Dae-Hyeong Kim’s team at Seoul National University, has developed a flexible hydrogel-based ‘Latent-Radiative Thermostat (LRT)’ that mimics the natural heat regulation strategy of the poplar leaf. The LRT developed by the research team is a bio-inspired thermal regulator that autonomously switches between cooling and heating modes. This technology is a new thermal management technique that can simultaneously realize latent heat regulation through the evaporation and condensation of water, and radiative heat regulation using light reflection and transmission, all within a single device. The primary functional material is a composite that integrates lithium ions (Li＋) and hydroxypropyl cellulose (HPC) within a polyacrylamide (PAAm) hydrogel. Li＋ maintains warmth by condensing and absorbing moisture to regulate latent heat, and HPC changes between transparent and opaque states according to temperature changes, regulating the reflection and absorption of sunlight to switch between cooling and heating modes. When the temperature rises, HPC molecules aggregate, causing the hydrogel to become opaque, which reflects sunlight and strengthens the natural cooling effect. The resulting LRT automatically switches among four thermal management modes based on the surrounding temperature, humidity, and sunlight. < external_image >  

KAIST Develops Wearable Ultrasound Sensor Enabling..

<(From Left) Professor Hyunjoo Jenny Lee, Dr.Sang-Mok Lee, Ph.D candidate Xiaojia Liang>       Conventional wearable ultrasound sensors have been limited by low power output and poor structural stability, making them unsuitable for high-resolution imaging or therapeutic applications. A KAIST research team has now overcome these challenges by developing a flexible ultrasound sensor with statically adjustable curvature. This breakthrough opens new possibilities for wearable medical devices that can capture precise, body-conforming images and perform noninvasive treatments using ultrasound energy. KAIST (President Kwang Hyung Lee) announced on November 12 that a research team led by Professor Hyunjoo Jenny Lee from the School of Electrical Engineering developed a “flex-to-rigid (FTR)” capacitive micromachined ultrasonic transducer (CMUT) capable of transitioning freely between flexibility and rigidity using a semiconductor wafer process (MEMS). The team incorporated a low-melting-point alloy (LMPA) inside the device. When an electric current is applied, the metal melts, allowing the structure to deform freely; upon cooling, it solidifies again, fixing the sensor into the desired curved shape. Conventional polymer-membrane-based CMUTs have suffered from a low elastic modulus, resulting in insufficient acoustic power and blurred focal points during vibration. They have also lacked curvature control, limiting precise focusing on target regions. Professor Lee’s team designed an FTR structure that combines a rigid silicon substrate with a flexible elastomer bridge, achieving both high output performance and mechanical flexibility. The embedded LMPA enables dynamic adjustment and fixation of the transducer’s shape by toggling between solid and liquid states through electrical control. As a result, the new sensor can automatically focus ultrasound on a specific region according to its curvature—without requiring separate beamforming electronics—and maintains stable electrical and acoustic performance even after repeated bending. The device’s acoustic output reaches the level of low-intensity focused ultrasound (LIFU), which can gently stimulate tissues to induce therapeutic effects without causing damage. Experiments on animal models demonstrated that noninvasive spleen stimulation reduced inflammation and improved mobility in arthritis models. In the future, the team plans to extend this technology to a two-dimensional (2D) array structure—arranging multiple sensors in a grid—to enable simultaneous high-resolution ultrasound imaging and therapeutic applications, paving the way for a new generation of smart medical systems. Because the technology is compatible with semiconductor fabrication processes, it can be mass-produced and adapted for wearable and home-use ultrasound systems. This study was conducted by Sang-Mok Lee, Xiaojia Liang (co–first authors), and their collaborators under the supervision of Professor Hyunjoo Jenny Lee. The results were published online on October 23 in npj Flexible Electronics (Impact Factor: 15.5). Paper title: “Flexible ultrasound transducer array with statically adjustable curvature for anti-inflammatory treatment” DOI: [10.1038/s41528-025-00484-7] The research was supported by the Bio & Medical Technology Development Program (Brain Science Convergence Research Program) of the Ministry of Science and ICT (MSIT) and the Korea Medical Device Development Fund, a multi-ministerial R&D initiative.      

Automatic C to Rust Translation Technology Gains G..

<(From Left) Professor Sukyoung Ryu, Researcher  Jaemin Hong>     As the C language, which forms the basis of critical global software like operating systems, faces security limitations, KAIST's research team is pioneering core original technology research for the accurate automatic conversion to Rust to replace it. By proving the mathematical correctness of the conversion, a limitation of existing Artificial Intelligence (LLM) methods, and solving C language security issues through automatic conversion to Rust, they presented a new direction and vision for future software security research. This work has been selected as the cover story for CACM, the world's highest-authority academic journal, thereby demonstrating KAIST's global research leadership in the field of computer science. KAIST announced on the 9th of November that the paper by Professor Sukyoung Ryu's research team (Programming Language Research Group) from the School of Computing was selected as the cover story for the November issue of CACM (Communications of the ACM), the highest authority academic journal published by ACM (Association for Computing Machinery), the world's largest computer society. <Photo of the Paper Selected for the Cover of Communications of the ACM> This paper comprehensively addresses the technology developed by Professor Sukyoung Ryu's research team for the automatic conversion of C language to Rust, and it received high acclaim from the international research community for presenting the technical vision and academic direction this research should pursue in the future. The C language has been widely used in the industry since the 1970s, but its structural limitations have continuously caused severe bugs and security vulnerabilities. Rust, on the other hand, is a secure programming language developed since 2015, used in the development of operating systems and web browsers, and has the characteristic of being able to detect and prevent bugs before program execution. The US White House recommended discontinuing the use of C language in a technology report released in February 2024, and the Defense Advanced Research Projects Agency (DARPA) also explicitly stated that Rust is the core alternative for resolving C language security issues by promoting a project to develop technology for the automatic conversion of C code to Rust. Professor Sukyoung Ryu's research team proactively raised the issues of C language safety and the importance of automatic conversion even before these movements began in earnest, and they have continuously developed core related technologies. In May 2023, the research team presented the Mutex conversion technology (necessary for program synchronization) at ICSE (International Conference on Software Eng), the top authority conference in software engineering. In June 2024, they presented the Output Parameter conversion technology (used for result delivery) at PLDI (Programming Language Design and Implementation), the top conference in programming languages, and in October of the same year, they presented the Union conversion technology (for storing diverse data together) at ASE (Automated Software Eng), the representative conference in software automation. These three studies are all "world-first" achievements presented at top-tier international academic conferences, successfully implementing automatic conversion technology for each feature with high completeness. Since 2023, the research team has consistently published papers in CACM every year, establishing themselves as global leading researchers who consistently solve important and challenging problems worldwide. This paper was published in CACM (Communications of the ACM) on October 24, with Dr. Jaemin Hong (Postdoctoral Research Fellow at KAIST Information and Electronics Research Institute) as the first author. ※Paper Title: Automatically Translating C to Rust, DOI: https://doi.org/10.1145/3737696 Dr. Jaemin Hong stated, "The conversion technology we developed is an original technology based on programming language theory, and its biggest strength is that we can logically prove the 'correctness' of the conversion." He added, "While most research relies on Large Language Models (LLMs), our technology can mathematically guarantee the correctness of the conversion." Dr. Hong is scheduled to be appointed as an Assistant Professor in the Computer Science Department at UNIST starting in March 2025. Furthermore, Professor Ryu's research team has four papers accepted for presentation at ASE 2025, the highest-authority conference in software engineering, including C→Rust conversion technology. These papers, in addition to automatic conversion technology, cover various cutting-edge software engineering fields and are receiving high international acclaim. They include: technology to verify whether quantum computer programs operate correctly, 'WEST' technology that automatically checks the correctness of WebAssembly programs (technology for fast and efficient program execution on the web) and creates tests for them, and technology that automatically simplifies complex WebAssembly code to quickly find errors. Among these, the WEST paper received the Distinguished Paper Award. This research was supported by the Leading Research Center/Mid-career Researcher Support Program of the National Research Foundation of Korea, the Institute of Information & Communications Technology Planning & Evaluation (IITP), and Samsung Electronics.  

KAIST＇s ＇FluidGPT＇ Wins Grand Prize at the 2025 AI..

<Commemorative Photo After Winning at the 2025 AI Champions Award Ceremony>   The era has begun where an AI assistant goes beyond simple conversation to directly view the screen, make decisions, and complete tasks such as hailing a taxi or booking an SRT ticket. KAIST (President Kwang Hyung Lee) announced on the 6th that the AutoPhone Team (Fluidez, KAIST, Korea University, Sungkyunkwan University), led by Professor Insik Shin (CEO of Fluidez Co., Ltd.) of the School of Computing, was selected as the inaugural AI Champion (1st place) in the '2025 Artificial Intelligence Champion (AI Champion) Competition,' hosted by the Ministry of Science and ICT. This competition is the nation's largest AI technology contest, comprehensively evaluating the innovativeness, social impact, and commercial potential of AI technology. With 630 teams participating nationwide, the AutoPhone Team claimed the top honor and will receive 3 billion Korean won in research and development funding. The technology developed by the AutoPhone Team, 'FluidGPT,' is a fully autonomous AI agent that understands a user's voice command and enables the smartphone to independently run apps, click, input, and even complete payments. For example, when a user says, "Book an SRT ticket from Seoul Station to Busan," or "Call a taxi," FluidGPT opens the actual app and sequentially performs the necessary steps to complete the request. The core of this technology is its 'Non-Invasive (API-Free)' structure. Previously, calling a taxi using an app required directly connecting to the app's internal system (API communication) through the taxi app's API. In contrast, this technology does not modify the existing app's code or link an API. Instead, the AI directly recognizes and operates the screen (UI), acquiring the ability to use the smartphone just like a human. As a result, FluidGPT presents a new paradigm—"AI that sees, judges, and moves a hand on behalf of a person"—and is evaluated as a core technology that will usher in the 'AI Phone Era.' FluidGPT moves beyond simple voice assistance to implement the concept of 'Agentic AI' (Action-Oriented Artificial Intelligence), where the AI directly views the screen, makes decisions, and takes action. As a fully action-oriented system, the AI clicks app buttons, fills in input fields, and references data to autonomously achieve the user's objective, foreshadowing an innovation in how smartphones are used. Professor In-sik Shin of the School of Computing shared his thoughts, stating, "AI is now evolving from conversation to action. FluidGPT is a technology that understands the user's words and autonomously executes actual apps, and it will be the starting point of the 'AI Phone Era.' The AutoPhone Team possesses world-class research capabilities, and we will contribute to the widespread adoption of AI services that everyone can easily use." KAIST President Kwang Hyung Lee remarked, "This achievement is a representative example that demonstrates KAIST's vision for AI convergence," adding, "AI technology is entering the daily lives of citizens and leading a new wave of innovation." He further added, "KAIST will continue to lead research in future core technologies such as AI and semiconductors to bolster national competitiveness."      

“AI,” the New Language of Materials Science and En..

<(From Left)  M.S candidate Chaeyul Kang, Professor Seumgbum Hong, Ph. D candidate Benediktus Madika, Ph.D candidate Batzorig Buyantogtokh, Ph.D candiate Aditi Saha, > Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee). Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization The era has arrived in which artificial intelligence (AI) autonomously imagines and predicts the structures and properties of new materials. Today, AI functions as a researcher’s “second brain,” actively participating in every stage of research, from idea generation to experimental validation. KAIST (President Kwang Hyung Lee) announced on October 26 that a comprehensive review paper analyzing the impact of AI, Machine Learning (ML), and Deep Learning (DL) technologies across materials science and engineering has been published in ACS Nano (Impact Factor = 18.7). The paper was co-authored by Professor Seungbum Hong and his team from the Department of Materials Science and Engineering at KAIST, in collaboration with researchers from Drexel University, Northwestern University, the University of St Andrews, and the University of Tennessee in the United States. The research team proposed a full-cycle utilization strategy for materials innovation through an AI-based catalyst search platform, which embodies the concept of a Self-Driving Lab—a system in which robots autonomously perform materials synthesis and optimization experiments. Professor Hong’s team categorized materials research into three major stages—Discovery, Development, and Optimization—and detailed the distinctive role of AI in each phase: In the Discovery Stage, AI designs new structures, predicts properties, and rapidly identifies the most promising materials among vast candidate pools. In the Development Stage, AI analyzes experimental data and autonomously adjusts experimental processes through Self-Driving Lab systems, significantly shortening research timelines. In the Optimization Stage, AI employs Reinforcement Learning, which identifies optimal conditions through Bayesian Optimization, which efficiently finds superior results with minimal experimentation, to fine-tune designs and process conditions for maximum performance. In essence, AI serves as a “smart assistant” that narrows down the most promising materials, reduces experimental trial and error, and autonomously optimizes experimental conditions to achieve the best-performing outcomes. The paper further highlights how cutting-edge technologies such as Generative AI, Graph Neural Networks (GNNs), and Transformer models are transforming AI from a computational tool into a “thinking researcher.” Nonetheless, the team cautions that AI’s predictions are not error-proof and that key challenges persist, such as imbalanced data quality, limited interpretability of AI predictions, and integration of heterogeneous datasets. To address these limitations, the authors emphasize the importance of developing AI systems capable of autonomously understanding physical principles and ensuring transparent, verifiable decision-making processes for researchers. The review also explores the concept of the Self-Driving Lab, where AI autonomously designs experimental plans, analyzes results, and determines the next experimental steps—without manual operation by researchers. The AI-Based Catalyst Search Platform exemplifies this concept, enabling robots to automatically design, execute, and optimize catalyst synthesis experiments. In particular, the study presents cases in which AI-driven experimentation has dramatically accelerated catalyst development, suggesting that similar approaches could revolutionize research in battery and energy materials. <AI Driving Innovation Across the Entire Cycle of New Material Discovery, Development, and Optimization> “This review demonstrates that artificial intelligence is emerging as the new language of materials science and engineering, transcending its role as a mere tool,” said Professor Seungbum Hong. “The roadmap presented by the KAIST team will serve as a valuable guide for researchers in Korea’s national core industries including batteries, semiconductors, and energy materials.” Benediktus Madika (Ph.D. candidate), Aditi Saha (Ph.D. candidate), Chaeyul Kang (M.S. candidate), and Batzorig Buyantogtokh (Ph.D. candidate) from KAIST’s Department of Materials Science and Engineering contributed as co-first authors. Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee). Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization DOI: 10.1021/acsnano.5c04200 This work was supported by the National Research Foundation of Korea (NRF) with funding from the Ministry of Science and ICT (RS-2023-00247245).

KAIST, Dancing Like ＇Navillera＇... AI Understands ..

<(From Left)Ph.D candidate Jihyun Lee, Professor Tae-Kyun Kim, M.S candidate Changmin Lee> The era has begun where AI moves beyond merely 'plausibly drawing' to understanding even why clothes flutter and wrinkles form. A KAIST research team has developed a new generative AI that learns movement and interaction in 3D space following physical laws. This technology, which overcomes the limitations of existing 2D-based video AI, is expected to enhance the realism of avatars in films, the metaverse, and games, and significantly reduce the need for motion capture or manual 3D graphics work. KAIST (President Kwang Hyung Lee) announced on the 22nd that the research team of Professor Tae-Kyun (T-K) Kim from the School of Computing has developed 'MPMAvatar,' a spatial and physics-based generative AI model that overcomes the limitations of existing 2D pixel-based video generation technology. To solve the problems of conventional 2D technology, the research team proposed a new method that reconstructs multi-view images into 3D space using Gaussian Splatting and combines it with the Material Point Method (MPM), a physics simulation technique. In other words, the AI was trained to learn physical laws on its own by stereoscopically reconstructing videos taken from multiple viewpoints and allowing objects within that space to move and interact as if they were in real physical world. This enables the AI to compute the movement based on objects' material, shape, and external forces, and then learn the physical laws by comparing the results with actual videos. The research team represented the 3D space using point-units, and by applying both Gaussian and MPM to each point, they simultaneously achieved physically natural movement and realistic video rendering. That is, they divided the 3D space into numerous small points, making each point move and deform like a real object, thereby realizing natural video that is nearly indistinguishable from reality. In particular, to precisely express the interaction of thin and complex objects like clothing, they calculated both the object's surface (mesh) and its particle-unit structure (point), and utilized the Material Point Method (MPM), which calculates the object's movement and deformation in 3D space according to physical laws. Furthermore, they developed a new collision handling technology to realistically reproduce scenes where clothes or objects move and collide with each other in multiple spots and complex manner. The generative AI model MPMAvatar, to which this technology is applied, successfully reproduced the realistic movement and interaction of a person wearing loose clothing, and also succeeded in 'Zero-shot' generation, where the AI processes data it has never seen during the learning process by inferring on its own. <Figure 1. Modeling new human poses and clothing dynamics from multi-view video input, and zero-shot generation of novel physical interactions.> The proposed method is applicable to various physical properties, such as rigid bodies, deformable objects, and fluids, allowing it to be used not only for avatars but also for the generation of general complex scenes. <“Figure 2. Depiction of graceful dance movements and soft clothing folds, like Navillera.> Professor Tae-Kyun (T-K) Kim explained, "This technology goes beyond AI simply drawing a picture; it makes the AI understand 'why' the world in front of it looks the way it does. This research demonstrates the potential of 'Physical AI' that understands and predicts physical laws, marking an important turning point toward AGI (Artificial General Intelligence)." He added, "It is expected to be practically applied across the broaden immersive content industry, including virtual production, films, short-form contents, and adverts, creating significant change." The research team is currently expanding this technology to develop a model that can generate physically consistent 3D videos simply from a user's text input. This research involved Changmin Lee, a Master's student at the KAIST Graduate School of AI, as the first author, and Jihyun Lee, a Ph.D. student at the KAIST School of Computing, as a co-author. The research results will be presented at NeurIPS, the most prestigious international academic conference in the field of AI, on December 2nd, and the program code is to be fully released. ·         Paper: C. Lee, J. Lee, T-K. Kim, MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics, Proc. of Thirty-Ninth Annual Conf. on Neural Information Processing Systems (NeurIPS), San Diego, US, 2025 ·         arXiv version: https://arxiv.org/abs/2510.01619 ·         Related Project Site: https://kaistchangmin.github.io/MPMAvatar/ ·         Related video links showing the 'Navillera'-like dancing drawn by AI: o    https://www.youtube.com/shorts/ZE2KoRvUF5c o    https://youtu.be/ytrKDNqACqM This work was supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) through the Human-Oriented Next-Generation Challenging General AI Technology Development Project (RS-2025-25443318) and the Professional AI Talent Development Program for Multimodal AI Agents (RS-2025-25441313).

Refrigerator Use Increases with Stress, IoT Sensor..

<(From Left) Ph.D candidate Chanhee Lee, Professor Uichin Lee, Professor Hyunsoo Lee, Ph.D candidate Youngji Koh from School of Computing> The number of single-person households in South Korea has exceeded 8 million, accounting for 36％ of the total, marking an all-time high. A Seoul Metropolitan Government survey found that 62％ of single-person households experience 'loneliness', deepening feelings of isolation and mental health issues. KAIST researchers have gone beyond the limitations of smartphones and wearables, utilizing in-home IoT data to reveal that a disruption in daily rhythm is a key indicator of worsening mental health. This research is expected to lay the foundation for developing personalized mental healthcare management systems. KAIST (President Kwang Hyung Lee) announced on the 21st of October that a research team led by Professor Uichin Lee from the School of Computing has demonstrated the possibility of accurately tracking an individual's mental health status using in-home Internet of Things (IoT) sensor data. Consistent self-monitoring is important for mental health management, but existing smartphone- or wearable-based tracking methods have the limitation of data loss when the user is not wearing or carrying the device inside the home. The research team therefore focused on in-home environmental data. A 4-week pilot study was conducted on 20 young single-person households, installing appliances, sleep mats, motion sensors, and other devices to collect IoT data, which was then analyzed along with smartphone and wearable data. The results confirmed that utilizing IoT data alongside existing methods allows for a significantly more accurate capture of changes in mental health. For instance, reduced sleep time was closely linked to increased levels of depression, anxiety, and stress, and increased indoor temperature also showed a correlation with anxiety and depression. <Picture1. Heatmap of the Correlation Between Each User’s Mental Health Status and Sensor Data> Participants' behavioral patterns varied, including a 'binge-eating type' with increased refrigerator use during stress and a 'lethargic type' with a sharp decrease in activity. However, a common trend clearly emerged: mental health deteriorated as daily routines became more irregular. Variability in daily patterns was confirmed to be a more important factor than the frequency of specific behaviors, suggesting that a regular routine is essential for maintaining mental health. When research participants viewed their life data through visualization software, they generally perceived the data as being genuinely helpful in understanding their mental health, rather than expressing concern about privacy invasion. This significantly enhanced the research acceptance and satisfaction with participation. <Figure 2. Comparison of Average Mental Health Status Between the High Irregularity Group (Red) and the Low Irregularity Group (Blue)>

KAIST Develops Ultrafast Photothermal Process Achi..

<< (from left) Ph.D. candidate Seo-Hak Park, Dr. Jae-Wan Ahn, Ph.D. candidate Do-Kyung Jeon, Prof. Sung-Yul Choi, Prof. Il-Doo Kim, Dr. Chung-Sung Park, Ph.D. candidate Ui-Chul Shin (top left) Dr. Ha-Min Shin, Dr. Jun-Hoe Choi >> The rapid and energy-efficient synthesis of high-performance catalysts is a critical hurdle in advancing clean energy technologies like hydrogen production. Addressing this challenge, a research team at KAIST has now developed a novel platform technology that utilizes a 0.02-second flash of light to generate an ultrahigh temperature of 3,000 °C, enabling the highly efficient synthesis of catalysts. This breakthrough process reduces energy consumption by more than a thousandfold compared to conventional methods while increasing hydrogen production efficiency by up to six times, marking a significant step toward the commercialization of clean energy. KAIST (President Kwang Hyung Lee) announced on October 20 that a joint research team, co-led by Professor Il-Doo Kim from the Department of Materials Science and Engineering and Professor Sung-Yool Choi from the School of Electrical Engineering, has developed a “direct-contact photothermal annealing” platform. This technique synthesizes high-performance nanomaterials through brief exposure to intense light, generating a transient temperature of 3,000 °C in just 0.02 seconds. Using this intense photothermal energy, the researchers successfully converted chemically inert nanodiamond (ND) precursors into highly conductive and catalytically active carbon nanoonions (CNOs). More impressively, the method simultaneously functionalizes the surface of the newly formed CNOs with single atoms. This integrated, one-step process restructures the support material and embeds catalytic functionality in a single light pulse, representing a significant innovation in catalyst synthesis. CNOs, composed of concentric graphitic shells, are ideal catalyst supports due to their high conductivity, large specific surface area, and chemical stability. However, traditional CNO synthesis has been hindered by complex, multi-step post-processing required to load metal catalysts and by reliance on energy-intensive, time-consuming thermal treatments that limit scalability. < Schematic Illustration of the Limitations of Conventional Thermal-Radiation Synthesis and the Carbon Nano-Onion Conversion via Direct-Contact Photothermal Treatment > To overcome these limitations, the KAIST team leveraged the photothermal effect. They devised a method of mixing ND precursors with light-absorbing carbon black (CB) and applying an intense pulse from a xenon lamp. This approach triggers the transformation of NDs into CNOs in just 0.02 seconds, a phenomenon validated by molecular dynamics simulations. A key innovation of this platform is the simultaneous synthesis of CNOs and functionalization of single-atom catalysts (SACs). When metal precursors, such as platinum (Pt), are included in the mixture, they decompose and anchor onto the surface of the nascent CNOs as individual atoms. The subsequent rapid cooling prevents atomic aggregation, resulting in a perfectly integrated one-step process for both synthesis and functionalization. The team has successfully synthesized eight different high-density SACs, including platinum (Pt), cobalt (Co), and nickel (Ni). The resulting Pt-CNO demonstrated a sixfold enhancement in hydrogen evolution efficiency compared to conventional catalysts, achieving high performance with significantly smaller quantities of precious metals. This highlights the technology's potential for scalable and sustainable hydrogen production. “We have developed, for the first time, a direct-contact photothermal annealing process that reaches 3,000°C in under 0.02 seconds,” said Professor Il-Doo Kim. “This ultrafast synthesis and single-atom functionalization platform reduces energy consumption by more than a thousandfold compared to traditional methods. We expect it to accelerate the commercialization of technologies in hydrogen energy, gas sensing, and environmental catalysis.” The study’s first authors are Dogyeong Jeon (Ph.D. candidate, Department of Materials Science and Engineering, KAIST), Dr. Hamin Shin (an alumnus of the Department of Materials Science and Engineering and a current postdoctoral researcher at ETH Zurich), and Dr. Jun-Hwe Cha (an alumnus of the School of Electrical Engineering, now at SK hynix). Professors Sung-Yool Choi and Il-Doo Kim are the corresponding authors.＼ < Inside Cover Image of the September Issue of ACS > The research was published as a Supplementary Cover Article in the September issue of ACS Nano, a leading international journal of the American Chemical Society (ACS). ※ Paper title: “Photothermal Annealing-Enabled Millisecond Synthesis of Carbon Nanoonions and Simultaneous Single-Atom Functionalization,” DOI: 10.1021/acsnano.5c11229 This research was supported by the Global R&D Infrastructure Program and the Leading Research Center Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT, and the Nano Convergence Technology Center’s Semiconductor–Battery Interfacing Platform Development Project.