Kaist

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)    

Professor Youngjin Kwon＇s Team Wins Google Award ＇..

  < Professor Youngjin Kwon >   Modern CPUs have complex structures, and in the process of handling multiple tasks simultaneously, an order-scrambling error known as a 'concurrency bug' can occur. Although this can lead to security issues, these bugs were extremely difficult to detect using conventional methods. Our university's research team has developed a world-first-level technology to automatically detect these bugs by precisely reproducing the internal operation of the CPU in a virtual environment without needing a physical chip. Through this, they successfully found and fixed 11 new bugs in the latest Linux kernel. Our university announced on the 21st that the research team led by Professor Youngjin Kwon of the School of Computing has won the 'Research Scholar Award' (Systems category) presented by Google. The Google Research Scholar Award is a global research support program, implemented since 2020, to support Early-Career Professors conducting innovative research in various fields such as AI, Systems, Security, and Data Management. It is known as a highly competitive program, with the selection process conducted directly by Google Research scientists, and only a tiny fraction of the hundreds of applicants worldwide are chosen. In particular, this award is recognized as one of the most prestigious industry research support programs globally in the field of AI and Computer Systems, and domestic recipients are rare. ■ Technology Developed to Detect Concurrency Bugs in the Latest Apple M3 and ARM Servers Professor Kwon's team developed a technology that automatically detects concurrency bugs in the latest ARM (a CPU design method that uses less power and is highly efficient) based servers, such as the Apple M3 (Apple's latest-generation computer processor chip). A concurrency bug is an error that occurs when the order of operations gets mixed up while the CPU handles multiple tasks simultaneously. This is a severe security vulnerability that can cause the computer to suddenly freeze or become a pathway for hackers to attack the system. However, these errors were extremely difficult to find with existing testing methods alone. ■ Automatically Detects Bugs by Reproducing CPU Internal Operations Without a Real CPU The core achievement of Professor Kwon's team is the 'technology to reproduce the internal operation of the CPU exactly in a virtual environment without a physical chip.' Using this technology, it is possible to precisely analyze the order in which instructions are executed and where problems occur using only software, without having to disassemble the CPU or use the actual chip. By running the Linux operating system based on this system to automatically detect bugs, the research team discovered 11 new bugs in the latest Linux kernel＊ and reported them to the developer community, where they were all fixed. ＊Linux kernel: The core operating system engine that forms the basis of servers, supercomputers, and smartphones (Android) worldwide. It acts as the 'heart' of the system, managing the CPU, memory, and storage devices. Google recognized this technology as 'very important for its own infrastructure' and conferred the Award. < Google Scholar Award Recipient Page > This technology is evaluated to have general applicability, not only to Linux but also to various operating systems such as Android and Windows. The research team has released the software as open-source (GitHub) so that anyone in academia or industry can utilize it. Professor Youngjin Kwon stated, "This award validates the international competitiveness of KAIST's systems research," and "We will continue our research to establish a safe and highly reliable computing environment."   ※ Google Scholar Award Recipient Page: https://research.google/programs-and-events/research-scholar-program/recipients/ GitHub (Technology Open-Source): https://github.com/casys-kaist/ozz  

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 >  

AI Opens a New Era in Medical Science and Bio​

< (From left) KAIST Professors Yoonjae Choi, Tae-Kyun Kim, Jong Chul Ye, Hyunwoo Kim, Seunghoon Hong, Sang Yup Lee >    KAIST announced on the 14th of November that it has been selected as a major participating institution in the 'Lunit Consortium' for the 'AI Specialized Foundation Model Development Project' supervised by the Ministry of Science and ICT, and has officially started developing an AI foundation model for the medical science and bio fields. Through this project, KAIST plans to develop an 'AI Foundation Model Specialized for Medical Science' that encompasses the entire lifecycle of bio and medical data, and lead the creation of an AI based life science innovation ecosystem. The 'Lunit Consortium' includes 7 companies-Lunit, Trillion Labs, Kakao Healthcare, Igenscience, SK Biopharm, and Rebellion-along with 9 medical and research institutions, including KAIST, Seoul National University, NYU, National Health Insurance Service Ilsan Hospital, and Yonsei Severance Hospital. This consortium will be supported by 256 state of the art B200 GPUs to build and demonstrate a 'Chain of Evidence-Based Full-Cycle Medical Science AI Model', an AI system that connects and analyzes medical data from beginning to end, and a 'Multi-Agent Service', a system where multiple AIs collaborate to perform diagnosis and prediction. KAIST's participation in this project involves a joint research team formed by professors from the School of Computing and the Kim Jaechul Graduate School of AI. Professors Yoonjae Choi, Tae-Kyun Kim, Jong Chul Ye, Hyunwoo Kim, and Seunghoon Hong will serve as the research team, and Vice President for Research  Sang Yup Lee will take on an advisory role. The research team is not merely collecting data but they are establishing a strategy (L1~L7 stages) to precisely process and systematically manage medical and life science data so that the AI can actually learn and utilize it. Through this, they plan to develop and verify an AI model that connects and analyzes diverse life science data, including medical information, gene/protein data, and new drug candidates. The data the research team aims to integrate includes a wide range from language to actual patient treatment information. Specifically, L1 represents language data, L2 is the structure of molecules, L3 is proteins and antibodies, L4 is omics data encompassing genetic and protein information, L5 is drug information, L6 is medical science research and clinical data, and L7 is real-world clinical data obtained from actual hospitals. In essence, the data handled by the AI connects everything from speech and text to molecules, proteins, drugs, clinical research, and actual patient treatment information. < The process of training AI by viewing X ray images and doctor's interpretation (text) together (MedViLL from Professor Jae-Yoon Choi' s lab) >  Vice President Sang Yup Lee is a world-renowned scholar in the fields of synthetic biology and systems metabolic engineering, leading the establishment of a bio manufacturing platform and policy advice through the convergence of life science, engineering, and AI. He advises on the analysis of life information (omics) such as genes and proteins and designs a feedback system for verifying experimental results, supporting the Korean-developed medical AI model to secure international reliability and competitiveness. Vice President Lee stated, "AI technology is breaking down the boundaries of life science and engineering, creating a new paradigm for knowledge creation," adding, "KAIST will utilize full cycle medical science data to accelerate the era where AI uncovers the causes of diseases and predicts treatments." KAIST President Kwang Hyung Lee said, "KAIST will contribute to creating an AI-based life science innovation ecosystem, lead the innovation of national strategic industries through world-class AI-bio convergence research, and drive the progress of human health and science and technology." The model developed in the Lunit Consortium will be released as an Open License for commercial use, and is expected to expand into various medical and healthcare services such as national health chatbots. With this participation, KAIST plans to strengthen research on AI-based life science data infrastructure establishment, medical AI standardization, and AI ethics and policy advice, leading the AI transition of national bio and medical science research.      

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.  

Professor Sang Yup Lee Selected as IETI ＇Laureate ..

  <Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering>       Professor Sang Yup Lee of KAIST Department of Chemical and Biomolecular Engineering has been selected as a 'Laureate Distinguished Fellow,' the highest rank of fellow, by the International Engineering and Technology Institute (IETI). Professor Lee is a globally renowned biotechnologist who has been leading research on the sustainable production of bio-based chemicals, and he received the 'ENI Award' in 2018. With this selection, he stands shoulder-to-shoulder with the world's top scholars, including recipients of the Nobel, Fields, and Turing Prizes. IETI is an international academic organization established in Hong Kong in 2015 to promote innovation and international cooperation in the fields of engineering, technology, and science. Each year, the institute selects researchers with significant academic influence worldwide and appoints them into three grades: Laureate Distinguished Fellow, Distinguished Fellow, and Fellow. Professor Lee has been named to the most prestigious grade among these. <IETI 2025 Fellow Selection Photo>     A total of 70 new fellows were selected in 2025. Among them, 14 individuals were named Laureate Distinguished Fellows, which includes recipients of top honors such as the Nobel, Fields, and Turing Prizes. Besides Professor Lee, this group includes Dudley Herschbach of Harvard University (Nobel Prize in Chemistry), Vint Cerf of Google (Turing Award), and Shigefumi Mori of Kyoto University (Fields Medal). IETI stated that the selection process involved a rigorous five-step procedure: nomination, qualification review, document screening, expert voting, and final evaluation. It also expressed hope that the newly appointed fellows will demonstrate academic leadership in their respective research fields and contribute to global scientific and technological innovation and the promotion of international cooperation.

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."      

IEEE President Professor Kramer Holds Special Lect..

Kathleen A. Kramer, President of the IEEE (Institute of Electrical and Electronics Engineers), the world's largest technical professional organization dedicated to electrical and electronic technology, visited our university on the 30th and delivered a special lecture under the theme, 'Drawing the Future of Artificial Intelligence Together.' < IEEE Leadership and KAIST EE Meeting KITIS Director (Sung-Hyun Hong), KAIST EE Professors (Joonwoo Bae), (Ian Oakley), (Hye-Won Jeong), (Chang-Shik Choi), (Dong-Soo Han), Head of EE Department (Seunghyup Yoo), IEEE President (Kathleen A. Kramer), IEEE Senior Sales Director (Francis Staples), IEEE Regional Manager for APAC (Ira Tan), KAIST EE Professor (Hee-Jin Ahn), Head of Semiconductor System Engineering Department (Sung-Hwan Cho)> Standing at the colloquium podium by invitation of the Department of Electrical Engineering (Head: Seung-Hyup Yoo), President Kramer emphasized based on IEEE's core vision, 'Advancing Technology for Humanity,' that "Artificial Intelligence (AI) is no longer a concept of the distant future; it has become a technology that is transforming human lives at the center of innovation." < Photo of IEEE President's KAIST EE Colloquium Lecture > She further added, "Technology must advance with human values at its core, and AI based on ethics and inclusiveness can lead to true innovation," sharing her insights on the direction of AI development and the social responsibility of technology. Seung-Hyup Yoo, Head of the Department of Electrical Engineering, stated, "We expect President Kramer's visit to be a stepping stone that will not only widely promote our department's capabilities in advanced fields such as AI, semiconductors, signal processing, and robotics to the international academic community but also strengthen cooperation in various ways." < Tea Meeting with the IEEE Leadership and the Vice Presidents . KITIS Director (Sung-Hyun Hong), IEEE Senior Sales Director (Francis Staples), IEEE President (Kathleen A. Kramer), KAIST Executive Vice President for Research (Sang Yup Lee), Head of EE Department (Seunghyup Yoo), IEEE Regional Manager for APAC (Ira Tan)> Meanwhile, prior to the lecture, President Kramer paid a courtesy visit to Sang-Yup Lee, KAIST Executive Vice President for Research, and reaffirmed the commitment of both organizations to advancing sustainable technology and building an ethical and inclusive research ecosystem to contribute to a better life for humanity.    

KAIST Develops Room-Temperature 3D Printing Techno..

  <(From Left) Professor Ji Tae Kim of the Department of Mechanical Engineering, Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong> The “electronic eyes” technology that can recognize objects even in darkness has taken a step forward. Infrared sensors, which act as the “seeing” component in devices such as LiDAR for autonomous vehicles, 3D face recognition systems in smartphones, and wearable healthcare devices, are regarded as key components in next-generation electronics. Now, a research team at KAIST and their collaborators have developed the world’s first room-temperature 3D printing technology that can fabricate miniature infrared sensors in any desired shape and size. KAIST (President Kwang Hyung Lee) announced on the 3rd of November that the research team led by Professor Ji Tae Kim of the Department of Mechanical Engineering, in collaboration with Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong, has developed a 3D printing technique capable of fabricating ultra-small infrared sensors—smaller than 10 micrometers (µm)—in customized shapes and sizes at room temperature. Infrared sensors convert invisible infrared signals into electrical signals and serve as essential components in realizing future electronic technologies such as robotic vision. Accordingly, miniaturization, weight reduction, and flexible form-factor design have become increasingly important. Conventional semiconductor fabrication processes were well suited for mass production but struggled to adapt flexibly to rapidly changing technological demands. They also required high-temperature processing, which limited material choices and consumed large amounts of energy. To overcome these challenges, the research team developed an ultra-precise 3D printing process that uses metal, semiconductor, and insulator materials in the form of liquid nanocrystal inks, stacking them layer by layer within a single printing platform. This method enables direct fabrication of core components of infrared sensors at room temperature, allowing for the realization of customized miniature sensors of various shapes and sizes. Particularly, the researchers achieved excellent electrical performance without the need for high-temperature annealing by applying a “ligand-exchange” process, where insulating molecules on the surface of nanoparticles are replaced with conductive ones. As a result, the team successfully fabricated ultra-small infrared sensors measuring less than one-tenth the thickness of a human hair (under 10 µm). <Figure 1. 3D printing of infrared sensors.a. Room-temperature printing process for the electrodes and photoactive layer that make up the infrared sensor.b. Structure and chemical composition of the printed infrared microsensor. c.Printed infrared sensor micropixel array.>   Professor Ji Tae Kim commented, “The developed 3D printing technology not only advances the miniaturization and lightweight design of infrared sensors but also paves the way for the creation of innovative new form-factor products that were previously unimaginable. Moreover, by reducing the massive energy consumption associated with high-temperature processes, this approach can lower production costs and enable eco-friendly manufacturing—contributing to the sustainable development of the infrared sensor industry.” The research results were published online in Nature Communications on October 16, 2025, under the title “Ligand-exchange-assisted printing of colloidal nanocrystals to enable all-printed sub-micron optoelectronics” (DOI: https://doi.org/10.1038/s41467-025-64596-4). This research was supported by the Ministry of Science and ICT of Korea through the Excellent Young Researcher Program (RS−2025−00556379), the National Strategic Technology Material Development Program (RS−2024−00407084), and the International Cooperation Research Program for Original Technology Development (RS−2024−00438059).  

“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).