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KAIST Develops AI that Automatically Detects Defects in Smart Factory Manufacturing Processes Even When Conditions Change
Recently, defect detection systems using artificial intelligence (AI) sensor data have been installed in smart factory manufacturing sites. However, when the manufacturing process changes due to machine replacement or variations in temperature, pressure, or speed, existing AI models fail to properly understand the new situation and their performance drops sharply. KAIST researchers have developed AI technology that can accurately detect defects even in such situations without retraining, achieving performance improvements up to 9.42%. This achievement is expected to contribute to reducing AI operating costs and expanding applicability in various fields such as smart factories, healthcare devices, and smart cities.
KAIST (President Kwang Hyung Lee) announced on the 26th of August that a research team led by Professor Jae-Gil Lee from the School of Computing has developed a new “time-series domain adaptation” technology that allows existing AI models to be utilized without additional defect labeling, even when manufacturing processes or equipment change.
Time-series domain adaptation technology enables AI models that handle time-varying data (e.g., temperature changes, machine vibrations, power usage, sensor signals) to maintain stable performance without additional training, even when the training environment (domain) and the actual application environment differ.
Professor Lee’s team paid attention to the fact that the core problem of AI models becoming confused by environmental (domain) changes lies not only in differences in data distribution but also in changes in defect occurrence patterns (label distribution) themselves. For example, in semiconductor wafer processes, the ratio of ring-shaped defects and scratch defects may change due to equipment modifications.
The research team developed a method for decomposing new process sensor data into three components—trends, non-trends, and frequencies—to analyze their characteristics individually. Just as humans detect anomalies by combining pitch, vibration patterns, and periodic changes in machine sounds, AI was enabled to analyze data from multiple perspectives.
In other words, the team developed TA4LS (Time-series domain Adaptation for mitigating Label Shifts) technology, which applies a method of automatically correcting predictions by comparing the results predicted by the existing model with the clustering information of the new process data. Through this, predictions biased toward the defect occurrence patterns of the existing process can be precisely adjusted to match the new process.
In particular, this technology is highly practical because it can be easily combined like an additional plug-in module inserted into existing AI systems without requiring separate complex development. That is, regardless of the AI technology currently being used, it can be applied immediately with only simple additional procedures.
In experiments using four benchmark datasets of time-series domain adaptation (i.e., four types of sensor data in which changes had occurred), the research team achieved up to 9.42% improvement in accuracy compared to existing methods.[TT1]
Especially when process changes caused large differences in label distribution (e.g., defect occurrence patterns), the AI demonstrated remarkable performance improvement by autonomously correcting and distinguishing such differences. These results proved that the technology can be used more effectively without defects in environments that produce small batches of various products, one of the main advantages of smart factories.
Professor Jae-Gil Lee, who supervised the research, said, “This technology solves the retraining problem, which has been the biggest obstacle to the introduction of artificial intelligence in manufacturing. Once commercialized, it will greatly contribute to the spread of smart factories by reducing maintenance costs and improving defect detection rates.”
This research was carried out with Jihye Na, a Ph.D. student at KAIST, as the first author, with Youngeun Nam, a Ph.D. student, and Junhyeok Kang, a researcher at LG AI Research, as co-authors. The research results were presented in August 2025 at KDD (the ACM SIGKDD Conference on Knowledge Discovery and Data Mining), the world’s top academic conference in artificial intelligence and data.
※Paper Title: “Mitigating Source Label Dependency in Time-Series Domain Adaptation under Label Shifts”
※DOI: https://doi.org/10.1145/3711896.3737050
This technology was developed as part of the research outcome of the SW Computing Industry Original Technology Development Program’s SW StarLab project (RS-2020-II200862, DB4DL: Development of Highly Available and High-Performance Distributed In-Memory DBMS for Deep Learning), supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP).
2025.08.26 View 4324 -
KAIST Takes the Lead in Developing Core Technologies for Generative AI National R&D Project
KAIST announced on the 15th of August that Professor Sanghoo Park of the Department of Nuclear and Quantum Engineering has won two consecutive awards for early-career researchers at two of the world's most prestigious plasma academic conferences.
Professor Park was selected as a recipient of the Early Career Award (ECA) at the Gaseous Electronics Conference (GEC), hosted by the American Physical Society, on August 4. He was also honored with the Young Investigator Award, presented by the International Plasma Chemistry Society (IPCS), on June 19.
The American Physical Society's GEC Early Career Award is given to only one person worldwide every two years, based on a comprehensive evaluation of research excellence, academic influence, and contributions to the field of plasma. The award will be presented at GEC 2025, which will be held at COEX in Seoul from October 13 to 17.
Established in 1948, the GEC is a leading academic conference in the plasma field with a 77-year history of showcasing key research achievements in all areas of plasma, including physics, chemistry, diagnostics, and application technologies. Recently, advanced application research such as eco-friendly chemical processes, next-generation semiconductors, and atomic layer and ultra-low-temperature etching technology for HBM processes have been gaining attention.
To commemorate the award, Professor Park will give an invited lecture at GEC 2025 on the topic of "Deep-Learning-Based Spectroscopic Data Analysis for Advancing Plasma Spectroscopy." In his lecture, he will use case studies to demonstrate a method that allows even non-specialists to easily and quickly perform spectroscopic data analysis—which is essential for spectroscopy, a key analytical method in modern science including plasma diagnostics—by using deep learning technology.
Professor Park also won the Young Investigator Award from the IPCS at the 26th International Symposium on Plasma Chemistry (ISPC 26), which was held in Minneapolis, USA, from June 15 to 20.
First held in 1973, the ISPC (International Symposium on Plasma Chemistry) is a representative international conference in the field of plasma chemistry, held biennially. It covers a wide range of topics, from basic plasma chemical reaction principles to applications in semiconductor processes, green energy, environmental science, and biotechnology. Researchers from industry, academia, and research institutions worldwide share their latest findings at each event. The Young Investigator Award is given to a scientist who has obtained their doctorate within the last 10 years and has demonstrated outstanding achievements in the field.
Professor Park was recognized for his leading research achievements in using plasma-liquid interactions and real-time optical diagnostic technology to environmentally fix nitrogen from the air and precisely control the quantity and types of reactive chemical species that are beneficial to the human body and the environment.
Professor Sanghoo Park stated, "It is very meaningful to receive the Young Investigator Award representing Korea at the GEC event, which is being held in Korea for the first time in its history." He added, "I am happy that my consistent interest in and achievements in fundamental plasma science have been recognized, and it is even more significant that the efforts of the KAIST research team have been acknowledged by the world's top conferences."
2025.08.16 View 3970 -
Vulnerability Found: One Packet Can Paralyze Smartphones
<(From left) Professor Yongdae Kim, PhD candidate Tuan Dinh Hoang, PhD candidate Taekkyung Oh from KAIST, Professor CheolJun Park from Kyung Hee University; and Professor Insu Yun from KAIST>
Smartphones must stay connected to mobile networks at all times to function properly. The core component that enables this constant connectivity is the communication modem (Baseband) inside the device. KAIST researchers, using their self-developed testing framework called 'LLFuzz (Lower Layer Fuzz),' have discovered security vulnerabilities in the lower layers of smartphone communication modems and demonstrated the necessity of standardizing 'mobile communication modem security testing.'
*Standardization: In mobile communication, conformance testing, which verifies normal operation in normal situations, has been standardized. However, standards for handling abnormal packets have not yet been established, hence the need for standardized security testing.
Professor Yongdae Kim's team from the School of Electrical Engineering at KAIST, in a joint research effort with Professor CheolJun Park's team from Kyung Hee University, announced on the 25th of July that they have discovered critical security vulnerabilities in the lower layers of smartphone communication modems. These vulnerabilities can incapacitate smartphone communication with just a single manipulated wireless packet (a data transmission unit in a network). In particular, these vulnerabilities are extremely severe as they can potentially lead to remote code execution (RCE)
The research team utilized their self-developed 'LLFuzz' analysis framework to analyze the lower layer state transitions and error handling logic of the modem to detect security vulnerabilities. LLFuzz was able to precisely extract vulnerabilities caused by implementation errors by comparing and analyzing 3GPP* standard-based state machines with actual device responses.
*3GPP: An international collaborative organization that creates global mobile communication standards.
The research team conducted experiments on 15 commercial smartphones from global manufacturers, including Apple, Samsung Electronics, Google, and Xiaomi, and discovered a total of 11 vulnerabilities. Among these, seven were assigned official CVE (Common Vulnerabilities and Exposures) numbers, and manufacturers applied security patches for these vulnerabilities. However, the remaining four have not yet been publicly disclosed.
While previous security research primarily focused on higher layers of mobile communication, such as NAS (Network Access Stratum) and RRC (Radio Resource Control), the research team concentrated on analyzing the error handling logic of mobile communication's lower layers, which manufacturers have often neglected.
These vulnerabilities occurred in the lower layers of the communication modem (RLC, MAC, PDCP, PHY*), and due to their structural characteristics where encryption or authentication is not applied, operational errors could be induced simply by injecting external signals.
*RLC, MAC, PDCP, PHY: Lower layers of LTE/5G communication, responsible for wireless resource allocation, error control, encryption, and physical layer transmission.
The research team released a demo video showing that when they injected a manipulated wireless packet (malformed MAC packet) into commercial smartphones via a Software-Defined Radio (SDR) device using packets generated on an experimental laptop, the smartphone's communication modem (Baseband) immediately crashed
※ Experiment video: https://drive.google.com/file/d/1NOwZdu_Hf4ScG7LkwgEkHLa_nSV4FPb_/view?usp=drive_link
The video shows data being normally transmitted at 23MB per second on the fast.com page, but immediately after the manipulated packet is injected, the transmission stops and the mobile communication signal disappears. This intuitively demonstrates that a single wireless packet can cripple a commercial device's communication modem.
The vulnerabilities were found in the 'modem chip,' a core component of smartphones responsible for calls, texts, and data communication, making it a very important component.
Qualcomm: Affects over 90 chipsets, including CVE-2025-21477, CVE-2024-23385.
MediaTek: Affects over 80 chipsets, including CVE-2024-20076, CVE-2024-20077, CVE-2025-20659.
Samsung: CVE-2025-26780 (targets the latest chipsets like Exynos 2400, 5400).
Apple: CVE-2024-27870 (shares the same vulnerability as Qualcomm CVE).
The problematic modem chips (communication components) are not only in premium smartphones but also in low-end smartphones, tablets, smartwatches, and IoT devices, leading to the widespread potential for user harm due to their broad diffusion.
Furthermore, the research team experimentally tested 5G vulnerabilities in the lower layers and found two vulnerabilities in just two weeks. Considering that 5G vulnerability checks have not been generally conducted, it is possible that many more vulnerabilities exist in the mobile communication lower layers of baseband chips.
Professor Yongdae Kim explained, "The lower layers of smartphone communication modems are not subject to encryption or authentication, creating a structural risk where devices can accept arbitrary signals from external sources." He added, "This research demonstrates the necessity of standardizing mobile communication modem security testing for smartphones and other IoT devices."
The research team is continuing additional analysis of the 5G lower layers using LLFuzz and is also developing tools for testing LTE and 5G upper layers. They are also pursuing collaborations for future tool disclosure. The team's stance is that "as technological complexity increases, systemic security inspection systems must evolve in parallel."
First author Tuan Dinh Hoang, a Ph.D. student in the School of Electrical Engineering, will present the research results in August at USENIX Security 2025, one of the world's most prestigious conferences in cybersecurity.
※ Paper Title: LLFuzz: An Over-the-Air Dynamic Testing Framework for Cellular Baseband Lower Layers (Tuan Dinh Hoang and Taekkyung Oh, KAIST; CheolJun Park, Kyung Hee Univ.; Insu Yun and Yongdae Kim, KAIST)
※ Usenix paper site: https://www.usenix.org/conference/usenixsecurity25/presentation/hoang (Not yet public), Lab homepage paper: https://syssec.kaist.ac.kr/pub/2025/LLFuzz_Tuan.pdf
※ Open-source repository: https://github.com/SysSec-KAIST/LLFuzz (To be released)
This research was conducted with support from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Ministry of Science and ICT.
2025.07.25 View 4769 -
KAIST Develops Glare-Free, Heat-Blocking 'Smart Window'... Applicable to Buildings and Vehicles
• Professor Hong Chul Moon of the Department of Chemical and Biomolecular Engineering develops RECM, a next-generation smart window technology, expecting cooling energy savings and effective indoor thermal management.
• When using the developed RECM, a significantly superior temperature reduction effect is observed compared to conventional windows.
• With a 'pedestrian-friendly smart window' design that eliminates glare by suppressing external reflections, it is expected to be adapted in architectural structures, transportation, and more.
< (From left) First author Hoy Jung Jo, Professor Hong Chul Moon >
In the building sector, which accounts for approximately 40% of global energy consumption, heat ingress through windows has been identified as a primary cause of wasted heating and cooling energy. Our research team has successfully developed a 'pedestrian-friendly smart window' technology capable of not only reducing heating and cooling energy in urban buildings but also resolving the persistent issue of 'light pollution' in urban living.
On the 17th of June, Professor Hong Chul Moon's research team at KAIST's Department of Chemical and Biomolecular Engineering announced the development of a 'smart window technology' that allows users to control the light and heat entering through windows according to their intent, and effectively neutralize glare from external sources.
Recently, 'active smart window' technology, which enables free adjustment of light and heat based on user operation, has garnered significant attention. Unlike conventional windows that passively react to changes in temperature or light, this is a next-generation window system that can be controlled in real-time via electrical signals.
The next-generation smart window technology developed by the research team, RECM (Reversible Electrodeposition and Electrochromic Mirror), is a smart window system based on a single-structured *electrochromic device that can actively control the transmittance of visible light and near-infrared (heat).
*Electrochromic device: A device whose optical properties change in response to an electrical signal.
In particular, by effectively suppressing the glare phenomenon caused by external reflected light—a problem previously identified in traditional metal *deposition smart windows—through the combined application of electrochromic materials, a 'pedestrian-friendly smart window' suitable for building facades has been realized.
*Deposition: A process involving the electrochemical reaction to coat metal ions, such as Ag+, onto an electrode surface in solid form.
The RECM system developed in this study operates in three modes depending on voltage control.
Mode I (Transparent Mode) is advantageous for allowing sunlight to enter the indoor space during winter, as it transmits both light and heat like ordinary glass.
In Mode II (Colored Mode), *Prussian Blue (PB) and **DHV+• chemical species are formed through a redox (oxidation-reduction) reaction, causing the window to turn a deep blue color. In this state, light is absorbed, and only a portion of the heat is transmitted, allowing for privacy while enabling appropriate indoor temperature control.
*Prussian Blue: An electrochromic material that transitions between colorless and blue upon electrical stimulation.
**DHV+•: A radical state colored molecule generated upon electrical stimulation.
Mode III (Colored and Deposition Mode) involves the reduction and deposition of silver (Ag+) ions on the electrode surface, reflecting both light and heat. Concurrently, the colored material absorbs the reflected light, effectively blocking glare for external pedestrians.
The research team validated the practical indoor temperature reduction effect of the RECM technology through experiments utilizing a miniature model house. When a conventional glass window was installed, the indoor temperature rose to 58.7°C within 45 minutes. Conversely, when RECM was operated in Mode III, the temperature reached 31.5°C, demonstrating a temperature reduction effect of approximately 27.2°C.
Furthermore, since each state transition is achievable solely by electrical signals, it is regarded as an active smart technology capable of instantaneous response according to season, time, and intended use.
< Figure 1. Operation mechanism of the RECM smart window. The RECM system can switch among three states—transparent, colored, and colored & deposition—via electrical stimulation. At -1.6 V, DHV•+ and Prussian Blue (PB) are formed, blocking visible light to provide privacy protection and heat blocking. At -2.0 V, silver (Ag) is deposited on the electrode surface, reflecting light and heat, while DHV•+ and Prussian Blue absorb reflected light, effectively suppressing external glare. Through this mechanism, it functions as an active smart window that simultaneously controls light, heat, and glare. >
Professor Hong Chul Moon of KAIST, the corresponding author of this study, stated, "This research goes beyond existing smart window technologies limited to visible light control, presenting a truly smart window platform that comprehensively considers not only active indoor thermal control but also the visual safety of pedestrians." He added, "Various applications are anticipated, from urban buildings to vehicles and trains."
< Figure 2. Analysis of glare suppression effect of conventional reflective smart windows and RECM. This figure presents the results comparing the glare phenomenon occurring during silver (Ag) deposition between conventional reflective smart windows and RECM Mode III. Conventional reflective devices resulted in strong reflected light on the desk surface due to their high reflectivity. In contrast, RECM Mode III, where the colored material absorbed reflected light, showed a 33% reduction in reflected light intensity, and no reflected light was observed from outside. This highlights the RECM system's distinctiveness and practicality as a 'pedestrian-friendly smart window' optimized for dense urban environments, extending beyond just heat blocking. >
The findings of this research were published on June 13, 2025, in Volume 10, Issue 6 of 'ACS Energy Letters'. The listed authors for this publication are Hoy Jung Jo, Yeon Jae Jang, Hyeon-Don Kim, Kwang-Seop Kim, and Hong Chul Moon.
※ Paper Title: Glare-Free, Energy-Efficient Smart Windows: A Pedestrian-Friendly System with Dynamically Tunable Light and Heat Regulation
※ DOI: 10.1021/acsenergylett.5c00637
< Figure 3. Temperature reduction performance verification in a miniature model house. The actual heat blocking effect was evaluated by applying RECM devices to a model building. Under identical conditions, the indoor temperature with ordinary glass rose to 58.7°C, whereas with RECM in Mode III, it reached 31.5°C, demonstrating a maximum temperature reduction effect of 27.2°C. The indoor temperature difference was also visually confirmed through thermal images, which proves the potential for indoor temperature control in urban buildings. >
This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and the internal research program of the Korea Institute of Machinery and Materials.
2025.06.20 View 9527 -
High-Resolution Spectrometer that Fits into Smartphones Developed by KAIST Researchers
- Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering develops an ultra-compact, high-resolution spectrometer using 'double-layer disordered metasurfaces' that generate unique random patterns depending on light's color.
- Unlike conventional dispersion-based spectrometers that were difficult to apply to portable devices, this new concept spectrometer technology achieves 1nm-level high resolution in a device smaller than 1cm, comparable in size to a fingernail.
- It can be utilized as a built-in spectrometer in smartphones and wearable devices in the future, and can be expanded to advanced optical technologies such as hyperspectral imaging and ultrafast imaging.
< Photo 1. (From left) Professor Mooseok Jang, Dong-gu Lee (Ph.D. candidate), Gookho Song (Ph.D. candidate) >
Color, as the way light's wavelength is perceived by the human eye, goes beyond a simple aesthetic element, containing important scientific information like a substance's composition or state. Spectrometers are optical devices that analyze material properties by decomposing light into its constituent wavelengths, and they are widely used in various scientific and industrial fields, including material analysis, chemical component detection, and life science research. Existing high-resolution spectrometers were large and complex, making them difficult for widespread daily use. However, thanks to the ultra-compact, high-resolution spectrometer developed by KAIST researchers, it is now expected that light's color information can be utilized even within smartphones or wearable devices.
KAIST (President Kwang Hyung Lee) announced on the 13th that Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering has successfully developed a reconstruction-based spectrometer technology using double-layer disordered metasurfaces*.
*Double-layer disordered metasurface: An innovative optical device that complexly scatters light through two layers of disordered nanostructures, creating unique and predictable speckle patterns for each wavelength.
Existing high-resolution spectrometers have a large form factor, on the order of tens of centimeters, and require complex calibration processes to maintain accuracy. This fundamentally stems from the operating principle of traditional dispersive elements, such as gratings and prisms, which separate light wavelengths along the propagation direction, much like a rainbow separates colors. Consequently, despite the potential for light's color information to be widely useful in daily life, spectroscopic technology has been limited to laboratory or industrial manufacturing environments.
< Figure 1. Through a simple structure consisting of a double layer of disordered metasurfaces and an image sensor, it was shown that speckles of predictable spectral channels with high spectral resolution can be generated in a compact form factor. The high similarity between the measured and calculated speckles was used to solve the inverse problem and verify the ability to reconstruct the spectrum. >
The research team devised a method that departs from the conventional spectroscopic paradigm of using diffraction gratings or prisms, which establish a one-to-one correspondence between light's color information and its propagation direction, by utilizing designed disordered structures as optical components. In this process, they employed metasurfaces, which can freely control the light propagation process using structures tens to hundreds of nanometers in size, to accurately implement 'complex random patterns (speckle*)'.
*Speckle: An irregular pattern of light intensity created by the interference of multiple wavefronts of light.
Specifically, they developed a method that involves implementing a double-layer disordered metasurface to generate wavelength-specific speckle patterns and then reconstructing precise color information (wavelength) of the light from the random patterns measured by a camera.
As a result, they successfully developed a new concept spectrometer technology that can accurately measure light across a broad range of visible to infrared (440-1,300nm) with a high resolution of 1 nanometer (nm) in a device smaller than a fingernail (less than 1cm) using only a single image capture.
< Figure 2. A disordered metasurface is a metasurface with irregularly arranged structures ranging from tens to hundreds of nanometers in size. In a double-layer structure, a propagation space is placed between the two metasurfaces to control the output speckle with high degrees of freedom, thereby achieving a spectral resolution of 1 nm even in a form factor smaller than 1 cm. >
Dong-gu Lee, a lead author of this study, stated, "This technology is implemented in a way that is directly integrated with commercial image sensors, and we expect that it will enable easy acquisition and utilization of light's wavelength information in daily life when built into mobile devices in the future."
Professor Mooseok Jang said, "This technology overcomes the limitations of existing RGB three-color based machine vision fields, which only distinguish and recognize three color components (red, green, blue), and has diverse applications. We anticipate various applied research for this technology, which expands the horizon of laboratory-level technology to daily-level machine vision technology for applications such as food component analysis, crop health diagnosis, skin health measurement, environmental pollution detection, and bio/medical diagnostics." He added, "Furthermore, it can be extended to various advanced optical technologies such as hyperspectral imaging, which records wavelength and spatial information simultaneously with high resolution, 3D optical trapping technology, which precisely controls light of multiple wavelengths into desired forms, and ultrafast imaging technology, which captures phenomena occurring in very short periods."
This research was collaboratively led by Dong-gu Lee (Ph.D. candidate) and Gookho Song (Ph.D. candidate) from the KAIST Department of Bio and Brain Engineering as co-first authors, with Professor Mooseok Jang as the corresponding author. The findings were published online in the international journal Science Advances on May 28, 2025.* Paper Title: Reconstructive spectrometer using double-layer disordered metasurfaces* DOI: 10.1126/sciadv.adv2376
This research was supported by the Samsung Research Funding and Incubation Center of Samsung Electronics grant, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT).
2025.06.13 View 8187 -
KAIST to Develop a Korean-style ChatGPT Platform Specifically Geared Toward Medical Diagnosis and Drug Discovery
On May 23rd, KAIST (President Kwang-Hyung Lee) announced that its Digital Bio-Health AI Research Center (Director: Professor JongChul Ye of KAIST Kim Jaechul Graduate School of AI) has been selected for the Ministry of Science and ICT's 'AI Top-Tier Young Researcher Support Program (AI Star Fellowship Project).' With a total investment of ₩11.5 billion from May 2025 to December 2030, the center will embark on the full-scale development of AI technology and a platform capable of independently inferring and determining the kinds of diseases, and discovering new drugs.
< Photo. On May 20th, a kick-off meeting for the AI Star Fellowship Project was held at KAIST Kim Jaechul Graduate School of AI’s Yangjae Research Center with the KAIST research team and participating organizations of Samsung Medical Center, NAVER Cloud, and HITS. [From left to right in the front row] Professor Jaegul Joo (KAIST), Professor Yoonjae Choi (KAIST), Professor Woo Youn Kim (KAIST/HITS), Professor JongChul Ye (KAIST), Professor Sungsoo Ahn (KAIST), Dr. Haanju Yoo (NAVER Cloud), Yoonho Lee (KAIST), HyeYoon Moon (Samsung Medical Center), Dr. Su Min Kim (Samsung Medical Center) >
This project aims to foster an innovative AI research ecosystem centered on young researchers and develop an inferential AI agent that can utilize and automatically expand specialized knowledge systems in the bio and medical fields.
Professor JongChul Ye of the Kim Jaechul Graduate School of AI will serve as the lead researcher, with young researchers from KAIST including Professors Yoonjae Choi, Kimin Lee, Sungsoo Ahn, and Chanyoung Park, along with mid-career researchers like Professors Jaegul Joo and Woo Youn Kim, jointly undertaking the project. They will collaborate with various laboratories within KAIST to conduct comprehensive research covering the entire cycle from the theoretical foundations of AI inference to its practical application.
Specifically, the main goals include: - Building high-performance inference models that integrate diverse medical knowledge systems to enhance the precision and reliability of diagnosis and treatment. - Developing a convergence inference platform that efficiently combines symbol-based inference with neural network models. - Securing AI technology for new drug development and biomarker discovery based on 'cell ontology.'
Furthermore, through close collaboration with industry and medical institutions such as Samsung Medical Center, NAVER Cloud, and HITS Co., Ltd., the project aims to achieve: - Clinical diagnostic AI utilizing medical knowledge systems. - AI-based molecular target exploration for new drug development. - Commercialization of an extendible AI inference platform.
Professor JongChul Ye, Director of KAIST's Digital Bio-Health AI Research Center, stated, "At a time when competition in AI inference model development is intensifying, it is a great honor for KAIST to lead the development of AI technology specialized in the bio and medical fields with world-class young researchers." He added, "We will do our best to ensure that the participating young researchers reach a world-leading level in terms of research achievements after the completion of this seven-year project starting in 2025."
The AI Star Fellowship is a newly established program where post-doctoral researchers and faculty members within seven years of appointment participate as project leaders (PLs) to independently lead research. Multiple laboratories within a university and demand-side companies form a consortium to operate the program.
Through this initiative, KAIST plans to nurture bio-medical convergence AI talent and simultaneously promote the commercialization of core technologies in collaboration with Samsung Medical Center, NAVER Cloud, and HITS.
2025.05.26 View 15088 -
KAIST’s Next-Generation Small Satellite-2 Completes a Two-Year Mission – the Successful Observation of Arctic and Forest Changes
KAIST (President Kwang-Hyung Lee) announced on the 25th of May that the Next-Generation Small Satellite-2 developed by the Satellite Technology Research Center (SaTReC, Director Jaeheung Han) and launched aboard the third Nuri rocket from the Naro Space Center at 18:24 on May 25, 2023, has successfully completed its two-year core mission of verifying homegrown Synthetic Aperture Radar (SAR) technology and conducting all-weather Earth observations.
The SAR system onboard the satellite was designed, manufactured, and tested domestically for the first time by KAIST’s Satellite Research Center. As of May 25, 2025, it has successfully completed its two-year in-orbit technology demonstration mission.
Particularly noteworthy is the fact that the SAR system was mounted on the 100 kg-class Next-Generation Small Satellite-2, marking a major step forward in the miniaturization and weight reduction of spaceborne radar systems and strengthening Korea’s competitiveness in satellite technology.
< Figure 1. Conceptual diagram of Earth observation by the Next-Generation Small Satellite No. 2's synthetic aperture radar >
The developed SAR is an active sensor that uses electromagnetic waves, allowing all-weather image acquisition regardless of time of day or weather conditions. This makes it especially useful for monitoring regions like the Korean Peninsula, which frequently experiences rain and cloud cover, as it can observe even in cloudy and rainy conditions or darkness.
Since its launch, the satellite has carried out three to four image acquisitions per day on average, undergoing functionality checks and technology verifications. To date, it has completed over 1,200 Earth observations and the SAR continues to perform stably, supporting ongoing observation tasks even beyond its designated mission lifespan.
< Photo 1. Researchers of the Next-Generation Small Satellite No. 2 at SatRec, taken at the KAIST ground station. (From left) Sung-Og Park, Jung-soo Lee, Hongyoung Park, TaeSeong Jang (Next-Generation Small Satellite No. 2 Project Manager), Seyeon Kim, Mi Young Park, Yongmin Kim, DongGuk Kim >
Although still in the domestic technology verification stage, KAIST’s Satellite Research Center has been collaborating with the Korea Polar Research Institute (Director Hyoung Chul Shin) and the Korea National Park Research Institute (Director Jin Tae Kim) since March 2024 to prioritize imaging of areas of interest related to Arctic ice changes and forest ecosystem monitoring.
KAIST’s Satellite Research Center is conducting repeated observations of Arctic sea ice, and the Remote Sensing and Cryosphere Information Center of the Korea Polar Research Institute is analyzing the results using time-series data to precisely track changes in sea ice area and structure due to climate change.
< Photo 2. Radar Images from Observations on July 24, 2024 - Around the Atchafalaya River in Louisiana, USA. The Wax Lake Delta is seen growing like a leaf. >
Recently, the Korea Polar Research Institute (KOPRI), by integrating observation data from the Next-Generation Small Satellite No. 2 and the European Space Agency's (ESA) Sentinel-1, detected a significant increase of 15 km² in the area of an ice lake behind Canada's Milne Ice Shelf (a massive, floating layer of ice where glaciers flow from land into the sea) between 2021 and 2025. This has exacerbated structural instability and is analyzed as an important sign indicating the acceleration of Arctic climate change.
Hyuncheol Kim, Director of the Remote Sensing and Cryosphere Information Center at the Korea Polar Research Institute, stated, “This research clearly demonstrates how vulnerable Arctic ice shelves are to climate change. We will continue to monitor and analyze Arctic environmental changes using the SAR aboard the Next-Generation Small Satellite-2 and promote international collaboration.” He added, “We also plan to present these findings at international academic conferences and expand educational and outreach efforts to raise public awareness about changes in the Arctic environment.”
< Photo 3. Sinduri Coastal Dune, Taean Coastal National Park, Taean-gun, Chungcheongnam-do >
In collaboration with the Climate Change Research Center of the National Park Research Institute, SAR imagery from the satellite is also being used to study phenological shifts due to climate change, the dieback of conifers in high-altitude zones, and landslide monitoring in forest ecosystems. Researchers are also analyzing the spatial distribution of carbon storage in forest areas using satellite data, comparing it with field measurements to improve accuracy.
Because SAR is unaffected by light and weather conditions, it can observe through fire and smoke during wildfires, making it an exceptionally effective tool for the regular monitoring of large protected areas. It is expected to play an important role in shaping future forest conservation policies.
In addition, KAIST’s Satellite Research Center is working on a system to convert the satellite’s technology demonstration data into standardized imagery products, with budget support from the Korea Aerospace Administration (Administrator Youngbin Yoon), making the data more accessible to research institutions and boosting the usability of the satellite’s observations.
< Photo 4. Jang Bogo Station, Antarctica >
Jaeheung Han, Director of the Satellite Research Center, said, “The significance of the Next-Generation Small Satellite-2 lies not only in the success of domestic development, but also in its direct contribution to real-world environmental analysis and national research efforts. We will continue to focus on expanding the application of SAR data from the satellite.”
KAIST President Kwang-Hyung Lee remarked, “This satellite is a product of KAIST’s advanced space technology and the innovation capacity of its researchers. Its success signals KAIST’s potential to lead in future space technology talent development and R&D, and we will continue to accelerate efforts in this direction.”
< Photo 5. Confirmation of changes in the expanded area of the Milne Ice Shelf lake using observation data from Next-Generation Small Satellite No. 2 and Sentinel-1 >
2025.05.25 View 6901 -
KAIST Develops AI-Driven Performance Prediction Model to Advance Space Electric Propulsion Technology
< (From left) PhD candidate Youngho Kim, Professor Wonho Choe, and PhD candidate Jaehong Park from the Department of Nuclear and Quantum Engineering >
Hall thrusters, a key space technology for missions like SpaceX's Starlink constellation and NASA's Psyche asteroid mission, are high-efficiency electric propulsion devices using plasma technology*. The KAIST research team announced that the AI-designed Hall thruster developed for CubeSats will be installed on the KAIST-Hall Effect Rocket Orbiter (K-HERO) CubeSat to demonstrate its in-orbit performance during the fourth launch of the Korean Launch Vehicle called Nuri rocket (KSLV-2) scheduled for November this year.
*Plasma is one of the four states of matter, where gases are heated to high energies, causing them to separate into charged ions and electrons. Plasma is used not only in space electric propulsion but also in semiconductor manufacturing, display processes, and sterilization devices.
On February 3rd, the research team from the KAIST Department of Nuclear and Quantum Engineering’s Electric Propulsion Laboratory, led by Professor Wonho Choe, announced the development of an AI-based technique to accurately predict the performance of Hall thrusters, the engines of satellites and space probes.
Hall thrusters provide high fuel efficiency, requiring minimal propellant to achieve significant acceleration of spacecrafts or satellites while producing substantial thrust relative to power consumption. Due to these advantages, Hall thrusters are widely used in various space missions, including the formation flight of satellite constellations, deorbiting maneuvers for space debris mitigation, and deep space missions such as asteroid exploration.
As the space industry continues to grow during the NewSpace era, the demand for Hall thrusters suited to diverse missions is increasing. To rapidly develop highly efficient, mission-optimized Hall thrusters, it is essential to predict thruster performance accurately from the design phase.
However, conventional methods have limitations, as they struggle to handle the complex plasma phenomena within Hall thrusters or are only applicable under specific conditions, leading to lower prediction accuracy.
The research team developed an AI-based performance prediction technique with high accuracy, significantly reducing the time and cost associated with the iterative design, fabrication, and testing of thrusters. Since 2003, Professor Wonho Choe’s team has been leading research on electric propulsion development in Korea. The team applied a neural network ensemble model to predict thruster performance using 18,000 Hall thruster training data points generated from their in-house numerical simulation tool.
The in-house numerical simulation tool, developed to model plasma physics and thrust performance, played a crucial role in providing high-quality training data. The simulation’s accuracy was validated through comparisons with experimental data from ten KAIST in-house Hall thrusters, with an average prediction error of less than 10%.
< Figure 1. This research has been selected as the cover article for the March 2025 issue (Volume 7, Issue 3) of the AI interdisciplinary journal, Advanced Intelligent Systems. >
The trained neural network ensemble model acts as a digital twin, accurately predicting the Hall thruster performance within seconds based on thruster design variables.
Notably, it offers detailed analyses of performance parameters such as thrust and discharge current, accounting for Hall thruster design variables like propellant flow rate and magnetic field—factors that are challenging to evaluate using traditional scaling laws.
This AI model demonstrated an average prediction error of less than 5% for the in-house 700 W and 1 kW KAIST Hall thrusters and less than 9% for a 5 kW high-power Hall thruster developed by the University of Michigan and the U.S. Air Force Research Laboratory. This confirms the broad applicability of the AI prediction method across different power levels of Hall thrusters.
Professor Wonho Choe stated, “The AI-based prediction technique developed by our team is highly accurate and is already being utilized in the analysis of thrust performance and the development of highly efficient, low-power Hall thrusters for satellites and spacecraft. This AI approach can also be applied beyond Hall thrusters to various industries, including semiconductor manufacturing, surface processing, and coating, through ion beam sources.”
< Figure 2. The AI-based prediction technique developed by the research team accurately predicts thrust performance based on design variables, making it highly valuable for the development of high-efficiency Hall thrusters. The neural network ensemble processes design variables, such as channel geometry and magnetic field information, and outputs key performance metrics like thrust and prediction accuracy, enabling efficient thruster design and performance analysis. >
Additionally, Professor Choe mentioned, “The CubeSat Hall thruster, developed using the AI technique in collaboration with our lab startup—Cosmo Bee, an electric propulsion company—will be tested in orbit this November aboard the K-HERO 3U (30 x 10 x 10 cm) CubeSat, scheduled for launch on the fourth flight of the KSLV-2 Nuri rocket.”
This research was published online in Advanced Intelligent Systems on December 25, 2024 with PhD candidate Jaehong Park as the first author and was selected as the journal’s cover article, highlighting its innovation.
< Figure 3. Image of the 150 W low-power Hall thruster for small and micro satellites, developed in collaboration with Cosmo Bee and the KAIST team. The thruster will be tested in orbit on the K-HERO CubeSat during the KSLV-2 Nuri rocket’s fourth launch in Q4 2025. >
This research was supported by the National Research Foundation of Korea’s Space Pioneer Program (200mN High Thrust Electric Propulsion System Development).
(Paper Title: Predicting Performance of Hall Effect Ion Source Using Machine Learning, DOI: https://doi.org/10.1002/aisy.202400555 )
< Figure 4. Graphs of the predicted thrust and discharge current of KAIST’s 700 W Hall thruster using the AI model (HallNN). The left image shows the Hall thruster operating in KAIST Electric Propulsion Laboratory’s vacuum chamber, while the center and right graphs present the prediction results for thrust and discharge current based on anode mass flow rate. The red lines represent AI predictions, and the blue dots represent experimental results, with a prediction error of less than 5%. >
2025.02.03 View 16459 -
KAIST Develops a Multifunctional Structural Battery Capable of Energy Storage and Load Support
Structural batteries are used in industries such as eco-friendly, energy-based automobiles, mobility, and aerospace, and they must simultaneously meet the requirements of high energy density for energy storage and high load-bearing capacity. Conventional structural battery technology has struggled to enhance both functions concurrently. However, KAIST researchers have succeeded in developing foundational technology to address this issue.
< Photo 1. (From left) Professor Seong Su Kim, PhD candidates Sangyoon Bae and Su Hyun Lim of the Department of Mechanical Engineering >
< Photo 2. (From left) Professor Seong Su Kim and Master's Graduate Mohamad A. Raja of KAIST Department of Mechanical Engineering >
KAIST (represented by President Kwang Hyung Lee) announced on the 19th of November that Professor Seong Su Kim's team from the Department of Mechanical Engineering has developed a thin, uniform, high-density, multifunctional structural carbon fiber composite battery* capable of supporting loads, and that is free from fire risks while offering high energy density.
*Multifunctional structural batteries: Refers to the ability of each material in the composite to simultaneously serve as a load-bearing structure and an energy storage element.
Early structural batteries involved embedding commercial lithium-ion batteries into layered composite materials. These batteries suffered from low integration of their mechanical and electrochemical properties, leading to challenges in material processing, assembly, and design optimization, making commercialization difficult.
To overcome these challenges, Professor Kim's team explored the concept of "energy-storing composite materials," focusing on interface and curing properties, which are critical in traditional composite design. This led to the development of high-density multifunctional structural carbon fiber composite batteries that maximize multifunctionality.
The team analyzed the curing mechanisms of epoxy resin, known for its strong mechanical properties, combined with ionic liquid and carbonate electrolyte-based solid polymer electrolytes. By controlling temperature and pressure, they were able to optimize the curing process.
The newly developed structural battery was manufactured through vacuum compression molding, increasing the volume fraction of carbon fibers—serving as both electrodes and current collectors—by over 160% compared to previous carbon-fiber-based batteries.
This greatly increased the contact area between electrodes and electrolytes, resulting in a high-density structural battery with improved electrochemical performance. Furthermore, the team effectively controlled air bubbles within the structural battery during the curing process, simultaneously enhancing the battery's mechanical properties.
Professor Seong Su Kim, the lead researcher, explained, “We proposed a framework for designing solid polymer electrolytes, a core material for high-stiffness, ultra-thin structural batteries, from both material and structural perspectives. These material-based structural batteries can serve as internal components in cars, drones, airplanes, and robots, significantly extending their operating time with a single charge. This represents a foundational technology for next-generation multifunctional energy storage applications.”
< Figure 2. Supplementary cover of ACS Applied Materials & Interfaces >
Mohamad A. Raja, a master’s graduate of KAIST’s Department of Mechanical Engineering, participated as the first author of this research, which was published in the prestigious journal ACS Applied Materials & Interfaces on September 10. The paper was recognized for its excellence and selected as a supplementary cover article. (Paper title: “Thin, Uniform, and Highly Packed Multifunctional Structural Carbon Fiber Composite Battery Lamina Informed by Solid Polymer Electrolyte Cure Kinetics.” https://doi.org/10.1021/acsami.4c08698)
This research was supported by the National Research Foundation of Korea’s Mid-Career Researcher Program and the National Semiconductor Research Laboratory Development Program.
2024.11.27 View 12753 -
KAIST holds its first ‘KAIST Tech Fair’ in New York, USA
< Photo 1. 2023 KAIST Tech Fair in New York >
KAIST (President Kwang-Hyung Lee) announced on the 11th that it will hold the ‘2023 KAIST Tech Fair in New York’ at the Kimmel Center at New York University in Manhattan, USA, on the 22nd of this month. It is an event designed to be the starting point for KAIST to expand its startup ecosystem into the global stage, and it is to attract investments and secure global customers in New York by demonstrating the technological value of KAIST startup companies directly at location.
< Photo 2. President Kwang Hyung Lee at the 2023 KAIST Tech Fair in New York >
KAIST has been holding briefing sessions for technology transfer in Korea every year since 2018, and this year is the first time to hold a tech fair overseas for global companies.
KAIST Institute of Technology Value Creation (Director Sung-Yool Choi) has prepared for this event over the past six months with the Korea International Trade Association (hereinafter KITA, CEO Christopher Koo) to survey customer base and investment companies to conduct market analysis.
Among the companies founded with the technologies developed by the faculty and students of KAIST and their partners, 7 companies were selected to be matched with companies overseas that expressed interests in these technologies. Global multinational companies in the fields of IT, artificial intelligence, environment, logistics, distribution, and retail are participating as demand agencies and are testing the marketability of the start-up's technology as of September.
Daim Research, founded by Professor Young Jae Jang of the Department of Industrial and Systems Engineering, is a company specializing in smart factory automation solutions and is knocking on the door of the global market with a platform technology optimized for automated logistics systems.
< Photo 3. Presentation by Professor Young Jae Jang for DAIM Research >
It is a ‘collaborative intelligence’ solution that maximizes work productivity by having a number of robots used in industrial settings collaborate with one another. The strength of their solution is that logistics robots equipped with AI reinforced learning technology can respond to processes and environmental changes on their own, minimizing maintenance costs and the system can achieve excellent performance even with a small amount of data when it is combined with the digital twin technology the company has developed on its own.
A student startup, ‘Aniai’, is entering the US market, the home of hamburgers, with hamburger patty automation equipments and solutions. This is a robot kitchen startup founded by its CEO Gunpil Hwang, a graduate of KAIST’s School of Electrical Engineering which gathered together the experts in the fields of robot control, design, and artificial intelligence and cognitive technology to develop technology to automatically cook hamburger patties.
At the touch of a button, both sides of the patty are cooked simultaneously for consistent taste and quality according to the set condition. Since it can cook about 200 dishes in an hour, it is attracting attention as a technology that can not only solve manpower shortages but also accelerate the digital transformation of the restaurant industry.
Also, at the tech fair to be held at the Kimmel Center of New York University on the 22nd, the following startups who are currently under market verification in the U.S. will be participating: ▴'TheWaveTalk', which developed a water quality management system that can measure external substances and metal ions by transferring original technology from KAIST; ▴‘VIRNECT’, which helps workers improve their skills by remotely managing industrial sites using XR*; ▴‘Datumo’, a solution that helps process and analyze artificial intelligence big data, ▴‘VESSL AI’, the provider of a solution to eliminate the overhead** of machine learning systems; and ▴ ‘DolbomDream’, which developed an inflatable vest that helps the psychological stability of people with developmental disabilities.
* XR (eXtended Reality): Ultra-realistic technology that enhances immersion by utilizing augmented reality, virtual reality, and mixed reality technologies
** Overhead: Additional time required for stable processing of the program
In addition, two companies (Plasmapp and NotaAI) that are participating in the D-Unicorn program with the support of the Daejeon City and two companies (Enget and ILIAS Biologics) that are receiving support from the Scale Up Tips of the Ministry of SMEs and Startups, three companies (WiPowerOne, IDK Lab, and Artificial Photosynthesis Lab) that are continuing to realize the sustainable development goals for a total of 14 KAIST startups, will hold a corporate information session with about 100 invited guests from global companies and venture capital.
< Photo 4. Presentation for AP Lab >
Prior to this event, participating startups will be visiting the New York Economic Development Corporation and large law firms to receive advice on U.S. government support programs and on their attemps to enter the U.S. market. In addition, the participating companies plan to visit a startup support investment institution pursuing sustainable development goals and the Leslie eLab, New York University's one-stop startup support space, to lay the foundation for KAIST's leap forward in global technology commercialization.
< Photo 5. Sung-Yool Choi, the Director of KAIST Institute of Technology Value Creation (left) at the 2023 KAIST Tech Fair in New York with the key participants >
Sung-Yool Choi, the Director of KAIST Institute of Technology Value Creation, said, “KAIST prepared this event to realize its vision of being a leading university in creating global value.” He added, “We hope that our startups founded with KAIST technology would successfully completed market verification to be successful in securing global demands and in attracting investments for their endeavors.”
2023.09.11 View 31875 -
KAIST presents a fundamental technology to remove metastatic traits from lung cancer cells
KAIST (President Kwang Hyung Lee) announced on January 30th that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering succeeded in using systems biology research to change the properties of carcinogenic cells in the lungs and eliminate both drug resistance and their ability to proliferate out to other areas of the body.
As the incidences of cancer increase within aging populations, cancer has become the most lethal disease threatening healthy life. Fatality rates are especially high when early detection does not happen in time and metastasis has occurred in various organs. In order to resolve this problem, a series of attempts were made to remove or lower the ability of cancer cells to spread, but they resulted in cancer cells in the intermediate state becoming more unstable and even more malignant, which created serious treatment challenges.
Professor Kwang-Hyun Cho's research team simulated various cancer cell states in the Epithelial-to-Mesenchymal Transition (EMT) of lung cancer cells, between epithelial cells without metastatic ability and mesenchymal cells with metastatic ability. A mathematical model of molecular network was established, and key regulators that could reverse the state of invasive and drug resistant mesenchymal cells back to the epithelial state were discovered through computer simulation analysis and molecular cell experiments. In particular, this process succeeded in properly reverting the mesenchymal lung cancer cells to a state where they were sensitive to chemotherapy treatment while avoiding the unstable EMT hybrid cell state in the middle process, which had remained a difficult problem.
The results of this research, in which KAIST Ph.D. student Namhee Kim, Dr. Chae Young Hwang, Researcher Taeyoung Kim, and Ph.D. student Hyunjin Kim participated, were published as an online paper in the international journal “Cancer Research” published by the American Association for Cancer Research (AACR) on January 30th. (Paper title: A cell fate reprogramming strategy reverses epithelial-to-mesenchymal transition of lung cancer cells while avoiding hybrid states)
Cells in an EMT hybrid state, which are caused by incomplete transitions during the EMT process in cancer cells, have the characteristics of both epithelial cells and mesenchymal cells, and are known to have high drug resistance and metastatic potential by acquiring high stem cell capacity. In particular, EMT is further enhanced through factors such as transforming growth factor-beta (TGF-β) secreted from the tumor microenvironment (TME) and, as a result, various cell states with high plasticity appear. Due to the complexity of EMT, it has been very difficult to completely reverse the transitional process of the mesenchymal cancer cells to an epithelial cell state in which metastatic ability and drug resistance are eliminated while avoiding the EMT hybrid cell state with high metastatic ability and drug resistance.
Professor Kwang-Hyun Cho's research team established a mathematical model of the gene regulation network that governs the complex process of EMT, and then applied large-scale computer simulation analysis and complex system network control technology to identify and verify 'p53', 'SMAD4', and 'ERK1' and 'ERK 2' (collectively ERKs) through molecular cell experiments as the three key molecular targets that can transform lung cancer cells in the mesenchymal cell state, reversed back to an epithelial cell state that no longer demonstrates the ability to metastasize, while avoiding the EMT hybrid cell state.
In particular, by analyzing the molecular regulatory mechanism of the complex EMT process at the system level, the key pathways were identified that were linked to the positive feedback that plays an important role in completely returning cancer cells to an epithelial cell state in which metastatic ability and drug resistance are removed.
This discovery is significant in that it proved that mesenchymal cells can be reverted to the state of epithelial cells under conditions where TGF-β stimulation are present, like they are in the actual environment where cancer tissue forms in the human body.
Abnormal EMT in cancer cells leads to various malignant traits such as the migration and invasion of cancer cells, changes in responsiveness to chemotherapy treatment, enhanced stem cell function, and the dissemination of cancer. In particular, the acquisition of the metastatic ability of cancer cells is a key determinant factor for the prognosis of cancer patients. The EMT reversal technology in lung cancer cells developed in this research is a new anti-cancer treatment strategy that reprograms cancer cells to eliminate their high plasticity and metastatic potential and increase their responsiveness to chemotherapy.
Professor Kwang-Hyun Cho said, "By succeeding in reversing the state of lung cancer cells that acquired high metastatic traits and resistance to drugs and reverting them to a treatable epithelial cell state with renewed sensitivity to chemotherapy, the research findings propose a new strategy for treatments that can improve the prognosis of cancer patients.”
Professor Kwang-Hyun Cho's research team was the first to present the principle of reversal treatment to revert cancer cells to normal cells, following through with the announcement of the results of their study that reverted colon cancer cells to normal colon cells in January of 2020, and also presenting successful re-programming research where the most malignant basal type breast cancer cells turned into less-malignant luminal type breast cancer cells that were treatable with hormonal therapies in January of 2022. This latest research result is the third in the development of reversal technology where lung cancer cells that had acquired metastatic traits returned to a state in which their metastatic ability was removed and drug sensitivity was enhanced.
This research was carried out with support from the Ministry of Science and ICT and the National Research Foundation of Korea's Basic Research in Science & Engineering Program for Mid-Career Researchers.
< Figure 1. Construction of the mathematical model of the regulatory network to represent the EMT phenotype based on the interaction between various molecules related to EMT.
(A) Professor Kwang-Hyun Cho's research team investigated numerous literatures and databases related to complex EMT, and based on comparative analysis of cell line data showing epithelial and mesenchymal cell conditions, they extracted key signaling pathways related to EMT and built a mathematical model of regulatory network (B) By comparing the results of computer simulation analysis and the molecular cell experiments, it was verified how well the constructed mathematical model simulated the actual cellular phenomena. >
< Figure 2. Understanding of various EMT phenotypes through large-scale computer simulation analysis and complex system network control technology.
(A) Through computer simulation analysis and experiments, Professor Kwang-Hyun Cho's research team found that complete control of EMT is impossible with single-molecule control alone. In particular, through comparison of the relative stability of attractors, it was revealed that the cell state exhibiting EMT hybrid characteristics has unstable properties. (B), (C) Based on these results, Prof. Cho’s team identified two feedbacks (positive feedback consisting of Snail-miR-34 and ZEB1-miR-200) that play an important role in avoiding the EMT hybrid state that appeared in the TGF-β-ON state. It was found through computer simulation analysis that the two feedbacks restore relatively high stability when the excavated p53 and SMAD4 are regulated. In addition, molecular cell experiments demonstrated that the expression levels of E-cad and ZEB1, which are representative phenotypic markers of EMT, changed similarly to the expression profile in the epithelial cell state, despite the TGF-β-ON state. >
< Figure 3. Complex molecular network analysis and discovery of reprogramming molecular targets for intact elimination of EMT hybrid features.
(A) Controlling the expression of p53 and SMAD4 in lung cancer cell lines was expected to overcome drug resistance, but contrary to expectations, chemotherapy responsiveness was not restored. (B) Professor Kwang-Hyun Cho's research team additionally analyzed computer simulations, genome data, and experimental results and found that high expression levels of TWIST1 and EPCAM were related to drug resistance. (C) Prof. Cho’s team identified three key molecular targets: p53, SMAD4 and ERK1 & ERK2. (D), (E) Furthermore, they identified a key pathway that plays an important role in completely reversing into epithelial cells while avoiding EMT hybrid characteristics, and confirmed through network analysis and attractor analysis that high stability of the key pathway was restored when the proposed molecular target was controlled. >
< Figure 4. Verification through experiments with lung cancer cell lines.
When p53 was activated and SMAD4 and ERK1/2 were inhibited in lung cancer cell lines, (A), (B) E-cad protein expression increased and ZEB1 protein expression decreased, and (C) mesenchymal cell status including TWIST1 and EPCAM and gene expression of markers related to stem cell potential characteristics were completely inhibited. In addition, (D) it was confirmed that resistance to chemotherapy treatment was also overcome as the cell state was reversed by the regulated target. >
< Figure 5. A schematic representation of the research results.
Prof. Cho’s research team identified key molecular regulatory pathways to avoid high plasticity formed by abnormal EMT of cancer cells and reverse it to an epithelial cell state through systems biology research. From this analysis, a reprogramming molecular target that can reverse the state of mesenchymal cells with acquired invasiveness and drug resistance to the state of epithelial cells with restored drug responsiveness was discovered.
For lung cancer cells, when a drug that enhances the expression of p53, one of the molecular targets discovered, and inhibits the expression of SMAD4 and ERK1 & ERK2 is administered, the molecular network of genes in the state of mesenchymal cells is modified, eventually eliminating metastatic ability and it is reprogrammed to turn into epithelial cells without the resistance to chemotherapy treatments. >
2023.01.30 View 26910 -
KAIST to showcase a pack of KAIST Start-ups at CES 2023
- KAIST is to run an Exclusive Booth at the Venetian Expo (Hall G) in Eureka Park, at CES 2023, to be held in Las Vegas from Thursday, January 5th through Sunday, the 8th.
- Twelve businesses recently put together by KAIST faculty, alumni, and the start-ups given legal usage of KAIST technologies will be showcased.
- Out of the participating start-ups, the products by Fluiz and Hills Robotics were selected as the “CES Innovation Award 2023 Honoree”, scoring top in their respective categories.
On January 3, KAIST announced that there will be a KAIST booth at Consumer Electronics Show (CES) 2023, the most influential tech event in the world, to be held in Las Vegas from January 3 to 8.
At this exclusive corner, KAIST will introduce the technologies of KAIST start-ups over the exhibition period.
KAIST first started holding its exclusive booth in CES 2019 with five start-up businesses, following up at CES 2020 with 12 start-ups and at CES 2022 with 10 start-ups. At CES 2023, which would be KAIST’s fourth conference, KAIST will be accompanying 12 businesses including start-ups by the faculty members, alumni, and technology transfer companies that just began their businesses with technologies from their research findings that stands a head above others.
To maximize the publicity opportunity, KAIST will support each company’s marketing strategies through cooperation with the Korea International Trade Association (KITA), and provide an opportunity for the school and each startup to create global identity and exhibit the excellence of their technologies at the convention.
The following companies will be at the KAIST Booth in Eureka Park:
The twelve startups mentioned above aim to achieve global technology commecialization in their respective fields of expertise spanning from eXtended Reality (XR) and gaming, to AI and robotics, vehicle and transport, mobile platform, smart city, autonomous driving, healthcare, internet of thing (IoT), through joint research and development, technology transfer and investment attraction from world’s leading institutions and enterprises.
In particular, Fluiz and Hills Robotics won the CES Innovation Award as 2023 Honorees and is expected to attain greater achievements in the future.
A staff member from the KAIST Institute of Technology Value Creation said, “The KAIST Showcase for CES 2023 has prepared a new pitching space for each of the companies for their own IR efforts, and we hope that KAIST startups will actively and effectively market their products and technologies while they are at the convention. We hope it will help them utilize their time here to establish their name in presence here which will eventually serve as a good foothold for them and their predecessors to further global commercialization goals.”
2023.01.04 View 29295