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'Team Atlanta', in which KAIST Professor Insu Yun research team participated, won the DARPA AI Cyber Challenge in the US, with a prize of 5.5 billion KRW
<Photo1. Group Photo of Team Atlanta>
Team Atlanta, led by Professor Insu Yun of the Department of Electrical and Electronic Engineering at KAIST and Tae-soo Kim, an executive from Samsung Research, along with researchers from POSTECH and Georgia Tech, won the final championship at the AI Cyber Challenge (AIxCC) hosted by the Defense Advanced Research Projects Agency (DARPA). The final was held at the world's largest hacking conference, DEF CON 33, in Las Vegas on August 8 (local time).
With this achievement, the team won a prize of $4 million (approximately 5.5 billion KRW), demonstrating the excellence of their AI-based autonomous cyber defense technology on the global stage.
<Photo2.Championship Commemorative:On the left and right are tournament officials. From the second person, Professor Tae-soo Kim(Samsung Research / Georgia Tech), Researcher Hyeong-seok Han (Samsung Research America), and Professor Insu Yun (KAIST)>
The AI Cyber Challenge is a two-year global competition co-hosted by DARPA and the Advanced Research Projects Agency for Health (ARPA-H). It challenges contestants to automatically analyze, detect, and fix software vulnerabilities using AI-based Cyber Reasoning Systems (CRS). The total prize money for the competition is $29.5 million, with the winning team receiving $4 million.
In the final, Team Atlanta scored a total of 392.76 points, a difference of over 170 points from the second-place team, Trail of Bits, securing a dominant victory. The CRS developed by Team Atlanta successfully and automatically detected various types of vulnerabilities and patched a significant number of them in real time.
Among the 7 finalist teams, an average of 77% of the 70 intentionally injected vulnerabilities were found, and 61% of them were patched. The teams also found 18 additional unknown vulnerabilities in real software, proving the potential of AI security technology.
All CRS technologies, including those of the winning team, will be provided as open-source and are expected to be used to strengthen the security of core infrastructure such as hospitals, water, and power systems.
<Photo3. Final Scoreboard: An overwhelming victory with over 170 points>
Professor Insu Yun of KAIST, a member of Team Atlanta, stated, "I am very happy to have achieved such a great result. This is a remarkable achievement that shows Korea's cyber security research has reached the highest level in the world, and it was meaningful to show the capabilities of Korean researchers on the world stage. I will continue to conduct research to protect the digital safety of the nation and global society through the fusion of AI and security technology."
KAIST President Kwang-hyung Lee stated, "This victory is another example that proves KAIST is a world-leading institution in the field of future cyber security and AI convergence. We will continue to provide full support to our researchers so they can compete and produce results on the world stage."
<Photo4. Results Announcement>
2025.08.10 View 99 -
Key Figures in the Establishment of KAIST, Specially Invited to the Presidential Office’s National Appointment Ceremony
KAIST announced on August 6 that Professor Emeritus Jung-Woong Ra from the Department of Electrical Engineering and Won-ki Kwon, former Vice Minister of the Ministry of Science and Technology, who played pivotal roles in the establishment of KAIST, were selected as special guests for the 'National Appointment Ceremony' hosted by the Presidential Office on August 15th.
The Presidential Office selected special invitees across eight categories for the ceremony. These include individuals born in 1945 (referred to as 'Liberation Babies'), those involved in the founding of KAIST in 1971, independence activists and national patriots, overseas workers in Germany and the Middle East, AI industry professionals, residents from regions facing depopulation, leading figures in K-culture, military personnel, firefighters, police officers, families of fallen public servants and victims of social disasters, as well as promising talents in economics, science, culture, and the arts.
Considering the historical significance of its establishment and its symbolic meaning for the development of national science and technology, KAIST Professor Emeritus Jung-Woong Ra, who was a key figure in the establishment of the Department of Electrical Engineering after being appointed as a professor in 1971, and former Vice Minister Kwon Won-ki, who was the first practical leader of the establishment project. Both were officially included on the special invitation list.
Briefing from the Presidential Office regarding the 'National Appointment Ceremony' (2025.07.28) https://www.president.go.kr/newsroom/briefing/grehGMuP
2025.08.06 View 155 -
KAIST Develops AI ‘MARIOH’ to Uncover and Reconstruct Hidden Multi-Entity Relationships
<(From Left) Professor Kijung Shin, Ph.D candidate Kyuhan Lee, and Ph.D candidate Geon Lee>
Just like when multiple people gather simultaneously in a meeting room, higher-order interactions—where many entities interact at once—occur across various fields and reflect the complexity of real-world relationships. However, due to technical limitations, in many fields, only low-order pairwise interactions between entities can be observed and collected, which results in the loss of full context and restricts practical use. KAIST researchers have developed the AI model “MARIOH,” which can accurately reconstruct* higher-order interactions from such low-order information, opening up innovative analytical possibilities in fields like social network analysis, neuroscience, and life sciences.
*Reconstruction: Estimating/reconstructing the original structure that has disappeared or was not observed.
KAIST (President Kwang Hyung Lee) announced on the 5th that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed an AI technology called “MARIOH” (Multiplicity-Aware Hypergraph Reconstruction), which can reconstruct higher-order interaction structures with high accuracy using only low-order interaction data.
Reconstructing higher-order interactions is challenging because a vast number of higher-order interactions can arise from the same low-order structure.
The key idea behind MARIOH, developed by the research team, is to utilize multiplicity information of low-order interactions to drastically reduce the number of candidate higher-order interactions that could stem from a given structure.
In addition, by employing efficient search techniques, MARIOH quickly identifies promising interaction candidates and uses multiplicity-based deep learning to accurately predict the likelihood that each candidate represents an actual higher-order interaction.
<Figure 1. An example of recovering high-dimensional relationships (right) from low-dimensional paper co-authorship relationships (left) with 100% accuracy, using MARIOH technology.>
Through experiments on ten diverse real-world datasets, the research team showed that MARIOH reconstructed higher-order interactions with up to 74% greater accuracy compared to existing methods.
For instance, in a dataset on co-authorship relations (source: DBLP), MARIOH achieved a reconstruction accuracy of over 98%, significantly outperforming existing methods, which reached only about 86%. Furthermore, leveraging the reconstructed higher-order structures led to improved performance in downstream tasks, including prediction and classification.
According to Kijung, “MARIOH moves beyond existing approaches that rely solely on simplified connection information, enabling precise analysis of the complex interconnections found in the real world.” Furthermore, “it has broad potential applications in fields such as social network analysis for group chats or collaborative networks, life sciences for studying protein complexes or gene interactions, and neuroscience for tracking simultaneous activity across multiple brain regions.”
The research was conducted by Kyuhan Lee (Integrated M.S.–Ph.D. program at the Kim Jaechul Graduate School of AI at KAIST; currently a software engineer at GraphAI), Geon Lee (Integrated M.S.–Ph.D. program at KAIST), and Professor Kijung Shin. It was presented at the 41st IEEE International Conference on Data Engineering (IEEE ICDE), held in Hong Kong this past May.
※ Paper title: MARIOH: Multiplicity-Aware Hypergraph Reconstruction ※ DOI: https://doi.ieeecomputersociety.org/10.1109/ICDE65448.2025.00233
<Figure 2. An example of the process of recovering high-dimensional relationships using MARIOH technology>
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the project “EntireDB2AI: Foundational technologies and software for deep representation learning and prediction using complete relational databases,” as well as by the National Research Foundation of Korea through the project “Graph Foundation Model: Graph-based machine learning applicable across various modalities and domains.”
2025.08.05 View 207 -
Is 24-hour health monitoring possible with ambient light energy?
<(From left) Ph.D candidate Youngmin Sim, Ph.D candidate Do Yun Park, Dr. Chanho Park, Professor Kyeongha Kwon>
Miniaturization and weight reduction of medical wearable devices for continuous health monitoring such as heart rate, blood oxygen saturation, and sweat component analysis remain major challenges. In particular, optical sensors consume a significant amount of power for LED operation and wireless transmission, requiring heavy and bulky batteries. To overcome these limitations, KAIST researchers have developed a next-generation wearable platform that enables 24-hour continuous measurement by using ambient light as an energy source and optimizing power management according to the power environment.
KAIST (President Kwang Hyung Lee) announced on the 30th that Professor Kyeongha Kwon's team from the School of Electrical Engineering, in collaboration with Dr. Chanho Park’s team at Northwestern University in the U.S., has developed an adaptive wireless wearable platform that reduces battery load by utilizing ambient light.
To address the battery issue of medical wearable devices, Professor Kyeongha Kwon’s research team developed an innovative platform that utilizes ambient natural light as an energy source. This platform integrates three complementary light energy technologies.
<Figure1.The wireless wearable platform minimizes the energy required for light sources through i) Photometric system that directly utilizes ambient light passing through windows for measurements, ii) Photovoltaic system that receives power from high-efficiency photovoltaic cells and wireless power receiver coils, and iii) Photoluminescent system that stores light using photoluminescent materials and emits light in dark conditions to support the two aforementioned systems. In-sensor computing minimizes power consumption by wirelessly transmitting only essential data. The adaptive power management system efficiently manages power by automatically selecting the optimal mode among 11 different power modes through a power selector based on the power supply level from the photovoltaic system and battery charge status.>
The first core technology, the Photometric Method, is a technique that adaptively adjusts LED brightness depending on the intensity of the ambient light source. By combining ambient natural light with LED light to maintain a constant total illumination level, it automatically dims the LED when natural light is strong and brightens it when natural light is weak.
Whereas conventional sensors had to keep the LED on at a fixed brightness regardless of the environment, this technology optimizes LED power in real time according to the surrounding environment. Experimental results showed that it reduced power consumption by as much as 86.22% under sufficient lighting conditions.
The second is the Photovoltaic Method using high-efficiency multijunction solar cells. This goes beyond simple solar power generation to convert light in both indoor and outdoor environments into electricity. In particular, the adaptive power management system automatically switches among 11 different power configurations based on ambient conditions and battery status to achieve optimal energy efficiency.
The third innovative technology is the Photoluminescent Method. By mixing strontium aluminate microparticles* into the sensor’s silicone encapsulation structure, light from the surroundings is absorbed and stored during the day and slowly released in the dark. As a result, after being exposed to 500W/m² of sunlight for 10 minutes, continuous measurement is possible for 2.5 minutes even in complete darkness.
*Strontium aluminate microparticles: A photoluminescent material used in glow-in-the-dark paint or safety signs, which absorbs light and emits it in the dark for an extended time.
These three technologies work complementarily—during bright conditions, the first and second methods are active, and in dark conditions, the third method provides additional support—enabling 24-hour continuous operation.
The research team applied this platform to various medical sensors to verify its practicality. The photoplethysmography sensor monitors heart rate and blood oxygen saturation in real time, allowing early detection of cardiovascular diseases. The blue light dosimeter accurately measures blue light, which causes skin aging and damage, and provides personalized skin protection guidance. The sweat analysis sensor uses microfluidic technology to simultaneously analyze salt, glucose, and pH in sweat, enabling real-time detection of dehydration and electrolyte imbalances.
Additionally, introducing in-sensor data computing significantly reduced wireless communication power consumption. Previously, all raw data had to be transmitted externally, but now only the necessary results are calculated and transmitted within the sensor, reducing data transmission requirements from 400B/s to 4B/s—a 100-fold decrease.
To validate performance, the research tested the device on healthy adult subjects in four different environments: bright indoor lighting, dim lighting, infrared lighting, and complete darkness. The results showed measurement accuracy equivalent to that of commercial medical devices in all conditions A mouse model experiment confirmed accurate blood oxygen saturation measurement in hypoxic conditions.
<Frigure2.The multimodal device applying the energy harvesting and power management platform consists of i) photoplethysmography (PPG) sensor, ii) blue light dosimeter, iii) photoluminescent microfluidic channel for sweat analysis and biomarker sensors (chloride ion, glucose, and pH), and iv) temperature sensor. This device was implemented with flexible printed circuit board (fPCB) to enable attachment to the skin. A silicon substrate with a window that allows ambient light and measurement light to pass through, along with photoluminescent encapsulation layer, encapsulates the PPG, blue light dosimeter, and temperature sensors, while the photoluminescent microfluidic channel is attached below the photoluminescent encapsulation layer to collect sweat>
Professor Kyeongha Kwon of KAIST, who led the research, stated, “This technology will enable 24-hour continuous health monitoring, shifting the medical paradigm from treatment-centered to prevention-centered shifting the medical paradigm from treatment-centered to prevention-centered,” further stating that “cost savings through early diagnosis as well as strengthened technological competitiveness in the next-generation wearable healthcare market are anticipated.”
This research was published on July 1 in the international journal Nature Communications, with Do Yun Park, a doctoral student in the AI Semiconductor Graduate Program, as co–first author.
※ Paper title: Adaptive Electronics for Photovoltaic, Photoluminescent and Photometric Methods in Power Harvesting for Wireless and Wearable Sensors ※ DOI: https://doi.org/10.1038/s41467-025-60911-1 ※ URL: https://www.nature.com/articles/s41467-025-60911-1
This research was supported by the National Research Foundation of Korea (Outstanding Young Researcher Program and Regional Innovation Leading Research Center Project), the Ministry of Science and ICT and Institute of Information & Communications Technology Planning & Evaluation (IITP) AI Semiconductor Graduate Program, and the BK FOUR Program (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE).
2025.07.30 View 433 -
KAIST Enables On-Site Disease Diagnosis in Just 3 Minutes... Nanozyme Reaction Selectivity Improved 38-Fold
<(From Left) Professor Jinwoo Lee, Ph.D candidate Seonhye Park and Ph.D candidate Daeeun Choi from Chemical & Biomolecular Engineering>
To enable early diagnosis of acute illnesses and effective management of chronic conditions, point-of-care testing (POCT) technology—diagnostics conducted near the patient—is drawing global attention. The key to POCT lies in enzymes that recognize and react precisely with specific substances. However, traditional natural enzymes are expensive and unstable, and nanozymes (enzyme-mimicking catalysts) have suffered from low reaction selectivity. Now, a Korean research team has developed a high-sensitivity sensor platform that achieves 38 times higher selectivity than existing nanozymes and allows disease diagnostics visible to the naked eye within just 3 minutes.
On the 28th, KAIST (President Kwang Hyung Lee) announced that Professor Jinwoo Lee’s research team from the Department of Chemical & Biomolecular Engineering, in collaboration with teams led by Professor Jeong Woo Han at Seoul National University and Professor Moon Il Kim at Gachon University, has developed a new single-atom catalyst that selectively performs only peroxidase-like reactions while maintaining high reaction efficiency.
Using bodily fluids such as blood, urine, or saliva, this diagnostic platform enables test results to be read within minutes even outside hospital settings—greatly improving medical accessibility and ensuring timely treatment. The key lies in the visual detection of biomarkers (disease indicators) through color changes triggered by enzyme reactions. However, natural enzymes are expensive and easily degraded in diagnostic environments, limiting their storage and distribution.
To address this, inorganic nanozyme materials have been developed as substitutes. Yet, they typically lack selectivity—when hydrogen peroxide is used as a substrate, the same catalyst triggers both peroxidase-like reactions (which cause color change) and catalase-like reactions (which remove the substrate), reducing diagnostic signal accuracy.
To control catalyst selectivity at the atomic level, the researchers used an innovative structural design: attaching chlorine (Cl) ligands in a three-dimensional configuration to the central ruthenium (Ru) atom to fine-tune its chemical properties. This enabled them to isolate only the desired diagnostic signal.
<Figure1. The catalyst in this study (ruthenium single-atom catalyst) exhibits peroxidase-like activity with selectivity akin to natural enzymes through three-dimensional directional ligand coordination. Due to the absence of competing catalase activity, selective peroxidase-like reactions proceed under biomimetic conditions. In contrast, conventional single-atom catalysts with active sites arranged on planar surfaces exhibit dual functionality depending on pH. Under neutral conditions, their catalase activity leads to hydrogen peroxide depletion, hindering accurate detection. The catalyst in this study eliminates such interference, enabling direct detection of biomarkers through coupled reactions with oxidases without the need for cumbersome steps like buffer replacement. The ability to simultaneously detect multiple target substances under biomimetic conditions demonstrates the practicality of ruthenium single-atom catalysts for on-site diagnostics>
Experimental results showed that the new catalyst achieved over 38-fold improvement in selectivity compared to existing nanozymes, with significantly increased sensitivity and speed in detecting hydrogen peroxide. Even in near-physiological conditions (pH 6.0), the catalyst maintained its performance, proving its applicability in real-world diagnostics.
By incorporating the catalyst and oxidase into a paper-based sensor, the team created a system that could simultaneously detect four key biomarkers related to health: glucose, lactate, cholesterol, and choline—all with a simple color change.
This platform is broadly applicable across various disease diagnostics and can deliver results within 3 minutes without complex instruments or pH adjustments. The findings show that diagnostic performance can be dramatically improved without changing the platform itself, but rather by engineering the catalyst structure.
<Figure 2.(a) Schematic diagram of the paper sensor (Zone 1: glucose oxidase immobilized; Zone 2: lactate oxidase immobilized; Zone 3: choline oxidase immobilized; Zone 4: cholesterol oxidase immobilized; Zone 5: no oxidase enzyme). (b) Single biomarker (single disease indicator) detection using the ruthenium single‑atom catalyst–based paper sensor.(c) Multiple biomarker (multiple disease indicator) detection using the ruthenium single‑atom catalyst–based paper sensor>
Professor Jinwoo Lee of KAIST commented, “This study is significant in that it simultaneously achieves enzyme-level selectivity and reactivity by structurally designing single-atom catalysts.” He added that “the structure–function-based catalyst design strategy can be extended to the development of various metal-based catalysts and other reaction domains where selectivity is critical.”
Seonhye Park and Daeeun Choi, both Ph.D. candidates at KAIST, are co-first authors. The research was published on July 6, 2025, in the prestigious journal Advanced Materials
-Title: Breaking the Selectivity Barrier of Single-Atom Nanozymes Through Out-of-Plane Ligand Coordinatio
- Authors: Seonhye Park (KAIST, co–first author), Daeeun Choi (KAIST, co–first author), Kyu In Shim (SNU, co–first author), Phuong Thy Nguyen (Gachon Univ., co–first author), Seongbeen Kim (KAIST), Seung Yeop Yi (KAIST), Moon Il Kim (Gachon Univ., corresponding author), Jeong Woo Han (SNU, corresponding author), Jinwoo Lee (KAIST, corresponding author
-DOI: https://doi.org/10.1002/adma.202506480
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea (NRF).
2025.07.29 View 320 -
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 556 -
Approaches to Human-Robot Interaction Using Biosignals
<(From left) Dr. Hwa-young Jeong, Professor Kyung-seo Park, Dr. Yoon-tae Jeong, Dr. Ji-hoon Seo, Professor Min-kyu Je, Professor Jung Kim >
A joint research team led by Professor Jung Kim of KAIST Department of Mechanical Engineering and Professor Min-kyu Je of the Department of Electrical and Electronic Engineering recently published a review paper on the latest trends and advancements in intuitive Human-Robot Interaction (HRI) using bio-potential and bio-impedance in the internationally renowned academic journal 'Nature Reviews Electrical Engineering'.
This review paper is the result of a collaborative effort by Dr. Kyung-seo Park (DGIST, co-first author), Dr. Hwa-young Jeong (EPFL, co-first author), Dr. Yoon-tae Jeong (IMEC), and Dr. Ji-hoon Seo (UCSD), all doctoral graduates from the two laboratories. Nature Reviews Electrical Engineering is a review specialized journal in the field of electrical, electronic, and artificial intelligence technology, newly launched by Nature Publishing Group last year. It is known to invite world-renowned scholars in the field through strict selection criteria. Professor Jung Kim's research team's paper, titled "Using bio-potential and bio-impedance for intuitive human-robot interaction," was published on July 18, 2025. (DOI: https://doi.org/10.1038/s44287-025-00191-5)
This review paper explains how biosignals can be used to quickly and accurately detect movement intentions and introduces advancements in movement prediction technology based on neural signals and muscle activity. It also focuses on the crucial role of integrated circuits (ICs) in maximizing low-noise performance and energy efficiency in biosignal sensing, covering thelatest development trends in low-noise, low-power designs for accurately measuring bio-potential and impedance signals.
The review emphasizes the importance of hybrid and multi-modal sensing approaches, presenting the possibility of building robust, intuitive, and scalable HRI systems. The research team stressed that collaboration between sensor and IC design fields is essential for the practical application of biosignal-based HRI systems and stated that interdisciplinary collaboration will play a significant role in the development of next-generation HRI technology. Dr. Hwa-young Jeong, a co-first author of the paper, presented the potential of bio-potential and impedance signals to make human-robot interaction more intuitive and efficient, predicting that it will make significant contributions to the development of HRI technologies such as rehabilitation robots and robotic prostheses using biosignals in the future. This research was supported by several research projects, including the Human Plus Project of the National Research Foundation of Korea.
2025.07.24 View 392 -
KAIST Designs a New Atomic Catalyst for Air Pollution Reduction
<(From Left)Professor Jong Hun Kim from Inha University, Dr. Gyuho Han and Professor Jeong Young Park from KAIST>
Platinum diselenide (PtSe2) is a two-dimensional multilayer material in which each layer is composed of platinum (Pt) and selenium (Se). It is known that its excellent crystallinity and precise control of interlayer interactions allow modulation of various physical and chemical properties. Due to these characteristics, it has been actively researched in multiple fields, including semiconductors, photodetectors, and electrochemical devices. Now, a research team has proposed a new design concept in which atomically dispersed platinum on the surface of platinum diselenide can function as a catalyst for gas reactions. Through this, they have proven its potential as a next-generation gas-phase catalyst technology for high-efficiency carbon dioxide conversion and carbon monoxide reduction.
KAIST (President Kwang Hyung Lee) announced on July 22 that a joint research team led by Endowed Chair Professor Jeong Young Park from the Department of Chemistry, along with Professor Hyun You Kim's team from Chungnam National University and Professor Yeonwoong (Eric) Jung's team from the University of Central Florida (UCF), has achieved excellent carbon monoxide oxidation performance by utilizing platinum atoms exposed on the surface of platinum diselenide, a type of two-dimensional transition metal dichalcogenide (TMD).
To maximize catalytic performance, the research team designed the catalyst by dispersing platinum atoms uniformly across the surface, departing from the conventional use of bulk platinum. This strategy allows more efficient catalytic reactions using a smaller amount of platinum. It also enhances electronic interactions between platinum and selenium by tuning the surface electronic structure. As a result, the platinum diselenide film with a thickness of a few nanometers showed superior carbon monoxide oxidation performance across the entire temperature range compared to a conventional platinum thin film under identical conditions.
In particular, carbon monoxide and oxygen were evenly adsorbed on the surface in similar proportions, increasing the likelihood that they would encounter each other and react, which significantly enhanced the catalytic activity. This improvement is primarily attributed to the increased exposure of surface platinum atoms resulting from selenium vacancies (Se-vacancies), which provide adsorption sites for gas molecules.
The research team confirmed in real-time that these platinum atoms served as active adsorption sites during the actual reaction process, using ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) conducted at the Pohang Accelerator Laboratory. This high-precision analysis was enabled by advanced instrumentation capable of observing surfaces at the nanometer scale under ambient pressure conditions. At the same time, computer simulations based on density functional theory (DFT) demonstrated that platinum diselenide exhibits distinct electronic behavior compared to conventional platinum.
*Density Functional Theory (DFT): A quantum mechanical method for calculating the total energy of a system based on electron density.
Professor Jeong Young Park stated, “This research presents a new design strategy that utilizes platinum diselenide, a two-dimensional layered material distinct from conventional platinum catalysts, to enable catalytic functions optimized for gas-phase reactions.” He added, “The electronic interaction between platinum and selenium created favorable conditions for the balanced adsorption of carbon monoxide and oxygen. By designing the catalyst to exhibit higher reactivity across the entire temperature range than conventional platinum, we improved its practical applicability. This enabled a high-efficiency catalytic reaction mechanism through atomic-level design, a two-dimensional material platform, and precise adsorption control.”
This research was co-authored by Dr. Gyuho Han from the Department of Chemistry at KAIST, Dr. Hyuk Choi from the Department of Materials Science and Engineering at Chungnam National University, and Professor Jong Hun Kim from Inha University. The study was published on July 3 in the world-renowned journal Nature Communications.
Paper Title: Enhanced catalytic activity on atomically dispersed PtSe2 two-dimensional layers
DOI: 10.1038/s41467-025-61320-0
This research was supported by the Mid-Career Researcher Program of the Ministry of Science and ICT, the Core Research Institute Program of the Ministry of Education, the National Strategic Technology Materials Development Project, the U.S. National Science Foundation (NSF) CAREER Program, research funding from Inha University, and the Postdoctoral Researcher Program (P3) at UCF. Accelerator-based analysis was conducted in cooperation with the Pohang Accelerator Laboratory and the Korea Basic Science Institute (KBSI).
2025.07.22 View 380 -
KAIST Develops Robots That React to Danger Like Humans
<(From left) Ph.D candidate See-On Park, Professor Jongwon Lee, and Professor Shinhyun Choi>
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
KAIST (President Kwang Hyung Lee) announced on July15th that a joint research team led by Endowed Chair Professor Shinhyun Choi of the School of Electrical Engineering at KAIST and Professor Jongwon Lee of the Department of Semiconductor Convergence at Chungnam National University (President Jung Kyum Kim) developed a next-generation neuromorphic semiconductor-based artificial sensory nervous system. This system mimics the functions of a living organism's sensory nervous system, and enables a new type of robotic system that can efficiently responds to external stimuli.
In nature, animals — including humans — ignore safe or familiar stimuli and selectively react sensitively to important or dangerous ones. This selective response helps prevent unnecessary energy consumption while maintaining rapid awareness of critical signals. For instance, the sound of an air conditioner or the feel of clothing against the skin soon become familiar and are disregarded. However, if someone calls your name or a sharp object touches your skin, a rapid focus and response occur. These behaviors are regulated by the 'habituation' and 'sensitization' functions in the sensory nervous system. Attempts have been consistently made to apply these sensory nervous system functions of living organisms in order to create robots that efficiently respond to external environments like humans.
However, implementing complex neural characteristics such as habituation and sensitization in robots has faced difficulties in miniaturization and energy efficiency due to the need for separate software or complex circuitry. In particular, there have been attempts to utilize memristors, a neuromorphic semiconductor. A memristor is a next-generation electrical device, which has been widely utilized as an artificial synapse due to its ability to store analog value in the form of device resistance. However, existing memristors had limitations in mimicking the complex characteristics of the nervous system because they only allowed simple monotonic changes in conductivity.
To overcome these limitations, the research team developed a new memristor capable of reproducing complex neural response patterns such as habituation and sensitization within a single device. By introducing additional layer inside the memristor that alter conductivity in opposite directions, the device can more realistically emulate the dynamic synaptic behaviors of a real nervous system — for example, decreasing its response to repeated safe stimuli but quickly regaining sensitivity when a danger signal is detected.
<New memristor mimicking functions of sensory nervous system such as habituation/sensitization>
Using this new memristor, the research team built an artificial sensory nervous system capable of recognizing touch and pain, an applied it to a robotic hand to test its performance. When safe tactile stimuli were repeatedly applied, the robot hand, which initially reacted sensitively to unfamiliar tactile stimuli, gradually showed habituation characteristics by ignoring the stimuli. Later, when stimuli were applied along with an electric shock, it recognized this as a danger signal and showed sensitization characteristics by reacting sensitively again. Through this, it was experimentally proven that robots can efficiently respond to stimuli like humans without separate complex software or processors, verifying the possibility of developing energy-efficient neuro-inspired robots.
<Robot arm with memristor-based artificial sensory nervous system>
See-On Park, researcher at KAIST, stated, "By mimicking the human sensory nervous system with next-generation semiconductors, we have opened up the possibility of implementing a new concept of robots that are smarter and more energy-efficient in responding to external environments." He added, "This technology is expected to be utilized in various fusion fields of next-generation semiconductors and robotics, such as ultra-small robots, military robots, and medical robots like robotic prosthetics".
This research was published online on July 1st in the international journal 'Nature Communications,' with Ph.D candidate See-On Park as the first author.
Paper Title: Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics
DOI: https://doi.org/10.1038/s41467-025-60818-x
This research was supported by the Korea National Research Foundation's Next-Generation Intelligent Semiconductor Technology Development Project, the Mid-Career Researcher Program, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the Excellent New Researcher Program, and the Nano Convergence Technology Division, National Nanofab Center's (NNFC) Nano-Medical Device Project.
2025.07.16 View 662 -
Professor Jung-woo' Choi ‘s Team Comes in First at the World's Top Acoustic AI Challenge
<Photo1. (From left) Ph.D candidate Yong-hoo Kwon, M.S candidate Do-hwan Kim, Professor Jung-woo Choi, Dr. Dong-heon Lee>
'Acoustic separation and classification technology' is a next-generation artificial intelligence (AI) core technology that enables the early detection of abnormal sounds in areas such as drones, fault detection of factory pipelines, and border surveillance systems, or allows for the separation and editing of spatial audio by sound source when producing AR/VR content.
On the 11th of July, a research team led by Professor Jung-woo Choi of KAIST's Department of Electrical and Electronic Engineering won first place in the 'Spatial Semantic Segmentation of Sound Scenes' task of the 'DCASE2025 Challenge,' the world's most prestigious acoustic detection and analysis competition.
This year’s challenge featured 86 teams competing across six tasks. In this competition, the KAIST research team achieved the best performance in their first-ever participation to Task 4. Professor Jung-woo Choi’s research team consisted of Dr. Dong-heon, Lee, Ph.D. candidate Young-hoo Kwon, and M.S. candidate Do-hwan Kim.
Task 4 titled 'Spatial Semantic Segmentation of Sound Scenes' is a highly demanding task requiring the analysis of spatial information in multi-channel audio signals with overlapping sound sources. The goal was to separate individual sounds and classify them into 18 predefined categories. The research team plans to present their technology at the DCASE workshop in Barcelona this October.
<Figure 1. Example of an acoustic scene with multiple mixed sounds>
Early this year, Dr. Dong-heon Lee developed a state-of-the-art sound source separation AI that combines Transformer and Mamba architectures. During the competition, centered around researcher Young-hoo Kwon, they completed a ‘chain-of-inference architecture' AI model that performs sound source separation and classification again, using the waveforms and types of the initially separated sound sources as clues. This AI model is inspired by human’s auditory scene analysis mechanism that isolates individual sounds by focusing on incomplete clues such as sound type, rhythm, or direction, when listening to complex sounds.
Through this, the team was the only participant to achieve double-digit performance (11 dB) in 'Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi)*,' which is the measure for ranking how well the AI separated and classified sounds, proving their technical excellence.
Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi): Measures how much clearer (less distorted) the desired sound is separated and classified compared to the original audio, in dB (decibels). A higher number indicates more accurate and cleaner sound separation.
Prof. Jung-woo Choi remarked, "The research team has showcased world-leading acoustic separation AI models for the past three years, and I am delighted that these results have been officially recognized." He added, "I am proud of every member of the research team for winning first place through focused research, despite the significant increase in difficulty and having only a few weeks for development."
<Figure 2. Time-frequency patterns of sound sources separated from a mixed source>
The IEEE DCASE Challenge 2025 was held online, with submissions accepted from April 1 to June 15 and results announced on June 30. Since its launch in 2013, the DCASE Challenge has served as a premier global platform of IEEE Signal Processing Society for showcasing cutting-edge AI models in acoustic signal processing.
This research was supported by the Mid-Career Researcher Support Project and STEAM Research Project of the National Research Foundation of Korea, funded by the Ministry of Education, Science and Technology, as well as support from the Future Defense Research Center, funded by the Defense Acquisition Program Administration and the Agency for Defense Development.
2025.07.13 View 428 -
KAIST Presents a Breakthrough in Overcoming Drug Resistance in Cancer – Hope for Treating Intractable Diseases like Diabetes
<(From the left) Prof. Hyun Uk Kim, Ph.D candiate Hae Deok Jung, Ph.D candidate Jina Lim, Prof.Yoosik Kim from the Department of Chemical and Biomolecular Engineering>
One of the biggest obstacles in cancer treatment is drug resistance in cancer cells. Conventional efforts have focused on identifying new drug targets to eliminate these resistant cells, but such approaches can often lead to even stronger resistance. Now, researchers at KAIST have developed a computational framework to predict key metabolic genes that can re-sensitize resistant cancer cells to treatment. This technique holds promise not only for a variety of cancer therapies but also for treating metabolic diseases such as diabetes.
On the 7th of July, KAIST (President Kwang Hyung Lee) announced that a research team led by Professors Hyun Uk Kim and Yoosik Kim from the Department of Chemical and Biomolecular Engineering had developed a computational framework that predicts metabolic gene targets to re-sensitize drug-resistant breast cancer cells. This was achieved using a metabolic network model capable of simulating human metabolism.
Focusing on metabolic alterations—key characteristics in the formation of drug resistance—the researchers developed a metabolism-based approach to identify gene targets that could enhance drug responsiveness by regulating the metabolism of drug-resistant breast cancer cells.
< Computational framework that can identify metabolic gene targets to revert the metabolic state of the drug-resistant cells to that of the drug-sensitive parental cells>
The team first constructed cell-specific metabolic network models by integrating proteomic data obtained from two different types of drug-resistant MCF7 breast cancer cell lines: one resistant to doxorubicin and the other to paclitaxel. They then performed gene knockout simulations* on all of the metabolic genes and analyzed the results.
*Gene knockout simulation: A computational method to predict changes in a biological network by virtually removing specific genes.
As a result, they discovered that suppressing certain genes could make previously resistant cancer cells responsive to anticancer drugs again. Specifically, they identified GOT1 as a target in doxorubicin-resistant cells, GPI in paclitaxel-resistant cells, and SLC1A5 as a common target for both drugs.
The predictions were experimentally validated by suppressing proteins encoded by these genes, which led to the re-sensitization of the drug-resistant cancer cells.
Furthermore, consistent re-sensitization effects were also observed when the same proteins were inhibited in other types of breast cancer cells that had developed resistance to the same drugs.
Professor Yoosik Kim remarked, “Cellular metabolism plays a crucial role in various intractable diseases including infectious and degenerative conditions. This new technology, which predicts metabolic regulation switches, can serve as a foundational tool not only for treating drug-resistant breast cancer but also for a wide range of diseases that currently lack effective therapies.”
Professor Hyun Uk Kim, who led the study, emphasized, “The significance of this research lies in our ability to accurately predict key metabolic genes that can make resistant cancer cells responsive to treatment again—using only computer simulations and minimal experimental data. This framework can be widely applied to discover new therapeutic targets in various cancers and metabolic diseases.”
The study, in which Ph.D. candidates JinA Lim and Hae Deok Jung from KAIST participated as co-first authors, was published online on June 25 in Proceedings of the National Academy of Sciences (PNAS), a leading multidisciplinary journal that covers top-tier research in life sciences, physics, engineering, and social sciences.
※ Title: Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets ※ DOI: https://doi.org/10.1073/pnas.2425384122 ※ Authors: JinA Lim (KAIST, co-first author), Hae Deok Jung (KAIST, co-first author), Han Suk Ryu (Seoul National University Hospital, corresponding author), Yoosik Kim (KAIST, corresponding author), Hyun Uk Kim (KAIST, corresponding author), and five others.
This research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute (ETRI).
2025.07.08 View 1017 -
KAIST Presents Game-Changing Technology for Intractable Brain Disease Treatment Using Micro OLEDs
<(From left)Professor Kyung Cheol Choi, Professor Hyunjoo J. Lee, Dr. Somin Lee from the School of Electrical Engineering>
Optogenetics is a technique that controls neural activity by stimulating neurons expressing light-sensitive proteins with specific wavelengths of light. It has opened new possibilities for identifying causes of brain disorders and developing treatments for intractable neurological diseases. Because this technology requires precise stimulation inside the human brain with minimal damage to soft brain tissue, it must be integrated into a neural probe—a medical device implanted in the brain. KAIST researchers have now proposed a new paradigm for neural probes by integrating micro OLEDs into thin, flexible, implantable medical devices.
KAIST (President Kwang Hyung Lee) announced on the 6th of July that professor Kyung Cheol Choi and professor Hyunjoo J. Lee from the School of Electrical Engineering have jointly succeeded in developing an optogenetic neural probe integrated with flexible micro OLEDs.
Optical fibers have been used for decades in optogenetic research to deliver light to deep brain regions from external light sources. Recently, research has focused on flexible optical fibers and ultra-miniaturized neural probes that integrate light sources for single-neuron stimulation.
The research team focused on micro OLEDs due to their high spatial resolution and flexibility, which allow for precise light delivery to small areas of neurons. This enables detailed brain circuit analysis while minimizing side effects and avoiding restrictions on animal movement. Moreover, micro OLEDs offer precise control of light wavelengths and support multi-site stimulation, making them suitable for studying complex brain functions.
However, the device's electrical properties degrade easily in the presence of moisture or water, which limited their use as implantable bioelectronics. Furthermore, optimizing the high-resolution integration process on thin, flexible probes remained a challenge.
To address this, the team enhanced the operational reliability of OLEDs in moist, oxygen-rich environments and minimized tissue damage during implantation. They patterned an ultrathin, flexible encapsulation layer* composed of aluminum oxide and parylene-C (Al₂O₃/parylene-C) at widths of 260–600 micrometers (μm) to maintain biocompatibility.
*Encapsulation layer: A barrier that completely blocks oxygen and water molecules from the external environment, ensuring the longevity and reliability of the device.
When integrating the high-resolution micro OLEDs, the researchers also used parylene-C, the same biocompatible material as the encapsulation layer, to maintain flexibility and safety. To eliminate electrical interference between adjacent OLED pixels and spatially separate them, they introduced a pixel define layer (PDL), enabling the independent operation of eight micro OLEDs.
Furthermore, they precisely controlled the residual stress and thickness in the multilayer film structure of the device, ensuring its flexibility even in biological environments. This optimization allowed for probe insertion without bending or external shuttles or needles, minimizing mechanical stress during implantation.
2025.07.07 View 710