Researchers at KAIST discovered critical security vulnerabilities in smartphone communication modems, which can be exploited with a single manipulated wireless packet. The study highlights the need for standardized mobile communication modem security testing.
A KAIST research team has developed an optogenetic platform, RELISR, that enables precise spatiotemporal control over the storage and release of proteins and mRNAs. The system employs specific protein-protein and protein-RNA interactions to isolate target molecules within engineered membrane-less condensates.
KAIST researchers developed a next-generation wearable platform that utilizes ambient light as an energy source, reducing battery load by up to 86.22%. The platform integrates three complementary technologies to enable 24-hour continuous operation, including photometric, photovoltaic, and photoluminescent methods.
A Korean research team has developed a new single-atom catalyst that selectively performs only peroxidase-like reactions while maintaining high reaction efficiency. The platform enables test results to be read within minutes in near-physiological conditions, improving medical accessibility and ensuring timely treatment.
A research team at KAIST has developed a new catalyst for reducing air pollution by utilizing platinum diselenide, a two-dimensional material. The catalyst exhibits superior carbon monoxide oxidation performance across the entire temperature range compared to conventional platinum thin films.
A team of researchers at KAIST has identified a specialized gut-brain pathway for glucose sensing, which plays a central role in maintaining energy balance and metabolic homeostasis. This discovery opens up new therapeutic possibilities for metabolic diseases.
A research team elucidated the mechanism inducing plant autoimmune responses and proposed a novel strategy for cultivar improvement. The study identified defects in protein structure as the cause of hybrid necrosis, which occurs due to an abnormal reaction of immune receptors during cross-breeding between plant hybrids.
A new technology enables high-performance and efficient inference for planning based on diffusion models, improving inference-time scalability. The research achieved a 100% success rate on the giant maze-solving task, demonstrating its performance in real-time decision-making applications like intelligent robotics.
A KAIST research team has identified a new molecular mechanism in which alcohol-damaged liver cells increase reactive oxygen species, leading to cell death and inflammatory responses. They discovered that Kupffer cells act as a 'dual-function regulator' that can either promote or suppress inflammation through interactions with liver ce...
Researchers at KAIST developed a new artificial sensory nervous system that enables robots to efficiently respond to external stimuli like humans. The system mimics the functions of a living organism's sensory nervous system, allowing robots to selectively react to important or dangerous signals while ignoring safe or familiar ones.
A KAIST research team created an integrated platform that replicates brain-like layered neuronal structures using 3D printing and precisely measures neuronal activity. The platform, developed by Professors Je-Kyun Park and Yoonkey Nam, enables the simultaneous analysis of structure and function with high precision.
A KAIST team engineered a microbial strain capable of producing lutein at industrially relevant levels. They developed an electron channeling system to overcome previous limitations, achieving record-breaking 1.78 g/L of lutein production in fed-batch fermentation.
Researchers at KAIST developed a computational framework to predict key metabolic genes that can re-sensitize resistant cancer cells to treatment. This approach holds promise not only for various cancer therapies but also for treating metabolic diseases like diabetes.
Researchers from KAIST developed a high-performance, low-power NPU technology that can improve the inference performance of generative AI models like ChatGPT by over 60% while consuming approximately 44% less power compared to the latest GPUs.
Se Jin Park's SpeechSSM model generates long-duration speech without time constraints, maintaining semantic and speaker consistency. It effectively processes unbounded speech sequences by dividing data into short units and using a Non-Autoregressive audio synthesis model to generate high-quality speech.
A Korean research team used machine learning to identify a new material that can effectively remove iodate from contaminated water. The multi-metal LDH developed in this study showed exceptional adsorption performance, removing over 90% of iodate.
A KAIST research team discovered a method to enhance glioblastoma immunotherapy by restoring microbial diversity in the gut. Tryptophan supplementation activated beneficial strains of bacteria, increasing CD8 T cell infiltration into tumor tissues and improving survival rates.
A new e-textile platform developed by KAIST's research team combines 3D printing technology with advanced materials engineering to create customized training models for individual combatants. The platform uses flexible and highly durable sensors and electrodes printed directly onto textile substrates, enabling precise movement and huma...
A new recycling technology has been developed to turn used tires into raw materials for rubber and nylon, achieving high selectivity of up to 92% and a yield of 82%. The process uses dual catalysis to convert waste rubber into valuable chemicals.
Researchers at KAIST have created a new Li-Fi platform that offers speeds up to 224 Gbps and enhances security through on-device encryption. The device uses eco-friendly quantum dots and demonstrates improved brightness and efficiency, opening up new possibilities for ultra-high-speed data communication.
Researchers at KAIST have developed a technology to enhance creative generation of AI generative models like Stable Diffusion, generating novel and useful images. The algorithm amplifies internal feature maps to boost creativity without new training, outperforming existing methods in novelty and utility.
Researchers at KAIST develop a 'pedestrian-friendly smart window' technology that reduces heating and cooling energy consumption in urban buildings while resolving light pollution issues. The RECM system operates in three modes, allowing for real-time adjustment of light and heat transmission.
Researchers developed a technology that precisely analyzes 21 types of reactants simultaneously using high-resolution fluorine nuclear magnetic resonance spectroscopy. This breakthrough contributes to new drug development and catalyst optimization in AI-driven autonomous synthesis.
Researchers at KAIST have developed a groundbreaking technology capable of selectively acetylating specific RNA molecules within the human body using the CRISPR-Cas13 system. This breakthrough enables precise, programmable control of RNA function and is expected to open new avenues in RNA-based therapeutic development.
The virtual teaching assistant (VTA) provides personalized feedback to individual students even in large-scale classes. The system, which automatically vectorizes a large volume of course materials and uses them as the basis for answering students' questions, has been shown to significantly reduce the burden on TAs.
Researchers at KAIST developed a quadrupedal navigation system that enables the robot to reach its target destination quickly and safely in complex terrain. Inspired by cat's paw placement, they significantly reduced computational complexity.
Researchers developed a novel liquid metal electronic ink that allows for micro-scale circuit printing at room temperature, with reversible stiffness switching depending on temperature. This advancement marks a significant leap toward next-generation wearable, implantable, and robotic devices with tunable mechanical properties.
A collaborative research team led by KAIST has developed a groundbreaking technology that uses advanced optical techniques combined with an AI-based deep learning algorithm to create realistic 3D images of cancer tissue. This breakthrough paves the way for next-generation non-invasive pathological diagnosis.
KAIST and Mainz researchers have predicted a 3D magnon Hall effect, demonstrating the ability of magnons to move freely and complexly in 3D space. This breakthrough could lead to novel functionalities in next-generation computing structures.
Researchers at KAIST have developed an AI-powered app called AACessTalk that enables meaningful communication between children with autism and their parents. The app uses personalized vocabulary cards and customized prompts to foster mutual engagement in dialogue.
A KAIST research team has identified a key neural pathway involved in forming fear memories triggered by psychological threats. The pIC-PBN circuit processes psychological distress and drives fear memory formation, offering a significant advance in understanding how psychological distress is processed.
Researchers at KAIST have identified a critical protein called SLIRP that acts as an 'immune switch' regulating immune responses to viruses and autoimmune diseases. Suppressing SLIRP can alleviate abnormal immune responses in patients with Sjögren's syndrome, coronavirus, and encephalomyocarditis virus infection.
Researchers developed T2IRay, a VR input method with precise object pointing, and ChoreoCraft, a virtual reality tool supporting choreographers' creativity. These technologies received Honorable Mention awards at CHI 2025 for their contributions to human-computer interaction.
KAIST researchers developed a highly sensitive mid-infrared photodetector that operates at room temperature, enabling low-cost mass production and real-time sensing of various molecular species. The technology has potential applications in environmental monitoring, medical diagnostics, and industrial process management.
Researchers developed an AI-based music creation support system called Amuse, which converts user inputs into harmonic structures to support composition. The system has high potential as a creative companion for musicians and is centered on the creator's initiative.
Researchers at KAIST have identified a key mechanism behind neuroinflammatory responses in Parkinson's disease, which is regulated by an RNA editing enzyme called ADAR1. This discovery suggests that targeting this enzyme could serve as a novel therapeutic strategy for treating the disease.
Researchers at KAIST discovered that DDX54 is the master regulator hindering immunotherapy's effectiveness in lung cancer. Supressing DDX54 enhances immune cell infiltration into tumors and improves immunotherapy efficacy.
Researchers at KAIST have successfully developed a treatment method that restores vision by inducing retinal regeneration and vision recovery in disease-model mice. The approach involves blocking the PROX1 protein, which suppresses retinal regeneration, using an antibody developed by Celliaz Inc.
Researchers at KAIST evaluated industrial microbial cell factories to identify suitable strains and optimal metabolic engineering strategies. Using genome-scale metabolic models, they calculated maximum theoretical yields and achievable yields under industrial conditions for 235 bio-based chemicals.
Researchers at KAIST have successfully developed an eco-friendly, bio-based plastic that combines the advantages of PET and nylon. The new material was produced through microbial fermentation and exhibited characteristics similar to high-density polyethylene, making it strong and durable enough for industrial use.
A new study proposes a theoretical framework for AI-based wearable blood pressure sensors, paving the way for non-invasive and continuous cardiovascular monitoring. The review highlights clinical aspects of implementation, real-time data transmission, and signal quality degradation, and presents strategies to address technical barriers.
Researchers at KAIST have discovered a molecular switch that can induce cancer reversal by capturing the moment of critical transition before normal cells become irreversibly cancerous. The technology uses single-cell RNA sequencing data and computer simulation analysis to identify the molecular switch.
The KAIST research team developed an AI-based technique to accurately predict Hall thruster performance, significantly reducing the time and cost associated with iterative design, fabrication, and testing. The trained neural network ensemble model offers detailed analyses of performance parameters, accounting for key design variables.
A KAIST research team identified core gene expression networks regulated by proteins that drive phenomena such as cancer development and tissue differentiation. The study revealed that IPMK acts as a critical transcriptional activator in these networks, enhancing SRF's protein activity.
Researchers at KAIST and U of Michigan developed a digital biomarker to predict symptoms of depression using smartwatch data. The technology estimates circadian rhythm disruption, which can lead to depression, anxiety, and other mental health issues.
The KAIST team developed a next-generation neuromorphic semiconductor-based integrated system that can learn and correct errors on its own. This technology will revolutionize AI use in everyday devices, making them faster, more private and energy-efficient.
Researchers at KAIST developed CamBio, a biotemplating method utilizing specific intracellular proteins to create functional nanostructures with high tunability. The method enables the selective synthesis of nanostructures from biological samples, showing improved performance in surface-enhanced Raman spectroscopy substrate detection.
A novel bio-inspired camera capable of ultra-high-speed imaging and high sensitivity has been developed by KAIST researchers. The camera mimics the visual structure of insect eyes and achieves frame rates thousands of times faster than conventional cameras, while providing clear images in low-light conditions.
The KAIST research team developed a highly stretchable microelectrode array to monitor organoids' functions, enabling real-time analysis of their states. The technology showed promise in high-throughput drug screening applications, revealing changes in signal characteristics according to size and identifying potential drug interactions.
Researchers at KAIST have developed a technology that can treat colon cancer by converting cancer cells into normal-like cells. The breakthrough involves creating a digital twin of the gene network associated with normal cell differentiation, leading to significant promise for reversible cancer therapies.