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Professor Insik Shin Becomes First Korean to Win the RTSS Most Influential Paper Award
< KAIST Professor Insik Shin >
KAIST announced on December 21st that Professor Insik Shin from the School of Computing has received the Influential Paper Award 2025 at the IEEE Real-Time Systems Symposium (RTSS), the world's most prestigious international conference in the field of real-time systems.
This honor is a "Test of Time Award," presented to papers that have exerted a sustained and significant influence on both academia and industry for more than 10 years after publication. This marks the first time a Korean researcher has received this prestigious award. The ceremony took place at IEEE RTSS 2025 in Boston, USA, on December 4th (local time).
Professor Shin’s award-winning research is the "Periodic Resource Model," co published in 2003 with Professor Insup Lee of the University of Pennsylvania. Rather than trying to verify a complex machine or system all at once, this study developed a method to verify individual components—much like LEGO blocks—to ensure each meets its designated timing requirements. It mathematically guarantees that when these components are assembled, the entire system will operate safely.
Paper Title: Periodic Resource Model for Compositional Real-Time Guarantees
DOI: 10.1109/REAL.2003.1253249
Thanks to this research, it has become possible to design real-time systems that cannot tolerate even a moment of delay—such as autonomous vehicles, aircraft, and industrial robots—with greater precision and safety. This breakthrough overcame the limitations of existing methods that required analyzing an entire system at once, which had become nearly impossible as the complexity of modern real-time systems increased rapidly.
Professor Shin presented a method to divide a system into small modules, verify that each module satisfies its time constraints, and mathematically prove that the safety of the entire system is guaranteed upon integration. This work is credited with establishing the foundation for modern compositional real-time scheduling theory.
At the time of its initial publication in 2003, this paper won the 'Best Paper Award' at RTSS—another first for a Korean researcher. Now, 20 years later, its academic and industrial value has been officially recognized once again. This is because the theory has transcended academic boundaries to become a core analytical tool in various safety-critical industries, including autonomous driving, aerospace control, and industrial automation.
The IEEE Technical Committee stated, "This model has established itself as a core language for modern real-time system design and has guided the direction of research and industry for the past 20 years." The paper is currently featured in textbooks at major universities in the United States and Europe, serving as a standard theory in the field.
"As a scholar, this is the award I have wanted most in my life," said Professor Shin. "I am honored to have it recognized that research from 20 years ago has truly had a major impact on the world. This was made possible by the many researchers and companies who applied this theory to actual systems."
Meanwhile, Professor Shin is expanding his research beyond real-time systems into the field of Artificial Intelligence (AI). He founded the faculty-led startup Fluiz and developed FluidGPT, a mobile AI agent technology that allows users to execute smartphone apps via voice commands. This technology recently won the AI Champion Competition hosted by the Ministry of Science and ICT. Experts evaluate Professor Shin as achieving rare success by bridging basic theory and applied technology, effectively linking research to industry.
2025.12.22 View 516 -
Where did this fish come from? Securing World-Class Seafood Traceability Technology
< (From left) KAIST Ph.D. candidate Hyeontaek Hwang, Research Professor Yalew Kidane, Senior Researcher Young-jong Lee, Researcher Geon-woo Park, and (Top) Professor Daeyoung Kim >
When buying seafood at a supermarket, you may have wondered where the fish was caught and what process it went through to reach your dinner table. However, due to complex distribution processes, it has been difficult to transparently track that path. KAIST’s research team has developed a digital technology that solves this problem, allowing the movement path of seafood to be checked at a glance based on international standards recognized worldwide.
KAIST announced on December 19th that "OLIOPASS," a GS1 international standard-based digital transformation solution developed by Director Daeyoung Kim (Professor, School of Computing) of the KAIST Auto-ID Labs Busan Innovation Center, has passed the rigorous performance verification of the GDST (Global Dialogue on Seafood Traceability). It is the first in Korea to obtain the "GDST Capable Solution" certification.
< (Left) GDST Global Certification Logo, (Right) KAIST OLIOPASS Platform Logo >
Only 13 technologies worldwide have received this GDST certification. Among them, only 7 entities, including KAIST, support "Full Chain" traceability technology, which manages the entire process from production and processing to distribution and sales.
The GDST is an international organization established in 2015 at the suggestion of the World Economic Forum (WEF). It helps record and share information on all seafood movement processes digitally, according to the GS1 international standard agreed upon by the global community. This can be compared to creating a "common language for the supply chain" used worldwide.
The GDST is a global standard system that increases the reliability of seafood history information by defining Key Data Elements (KDEs) that must be recorded during the movement of seafood and Critical Tracking Events (CTEs) that define when, where, and what moved, based on international standards.
As major food distribution companies in the United States and Europe have recently begun requiring GDST compliance, this standard is becoming a de facto essential requirement for entering the global market. Since 2019, KAIST has participated as a founding member of GDST and has played a key role in designing seafood traceability models and system-to-system information interoperability.
In particular, with the U.S. Food and Drug Administration (FDA) announcing the mandatory enforcement of food traceability (FSMA 204) starting in July 2028, this certification is significant as it secures a technical solution for domestic companies to meet global market regulations.
OLIOPASS, which received certification on November 5th, is a digital traceability platform that combines KAIST's IoT technology with international standards (GS1 EPCIS 2.0, GS1 Digital Link). It records and shares movement information of various products and assets in a standardized language and utilizes blockchain technology to fundamentally prevent forgery or alteration. Even if systems differ between companies, history data is seamlessly linked.
Furthermore, OLIOPASS is designed as an "AI-ready data" infrastructure, allowing for the easy application of next-generation AI technologies such as Large Multimodal Models (LMM), AI agents, knowledge graphs, and ontologies. This allows it to serve as a platform that supports both digital and AI transformation beyond simple history management.
Daeyoung Kim, Director of the KAIST Auto-ID Labs Busan Innovation Center, stated, "This certification is an international recognition of our capability in reliable data technology across the global supply chain. We will expand OLIOPASS beyond seafood and food into various fields such as pharmaceuticals, logistics, defense, and smart cities, ensuring KAIST’s technology grows into a platform used by the world."
※ Related Link: https://thegdst.org/verified-gdst-capable-solutions/
< List of Certified Organizations >
2025.12.22 View 497 -
AI Technology World No. 1 in Finding the Exact Moment in a Video: Where is the First Place?
< (From left) Professor Joon Hyuk Noh (Assistant Professor, Department of Artificial Intelligence, Ewha Womans University), Seojin Hwan, Yoonki Cho (Ph.D. Candidate), Professor Sung-Eui Yoon (School of Computing, KAIST) >
When faced with a complex question like 'What object disappeared while the camera was pointing elsewhere?', a common problem is that AI often relies on language patterns to guess a 'plausible answer,' instead of actually observing the real situation in the video. To overcome this limitation, our university's research team developed a technology that enables the AI to autonomously identify the 'exact critical moment (Trigger moment)' within the video, and the team’s excellence was proven by winning an international AI competition with this technology. The university announced on the 28th that the research team led by Professor Sung-Eui Yoon from the School of Computing, in collaboration with Professor Joon Hyuk Noh's team from Ewha Womans University, took 1st place in the Grounded Video Question Answering track of the Perception Test Challenge held at ICCV 2025, a world-renowned computer vision conference. The Perception Test Challenge held at ICCV 2025 was organized by Google DeepMind with a total prize pool of 50,000 Euros (approximately 83 million KRW). It assesses the cognitive and reasoning abilities of multimodal AI, which must comprehensively understand various data, including video, audio, and text. Crucially, the core evaluation factor is the ability to make judgments based on actual video evidence, moving beyond language-centric bias. Unlike conventional methods that analyze the entire video indiscriminately, our university's research team developed a new technology that instructs the AI to first locate the core scene (Trigger moment) essential for finding the correct answer. Simply put, this technology is designed to make the AI autonomously determine: “This scene is decisive for answering this question!” The research team calls this framework CORTEX (Chain-of-Reasoning for Trigger Moment Extraction). The research team's system consists of a three-stage structure where three models performing different functions operate sequentially. First, the Reasoning AI (Gemini 2.5 Pro) reasons about which moment is required to answer the question and finds candidate Trigger moments. Next, the Object Location Finding Model (Grounding Model, Molmo-7B) accurately identifies the exact location (coordinates) of people, cars, and objects on the screen during the selected moment. Finally, the Tracking Model (SAM2) precisely tracks the movement of objects in the time frame before and after the selected scene, using that scene as a reference, thereby reducing errors. In short, the 'method of accurately pinpointing a key scene and tracking the evidence for the answer centered on that scene' significantly reduced problems like initial misjudgment or occlusion in the video. In the Grounded Video Question Answering (Grounded VideoQA) track, which saw 23 participating teams, the KAIST team SGVR Lab (Scalable Graphics, Vision & Robotics Lab) recorded 0.4968 points in the HOTA (Higher Order Tracking Accuracy) metric, overwhelmingly surpassing the 2nd place score of 0.4304 from Columbia University, USA, to secure 1st place. This achievement is nearly double the previous year's winning score of 0.2704 points. This technology has wide-ranging applications in real-life settings. Autonomous driving vehicles can accurately identify moments of potential accident risk, robots can understand the surrounding environment smarter, security and surveillance systems can rapidly locate critical scenes, and media analysis can precisely track the actions of people or objects in chronological order. This is a core technology that enables AI to judge based on "actual evidence in the video." The ability to accurately pinpoint how objects behave over time in a video is expected to greatly expand the application of AI in real-world scenarios in the future.
< Pipeline image of the grounding framework for video question answering proposed by the research team >
This research was presented on October 19th at ICCV 2025, the 3rd Perception Test Challenge conference. The achievement was supported by the Ministry of Science and ICT's Basic Research Program (Mid-Career Researcher), the SW Star Lab Project's 'Development of Perception, Action, and Interaction Algorithms for Open-World Robot Services,' and the AGI Project's 'Reality Construction and Bi-directional Capability Approach based on Cognitive Agents for Embodied AGI' tasks."
2025.12.01 View 887 -
Professor Youngjin Kwon's Team Wins Google Award 'Catches Bugs Without a Real CPU
< Professor Youngjin Kwon >
Modern CPUs have complex structures, and in the process of handling multiple tasks simultaneously, an order-scrambling error known as a 'concurrency bug' can occur. Although this can lead to security issues, these bugs were extremely difficult to detect using conventional methods. Our university's research team has developed a world-first-level technology to automatically detect these bugs by precisely reproducing the internal operation of the CPU in a virtual environment without needing a physical chip. Through this, they successfully found and fixed 11 new bugs in the latest Linux kernel.
Our university announced on the 21st that the research team led by Professor Youngjin Kwon of the School of Computing has won the 'Research Scholar Award' (Systems category) presented by Google.
The Google Research Scholar Award is a global research support program, implemented since 2020, to support Early-Career Professors conducting innovative research in various fields such as AI, Systems, Security, and Data Management.
It is known as a highly competitive program, with the selection process conducted directly by Google Research scientists, and only a tiny fraction of the hundreds of applicants worldwide are chosen. In particular, this award is recognized as one of the most prestigious industry research support programs globally in the field of AI and Computer Systems, and domestic recipients are rare.
■ Technology Developed to Detect Concurrency Bugs in the Latest Apple M3 and ARM Servers
Professor Kwon's team developed a technology that automatically detects concurrency bugs in the latest ARM (a CPU design method that uses less power and is highly efficient) based servers, such as the Apple M3 (Apple's latest-generation computer processor chip).
A concurrency bug is an error that occurs when the order of operations gets mixed up while the CPU handles multiple tasks simultaneously. This is a severe security vulnerability that can cause the computer to suddenly freeze or become a pathway for hackers to attack the system. However, these errors were extremely difficult to find with existing testing methods alone.
■ Automatically Detects Bugs by Reproducing CPU Internal Operations Without a Real CPU
The core achievement of Professor Kwon's team is the 'technology to reproduce the internal operation of the CPU exactly in a virtual environment without a physical chip.' Using this technology, it is possible to precisely analyze the order in which instructions are executed and where problems occur using only software, without having to disassemble the CPU or use the actual chip.
By running the Linux operating system based on this system to automatically detect bugs, the research team discovered 11 new bugs in the latest Linux kernel* and reported them to the developer community, where they were all fixed.
*Linux kernel: The core operating system engine that forms the basis of servers, supercomputers, and smartphones (Android) worldwide. It acts as the 'heart' of the system, managing the CPU, memory, and storage devices.
Google recognized this technology as 'very important for its own infrastructure' and conferred the Award.
< Google Scholar Award Recipient Page >
This technology is evaluated to have general applicability, not only to Linux but also to various operating systems such as Android and Windows. The research team has released the software as open-source (GitHub) so that anyone in academia or industry can utilize it.
Professor Youngjin Kwon stated, "This award validates the international competitiveness of KAIST's systems research," and "We will continue our research to establish a safe and highly reliable computing environment."
※ Google Scholar Award Recipient Page: https://research.google/programs-and-events/research-scholar-program/recipients/ GitHub (Technology Open-Source): https://github.com/casys-kaist/ozz
2025.11.21 View 1060 -
Automatic C to Rust Translation Technology Gains Global Attention for Accuracy Beyond AI
<(From Left) Professor Sukyoung Ryu, Researcher Jaemin Hong>
As the C language, which forms the basis of critical global software like operating systems, faces security limitations, KAIST's research team is pioneering core original technology research for the accurate automatic conversion to Rust to replace it. By proving the mathematical correctness of the conversion, a limitation of existing Artificial Intelligence (LLM) methods, and solving C language security issues through automatic conversion to Rust, they presented a new direction and vision for future software security research. This work has been selected as the cover story for CACM, the world's highest-authority academic journal, thereby demonstrating KAIST's global research leadership in the field of computer science.
KAIST announced on the 9th of November that the paper by Professor Sukyoung Ryu's research team (Programming Language Research Group) from the School of Computing was selected as the cover story for the November issue of CACM (Communications of the ACM), the highest authority academic journal published by ACM (Association for Computing Machinery), the world's largest computer society.
<Photo of the Paper Selected for the Cover of Communications of the ACM>
This paper comprehensively addresses the technology developed by Professor Sukyoung Ryu's research team for the automatic conversion of C language to Rust, and it received high acclaim from the international research community for presenting the technical vision and academic direction this research should pursue in the future.
The C language has been widely used in the industry since the 1970s, but its structural limitations have continuously caused severe bugs and security vulnerabilities. Rust, on the other hand, is a secure programming language developed since 2015, used in the development of operating systems and web browsers, and has the characteristic of being able to detect and prevent bugs before program execution.
The US White House recommended discontinuing the use of C language in a technology report released in February 2024, and the Defense Advanced Research Projects Agency (DARPA) also explicitly stated that Rust is the core alternative for resolving C language security issues by promoting a project to develop technology for the automatic conversion of C code to Rust.
Professor Sukyoung Ryu's research team proactively raised the issues of C language safety and the importance of automatic conversion even before these movements began in earnest, and they have continuously developed core related technologies.
In May 2023, the research team presented the Mutex conversion technology (necessary for program synchronization) at ICSE (International Conference on Software Eng), the top authority conference in software engineering. In June 2024, they presented the Output Parameter conversion technology (used for result delivery) at PLDI (Programming Language Design and Implementation), the top conference in programming languages, and in October of the same year, they presented the Union conversion technology (for storing diverse data together) at ASE (Automated Software Eng), the representative conference in software automation.
These three studies are all "world-first" achievements presented at top-tier international academic conferences, successfully implementing automatic conversion technology for each feature with high completeness.
Since 2023, the research team has consistently published papers in CACM every year, establishing themselves as global leading researchers who consistently solve important and challenging problems worldwide.
This paper was published in CACM (Communications of the ACM) on October 24, with Dr. Jaemin Hong (Postdoctoral Research Fellow at KAIST Information and Electronics Research Institute) as the first author. ※Paper Title: Automatically Translating C to Rust, DOI: https://doi.org/10.1145/3737696
Dr. Jaemin Hong stated, "The conversion technology we developed is an original technology based on programming language theory, and its biggest strength is that we can logically prove the 'correctness' of the conversion." He added, "While most research relies on Large Language Models (LLMs), our technology can mathematically guarantee the correctness of the conversion."
Dr. Hong is scheduled to be appointed as an Assistant Professor in the Computer Science Department at UNIST starting in March 2025.
Furthermore, Professor Ryu's research team has four papers accepted for presentation at ASE 2025, the highest-authority conference in software engineering, including C→Rust conversion technology.
These papers, in addition to automatic conversion technology, cover various cutting-edge software engineering fields and are receiving high international acclaim. They include: technology to verify whether quantum computer programs operate correctly, 'WEST' technology that automatically checks the correctness of WebAssembly programs (technology for fast and efficient program execution on the web) and creates tests for them, and technology that automatically simplifies complex WebAssembly code to quickly find errors. Among these, the WEST paper received the Distinguished Paper Award.
This research was supported by the Leading Research Center/Mid-career Researcher Support Program of the National Research Foundation of Korea, the Institute of Information & Communications Technology Planning & Evaluation (IITP), and Samsung Electronics.
2025.11.10 View 5464 -
KAIST's 'FluidGPT' Wins Grand Prize at the 2025 AI Champion Competition
<Commemorative Photo After Winning at the 2025 AI Champions Award Ceremony>
The era has begun where an AI assistant goes beyond simple conversation to directly view the screen, make decisions, and complete tasks such as hailing a taxi or booking an SRT ticket.
KAIST (President Kwang Hyung Lee) announced on the 6th that the AutoPhone Team (Fluidez, KAIST, Korea University, Sungkyunkwan University), led by Professor Insik Shin (CEO of Fluidez Co., Ltd.) of the School of Computing, was selected as the inaugural AI Champion (1st place) in the '2025 Artificial Intelligence Champion (AI Champion) Competition,' hosted by the Ministry of Science and ICT.
This competition is the nation's largest AI technology contest, comprehensively evaluating the innovativeness, social impact, and commercial potential of AI technology. With 630 teams participating nationwide, the AutoPhone Team claimed the top honor and will receive 3 billion Korean won in research and development funding.
The technology developed by the AutoPhone Team, 'FluidGPT,' is a fully autonomous AI agent that understands a user's voice command and enables the smartphone to independently run apps, click, input, and even complete payments.
For example, when a user says, "Book an SRT ticket from Seoul Station to Busan," or "Call a taxi," FluidGPT opens the actual app and sequentially performs the necessary steps to complete the request.
The core of this technology is its 'Non-Invasive (API-Free)' structure. Previously, calling a taxi using an app required directly connecting to the app's internal system (API communication) through the taxi app's API. In contrast, this technology does not modify the existing app's code or link an API. Instead, the AI directly recognizes and operates the screen (UI), acquiring the ability to use the smartphone just like a human.
As a result, FluidGPT presents a new paradigm—"AI that sees, judges, and moves a hand on behalf of a person"—and is evaluated as a core technology that will usher in the 'AI Phone Era.'
FluidGPT moves beyond simple voice assistance to implement the concept of 'Agentic AI' (Action-Oriented Artificial Intelligence), where the AI directly views the screen, makes decisions, and takes action. As a fully action-oriented system, the AI clicks app buttons, fills in input fields, and references data to autonomously achieve the user's objective, foreshadowing an innovation in how smartphones are used.
Professor In-sik Shin of the School of Computing shared his thoughts, stating, "AI is now evolving from conversation to action. FluidGPT is a technology that understands the user's words and autonomously executes actual apps, and it will be the starting point of the 'AI Phone Era.' The AutoPhone Team possesses world-class research capabilities, and we will contribute to the widespread adoption of AI services that everyone can easily use."
KAIST President Kwang Hyung Lee remarked, "This achievement is a representative example that demonstrates KAIST's vision for AI convergence," adding, "AI technology is entering the daily lives of citizens and leading a new wave of innovation." He further added, "KAIST will continue to lead research in future core technologies such as AI and semiconductors to bolster national competitiveness."
2025.11.06 View 1662 -
KAIST, Dancing Like 'Navillera'... AI Understands and Renders Garment Motions of Avatars
<(From Left)Ph.D candidate Jihyun Lee, Professor Tae-Kyun Kim, M.S candidate Changmin Lee>
The era has begun where AI moves beyond merely 'plausibly drawing' to understanding even why clothes flutter and wrinkles form. A KAIST research team has developed a new generative AI that learns movement and interaction in 3D space following physical laws. This technology, which overcomes the limitations of existing 2D-based video AI, is expected to enhance the realism of avatars in films, the metaverse, and games, and significantly reduce the need for motion capture or manual 3D graphics work.
KAIST (President Kwang Hyung Lee) announced on the 22nd that the research team of Professor Tae-Kyun (T-K) Kim from the School of Computing has developed 'MPMAvatar,' a spatial and physics-based generative AI model that overcomes the limitations of existing 2D pixel-based video generation technology.
To solve the problems of conventional 2D technology, the research team proposed a new method that reconstructs multi-view images into 3D space using Gaussian Splatting and combines it with the Material Point Method (MPM), a physics simulation technique.
In other words, the AI was trained to learn physical laws on its own by stereoscopically reconstructing videos taken from multiple viewpoints and allowing objects within that space to move and interact as if they were in real physical world.
This enables the AI to compute the movement based on objects' material, shape, and external forces, and then learn the physical laws by comparing the results with actual videos.
The research team represented the 3D space using point-units, and by applying both Gaussian and MPM to each point, they simultaneously achieved physically natural movement and realistic video rendering.
That is, they divided the 3D space into numerous small points, making each point move and deform like a real object, thereby realizing natural video that is nearly indistinguishable from reality.
In particular, to precisely express the interaction of thin and complex objects like clothing, they calculated both the object's surface (mesh) and its particle-unit structure (point), and utilized the Material Point Method (MPM), which calculates the object's movement and deformation in 3D space according to physical laws.
Furthermore, they developed a new collision handling technology to realistically reproduce scenes where clothes or objects move and collide with each other in multiple spots and complex manner.
The generative AI model MPMAvatar, to which this technology is applied, successfully reproduced the realistic movement and interaction of a person wearing loose clothing, and also succeeded in 'Zero-shot' generation, where the AI processes data it has never seen during the learning process by inferring on its own.
<Figure 1. Modeling new human poses and clothing dynamics from multi-view video input, and zero-shot generation of novel physical interactions.>
The proposed method is applicable to various physical properties, such as rigid bodies, deformable objects, and fluids, allowing it to be used not only for avatars but also for the generation of general complex scenes.
<“Figure 2. Depiction of graceful dance movements and soft clothing folds, like Navillera.>
Professor Tae-Kyun (T-K) Kim explained, "This technology goes beyond AI simply drawing a picture; it makes the AI understand 'why' the world in front of it looks the way it does. This research demonstrates the potential of 'Physical AI' that understands and predicts physical laws, marking an important turning point toward AGI (Artificial General Intelligence)." He added, "It is expected to be practically applied across the broaden immersive content industry, including virtual production, films, short-form contents, and adverts, creating significant change."
The research team is currently expanding this technology to develop a model that can generate physically consistent 3D videos simply from a user's text input.
This research involved Changmin Lee, a Master's student at the KAIST Graduate School of AI, as the first author, and Jihyun Lee, a Ph.D. student at the KAIST School of Computing, as a co-author. The research results will be presented at NeurIPS, the most prestigious international academic conference in the field of AI, on December 2nd, and the program code is to be fully released.
· Paper: C. Lee, J. Lee, T-K. Kim, MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics, Proc. of Thirty-Ninth Annual Conf. on Neural Information Processing Systems (NeurIPS), San Diego, US, 2025
· arXiv version: https://arxiv.org/abs/2510.01619
· Related Project Site: https://kaistchangmin.github.io/MPMAvatar/
· Related video links showing the 'Navillera'-like dancing drawn by AI:
o https://www.youtube.com/shorts/ZE2KoRvUF5c
o https://youtu.be/ytrKDNqACqM
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) through the Human-Oriented Next-Generation Challenging AGI Technology Project (RS-2025-25443318) and the Professional AI Talent Development Program for Multimodal AI Agents (RS-2025-25441313).
2025.10.22 View 1862 -
Refrigerator Use Increases with Stress, IoT Sensors Read Mental Health
<(From Left) Ph.D candidate Chanhee Lee, Professor Uichin Lee, Professor Hyunsoo Lee, Ph.D candidate Youngji Koh from School of Computing>
The number of single-person households in South Korea has exceeded 8 million, accounting for 36% of the total, marking an all-time high. A Seoul Metropolitan Government survey found that 62% of single-person households experience 'loneliness', deepening feelings of isolation and mental health issues. KAIST researchers have gone beyond the limitations of smartphones and wearables, utilizing in-home IoT data to reveal that a disruption in daily rhythm is a key indicator of worsening mental health. This research is expected to lay the foundation for developing personalized mental healthcare management systems.
KAIST (President Kwang Hyung Lee) announced on the 21st of October that a research team led by Professor Uichin Lee from the School of Computing has demonstrated the possibility of accurately tracking an individual's mental health status using in-home Internet of Things (IoT) sensor data.
Consistent self-monitoring is important for mental health management, but existing smartphone- or wearable-based tracking methods have the limitation of data loss when the user is not wearing or carrying the device inside the home.
The research team therefore focused on in-home environmental data. A 4-week pilot study was conducted on 20 young single-person households, installing appliances, sleep mats, motion sensors, and other devices to collect IoT data, which was then analyzed along with smartphone and wearable data.
The results confirmed that utilizing IoT data alongside existing methods allows for a significantly more accurate capture of changes in mental health. For instance, reduced sleep time was closely linked to increased levels of depression, anxiety, and stress, and increased indoor temperature also showed a correlation with anxiety and depression.
<Picture1. Heatmap of the Correlation Between Each User’s Mental Health Status and Sensor Data>
Participants' behavioral patterns varied, including a 'binge-eating type' with increased refrigerator use during stress and a 'lethargic type' with a sharp decrease in activity. However, a common trend clearly emerged: mental health deteriorated as daily routines became more irregular.
Variability in daily patterns was confirmed to be a more important factor than the frequency of specific behaviors, suggesting that a regular routine is essential for maintaining mental health.
When research participants viewed their life data through visualization software, they generally perceived the data as being genuinely helpful in understanding their mental health, rather than expressing concern about privacy invasion. This significantly enhanced the research acceptance and satisfaction with participation.
<Figure 2. Comparison of Average Mental Health Status Between the High Irregularity Group (Red) and the Low Irregularity Group (Blue)>
Professor Uichin Lee stated, "This research demonstrates that in-home IoT data can serve as an important clue for understanding mental health within the context of an individual's daily life," and added, "We plan to further develop this into a remote healthcare system that can predict individual lifestyle patterns and provide personalized coaching using AI."
Youngji Koh, a Ph.D candidate, participated as the first author in this research. The findings were published in the September issue of the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, a prominent international journal in the field of human-computer interaction (HCI). ※ Harnessing Home IoT for Self-tracking Emotional Wellbeing: Behavioral Patterns, Self-Reflection, and Privacy Concerns DOI: https://dl.acm.org/doi/10.1145/3749485 ※ Youngji Koh (KAIST, 1st author), Chanhee Lee (KAIST, 2nd author), Eunki Joung (KAIST, 3rd author), Hyunsoo Lee (KAIST, corresponding author), Uichin Lee (KAIST, corresponding author)
This research was conducted with support from the LG Electronics-KAIST Digital Healthcare Research Center and the National Research Foundation of Korea, funded by the government (Ministry of Science and ICT).
2025.10.21 View 1707 -
KAIST Develops an AI Semiconductor Brain Combining Transformer's Intelligence and Mamba's Efficiency
<(From Left) Ph.D candidate Seongryong Oh, Ph.D candidate Yoonsung Kim, Ph.D candidate Wonung Kim, Ph.D candidate Yubin Lee, M.S candidate Jiyong Jung, Professor Jongse Park, Professor Divya Mahajan, Professor Chang Hyun Park>
As recent Artificial Intelligence (AI) models’ capacity to understand and process long, complex sentences grows, the necessity for new semiconductor technologies that can simultaneously boost computation speed and memory efficiency is increasing. Amidst this, a joint research team featuring KAIST researchers and international collaborators has successfully developed a core AI semiconductor 'brain' technology based on a hybrid Transformer and Mamba structure, which was implemented for the first time in the world in a form capable of direct computation inside the memory, resulting in a four-fold increase in the inference speed of Large Language Models (LLMs) and a 2.2-fold reduction in power consumption.
KAIST (President Kwang Hyung Lee) announced on the 17th of October that the research team led by Professor Jongse Park from KAIST School of Computing, in collaboration with Georgia Institute of Technology in the United States and Uppsala University in Sweden, developed 'PIMBA,' a core technology based on 'AI Memory Semiconductor (PIM, Processing-in-Memory),' which acts as the brain for next-generation AI models.
Currently, LLMs such as ChatGPT, GPT-4, Claude, Gemini, and Llama operate based on the 'Transformer' brain structure, which sees all of the words simultaneously. Consequently, as the AI model grows and the processed sentences become longer, the computational load and memory requirements surge, leading to speed reductions and high energy consumption as major issues.
To overcome these problems with Transformer, the recently proposed sequential memory-based 'Mamba' structure introduced a method for processing information over time, increasing efficiency. However, memory bottlenecks and power consumption limits still remained.
Professor Park Jongse's research team designed 'PIMBA,' a new semiconductor structure that directly performs computations inside the memory in order to maximize the performance of the 'Transformer–Mamba Hybrid Model,' which combines the advantages of both Transformer and Mamba.
While existing GPU-based systems move data out of the memory to perform computations, PIMBA performs calculations directly within the storage device without moving the data. This minimizes data movement time and significantly reduces power consumption.
<Analysis of Post-Transformer Models and Proposal of a Problem-Solving Acceleration System>
As a result, PIMBA showed up to a 4.1-fold improvement in processing performance and an average 2.2-fold decrease in energy consumption compared to existing GPU systems.
The research outcome is scheduled to be presented on October 20th at the '58th International Symposium on Microarchitecture (MICRO 2025),' a globally renowned computer architecture conference that will be held in Seoul. It was previously recognized for its excellence by winning the Gold Prize at the '31st Samsung Humantech Paper Award.' ※Paper Title: Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving, DOI: 10.1145/3725843.3756121
This research was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP), the AI Semiconductor Graduate School Support Project, and the ICT R&D Program of the Ministry of Science and ICT and the IITP, with assistance from the Electronics and Telecommunications Research Institute (ETRI). The EDA tools were supported by IDEC (the IC Design Education Center).
2025.10.17 View 2243 -
The Fall of Tor for Just $2: A Solution to the Tor Vulnerability
<(From Left) Ph.D candidate Jinseo Lee, Hobin Kim, Professor Min Suk Kang>
KAIST research team has made a new milestone in global security research, becoming the first Korean research team to identify a security vulnerability in Tor, the world's largest anonymous network, and propose a solution.
On September 12, our university's Professor Min Suk Kang's research team from the School of Computing announced that they had received an Honorable Mention Award at the USENIX Security 2025 conference, held from August 13 to 15 in Seattle, USA.
The USENIX Security conference is one of the world's most prestigious conferences in information security, ranking first among all security and cryptography conferences and journals based on the Google Scholar h-5 index. The Honorable Mention Award is a highly regarded honor given to only about 6% of all papers.
The core of this research was the discovery of a new denial-of-service (DoS) attack vulnerability in Tor, the world's largest anonymous network, and the proposal of a method to resolve it. The Tor Onion Service, a key technology for various anonymity-based services, is a primary tool for privacy protection, used by millions of people worldwide every day.
The research team found that Tor's congestion-sensing mechanism is insecure and proved through a real-world network experiment that a website could be crippled for as little as $2. This is just 0.2% of the cost of existing attacks. The study is particularly notable as it was the first to show that the existing security measures implemented in Tor to prevent DoS attacks can actually make the attacks worse.
In addition, the team used mathematical modeling to uncover the principles behind this vulnerability and provided guidelines for Tor to maintain a balance between anonymity and availability. These guidelines have been shared with the Tor development team and are currently being applied through a phased patch.
A new attack model proposed by the research team shows that when an attacker sends a tiny, pre-designed amount of attack traffic to a Tor website, it confuses the congestion measurement system. This triggers an excessive congestion control, which ultimately prevents regular users from accessing the website. The research team proved through experiments that the cost of this attack is only 0.2% of existing methods.
In February, Tor founder Roger Dingledine visited KAIST and discussed collaboration with the research team. In June, the Tor administration paid a bug bounty of approximately $800 in appreciation for the team's proactive report.
"Tor anonymity system security is an area of active global research, but this is the first study on security vulnerabilities in Korea, which makes it very significant," said Professor Kang Min-seok. "The vulnerability we identified is very high-risk, so it received significant attention from many Tor security researchers at the conference. We will continue our comprehensive research, not only on enhancing the Tor system's anonymity but also on using Tor technology in the field of criminal investigation."
The research was conducted by Ph.D. candidate Jinseo Lee (first author), and former master's student Hobin Kim at the KAIST Graduate School of Information Security and a current Ph.D. candidate at Carnegie Mellon University (second author).
The paper is titled "Onions Got Puzzled: On the Challenges of Mitigating Denial-of-Service Problems in Tor Onion Services." https://www.usenix.org/conference/usenixsecurity25/presentation/lee
This achievement was recognized as a groundbreaking, first-of-its-kind study on Tor security vulnerabilities in Korea and played a decisive role in the selection of Professor Kang's lab for the 2025 Basic Research Program (Global Basic Research Lab) by the Ministry of Science and ICT.
< Photo 2. Presentation photo of Ph.D cadidate Jinseo Lee from School of Computing>
Through this program, the research team plans to establish a domestic research collaboration system with Ewha Womans University and Sungshin Women's University and expand international research collaborations with researchers in the U.S. and U.K. to conduct in-depth research on Tor vulnerabilities and anonymity over the next three years.
< Photo 3. Presentation photo of Ph.D cadidate Jinseo Lee from School of Computing>
2025.09.12 View 1501 -
Making Truly Smart AI Agents a Reality with the World's Best DB Integration Technology
<(From Left) Engineer Jeongho Park from GraphAI, Ph.D candidate Geonho Lee, Prof. Min-Soo Kim from KAIST>
For a long time, companies have been using relational databases (DB) to manage data. However, with the increasing use of large AI models, integration with graph databases is now required. This process, however, reveals limitations such as cost burden, data inconsistency, and the difficulty of processing complex queries.
Our research team has succeeded in developing a next-generation graph-relational DB system that can solve these problems at once, and it is expected to be applied to industrial sites immediately. When this technology is applied, AI will be able to reason about complex relationships in real time, going beyond simple searches, making it possible to implement a smarter AI service.
The research team led by Professor Min-Soo Kim announced on the 8th of September that the team has developed a new DB system named 'Chimera' that fully integrates relational DB and graph DB to efficiently execute graph-relational queries. Chimera has proven its world-class performance by processing queries at least 4 times and up to 280 times faster than existing systems in international performance standard benchmarks.
Unlike existing relational DBs, graph DBs have a structure that represents data as vertices (nodes) and edges (connections), which gives them a strong advantage in analyzing and reasoning about complexly intertwined information like people, events, places, and time. Thanks to this feature, its use is rapidly spreading in various fields such as AI agents, SNS, finance, and e-commerce.
With the growing demand for complex query processing between relational DBs and graph DBs, a new standard language, 'SQL/PGQ,' which extends relational query language (SQL) with graph query functions, has also been proposed.
SQL/PGQ is a new standard language that adds graph traversal capabilities to the existing database language (SQL) and is designed to query both table-like data and connected information such as people, events, and places at once. Using this, complex relationships such as 'which company does my friend's friend work for?' can be searched much more simply than before.
<Diagram (a): This diagram shows the typical architecture of a graph query processing system based on a traditional RDBMS. It has separate dedicated operators for graph traversal and an in-memory graph structure, while attribute joins are handled by relational operators. However, this structure makes it difficult to optimize execution plans for hybrid queries because traversal and joins are performed in different pipelines. Additionally, for large-scale graphs, the in-memory structure creates memory constraints, and the method of extracting graph data from relational data limits data freshness. Diagram (b): This diagram shows Chimera's integrated architecture. Chimera introduces new components to the existing RDBMS architecture: a traversal-join operator that combines graph traversal and joins, a disk-based graph storage, and a dedicated graph access layer. This allows it to process both graph and relational data within a single execution flow. Furthermore, a hybrid query planner integrally optimizes both graph and relational operations. Its shared transaction management and disk-based storage structure enable it to handle large-scale graph databases without memory constraints while maintaining data freshness. This architecture removes the bottlenecks of existing systems by flexibly combining traversal, joins, and mappings in a single execution plan, thereby simultaneously improving performance and scalability.>
The problem is that existing approaches have relied on either trying to mimic graph traversal with join operations or processing by pre-building a graph view in memory. In the former case, performance drops sharply as the traversal depth increases, and in the latter case, execution fails due to insufficient memory even if the data size increases slightly. Furthermore, changes to the original data are not immediately reflected in the view, resulting in poor data freshness and the inefficiency of having to combine relational and graph results separately.
KAIST research team's 'Chimera' fundamentally solves these limitations. The research team redesigned both the storage layer and the query processing layer of the database.
First, the research team introduced a 'dual-store structure' that operates a graph-specific storage and a relational data storage together. They then applied a 'traversal-join operator' that processes graph traversal and relational operations simultaneously, allowing complex operations to be executed efficiently in a single system. Thanks to this, Chimera has established itself as the world's first graph-relational DB system that integrates the entire process from data storage to query processing into one.
As a result, it recorded world-class performance on the international performance standard benchmark 'LDBC Social Network Benchmark (SNB),' being at least 4 times and up to 280 times faster than existing systems.
Query failure due to insufficient memory does not occur no matter how large the graph data becomes, and since it does not use views, there is no delay problem in terms of data freshness.
Professor Min-Soo Kim stated, "As the connections between data become more complex, the need for integrated technology that encompasses both graph and relational DBs is increasing. Chimera is a technology that fundamentally solves this problem, and we expect it to be widely used in various industries such as AI agents, finance, and e-commerce."
The study was co-authored by Geonho Lee, a Ph.D. student in KAIST School of Computing, as the first author, and Jeongho Park, an engineer at Professor Kim's startup GraphAI Co., Ltd., as the second author, with Professor Kim as the corresponding author.
The research results were presented on September 1st at VLDB, a world-renowned international academic conference in the field of databases. In particular, the newly developed Chimera technology is expected to have an immediate industrial impact as a core technology for implementing 'high-performance AI agents based on RAG (a smart AI assistant with search capabilities),' which will be applied to 'AkasicDB,' a vector-graph-relational DB system scheduled to be released by GraphAI Co., Ltd.
*Paper title: Chimera: A System Design of Dual Storage and Traversal-Join Unified Query Processing for SQL/PGQ *DOI: https://dl.acm.org/doi/10.14778/3705829.3705845
This research was supported by the Ministry of Science and ICT's IITP SW Star Lab and the National Research Foundation of Korea's Mid-Career Researcher Program.
2025.09.08 View 1224 -
KAIST develops “FlexGNN,” a graph analysis AI 95 times faster with a single GPU server
<(From Left) Donghyoung Han, CTO of GraphAI Co, Ph.D candidate Jeongmin Bae from KAIST, Professor Min-soo Kim from KAIST>
Alongside text-based large language models (LLMs) including ChatGPT, in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial transactions, stocks, social media, and patient records in graph form are being actively used. However, there is a limitation in that full graph learning—training the entire graph at once—requires massive memory and GPU servers. A KAIST research team has succeeded in developing the world’s highest-performance software technology that can train large-scale GNN models at maximum speed using only a single GPU server.
KAIST (President Kwang Hyung Lee) announced on the 13th that the research team led by Professor Min-Soo Kim of the School of Computing has developed “FlexGNN,” a GNN system that, unlike existing methods using multiple GPU servers, can quickly train and infer large-scale full-graph AI models on a single GPU server. FlexGNN improves training speed by up to 95 times compared to existing technologies.
Recently, in various fields such as climate, finance, medicine, pharmaceuticals, manufacturing, and distribution, there has been a growing number of cases where data is converted into graph form, consisting of nodes and edges, for analysis and prediction.
While the full graph approach, which uses the entire graph for training, achieves higher accuracy, it has the drawback of frequently running out of memory due to the generation of massive intermediate data during training, as well as prolonged training times caused by data communication between multiple servers.
To overcome these problems, FlexGNN performs optimal AI model training on a single GPU server by utilizing SSDs (solid-state drives) and main memory instead of multiple GPU servers.
<Figure (a): This illustrates the typical execution flow of a conventional full-graph GNN training system. All intermediate data generated during training are retained in GPU memory, and computations are performed sequentially without data movement or memory optimization. Consequently, if the GPU memory capacity is exceeded, training becomes infeasible. Additionally, inter-GPU data exchange relies solely on a fixed method (X_rigid), limiting performance and scalability. Figure (b): This depicts an example of the execution flow based on the optimized training execution plan generated by FlexGNN. For each intermediate data, strategies such as retention, offloading, or recomputation are selectively applied. Depending on resource constraints and data size, an appropriate inter-GPU exchange method—either GPU-to-GPU (G2G) or GPU-to-Host (G2H)—is adaptively chosen by the exchange operator (X_adapt). Furthermore, offloading and reloading operations are scheduled to overlap as much as possible with computation, maximizing compute-data movement parallelism. The adaptive exchange operator and various data offloading and reloading operators (R, O) within the figure demonstrate FlexGNN's ability to flexibly control intermediate data management and inter-GPU exchange strategies based on the training execution plan.>
Particularly, through AI query optimization training—which optimizes the quality of database systems—the team developed a new training optimization technology that calculates model parameters, training data, and intermediate data between GPU, main memory, and SSD layers at the optimal timing and method.
As a result, FlexGNN flexibly generates optimal training execution plans according to available resources such as data size, model scale, and GPU memory, thereby achieving high resource efficiency and training speed.
Consequently, it became possible to train GNN models on data far exceeding main memory capacity, and training could be up to 95 times faster even on a single GPU server. In particular, the realization of full-graph AI, capable of more precise analysis than supercomputers in applications such as climate prediction, has become a reality.
Professor Min-Soo Kim of KAIST stated, “As full-graph GNN models are actively used to solve complex problems such as weather prediction and new material discovery, the importance of related technologies is increasing.” He added that “since FlexGNN has dramatically solved the longstanding problems of training scale and speed in graph AI models, we expect it to be widely used in various industries.”
In this research, Jeongmin Bae, a doctoral student in the School of Computing at KAIST, participated as the first author, Donghyoung Han, CTO of GraphAI Co. (founded by Professor Kim) participated as the second author, and Professor Kim served as the corresponding author.
The research results were presented on August 5 at ACM KDD, a world-renowned data mining conference. The FlexGNN technology is also planned to be applied to Grapheye’s graph database solution, GraphOn.
● Paper title: FlexGNN: A High-Performance, Large-Scale Full-Graph GNN System with Best-Effort Training Plan Optimization
● DOI: https://doi.org/10.1145/3711896.3736964
This research was supported by the IITP SW Star Lab and IITP-ITRC of the Ministry of Science and ICT, as well as the mid-level project program of the National Research Foundation of Korea.
2025.08.13 View 2502