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Depression is Not Only a Disease of the Mind. KAIST Discovers the Immune-Brain Connection
<(From Left) Ph.D candidate Insook Ahn from KAIST, Professor Jinju Han from KAIST, (Upper Left) Yangsik Kim from Inhan University School of Medicine, Ph.D candidate Soyeon Chang(psychiatrist)>
Major depressive disorder (MDD) is characterized by a lowered mood and loss of interest, contributing not only to difficulties in academic and professional life but also as a major cause of suicide in South Korea. However, there are currently no objective biological markers that can be used for diagnosis or treatment. Amidst this, a research team from KAIST has revealed that depression is not merely a problem of the mind or brain, but is deeply connected to abnormalities in the body's overall immune response. They found that this immune abnormality affects brain function, and the 'Immune Neural Axis' imbalance is the core mechanism of depression, opening up the possibility for the discovery of new biomarkers and the development of new drugs for depression treatment.
KAIST announced on the November 20th that Professor Jinju Han's research team from the Graduate School of Medical Science and Engineering (GSMSE) at KAIST, in collaboration with Professor Yangsik Kim's research team (Ph.D., KAIST GSMSE) from Inha University School of Medicine, performed a multi-omics analysis combining plasma proteomic analysis, WBC single-cell analysis, and patient-derived brain organoids (mini-brains). This study focused on female patients with MDD who exhibited 'Atypical Features' (such as hypersomnia and overeating) and 'Psychotic Symptoms'(such as auditory hallucinations and idea of reference), which are different from typical depression symptoms, and who also had impaired reality judgment.
Sduio
■ "Immune Cells and Brain Function are Altered Together" A New Biological Clue for Depression
The research team simultaneously examined genetic changes in immune cells in the blood and changes in nervous-system-related proteins. The results confirmed a breakdown in the balance of immune-neural interaction in patients with depression.
MDD, especially in young women, often presents with atypical symptoms (hypersomnia, overeating, mood reactivity, etc.), which increases the risk of a later diagnosis of bipolar disorder. Furthermore, about 40% of patients are classified as treatment-resistant depression, showing no response to various antidepressants.
Consequently, there has been a continuous call for the development of new therapeutic strategies and the discovery of biomarkers based on immunity and metabolism, moving beyond the traditional drug-centric approach.
■ World's First Integration of "Leukocyte Single-Cell Analysis + Brain Organoid" A New Paradigm for Psychiatric Research
The research team presented the world's first precision medicine approach by integrating plasma proteomics, leukocyte single-cell transcriptome analysis, and analysis of brain organoids created from patient-derived induced pluripotent stem cells (iPSCs).
The results showed that patients with atypical depression exhibited high levels of stress, anxiety, and depression. Furthermore, proteins crucial for inter-neuronal signaling (DCLK3 and CALY) were significantly elevated compared to normal levels, and Complement Protein C5, which strongly enhances the body's immune response, was also increased. This indicates that both 'brain function' and 'immune function' are excessively activated and out of balance within the body.
This finding confirms a clue that depression is not merely a mood issue but is connected to biological changes occurring throughout the entire body. Upon examining the immune cells of depression patients, genetic changes were found that make inflammatory responses in the body occur more easily and strongly than usual. This implies that the entire bodily immune system is in a state of excessive activation, and this immune/inflammatory abnormality may influence the development of depression.
The patient-derived brain organoids showed accompanying growth retardation and abnormal neural development, supporting the possibility that immune abnormalities interact with changes in brain function to exacerbate the disease.
■ "Immune-Neural Axis Imbalance is the Core Mechanism of Atypical Depression"
This study integrated clinical data, single-cell omics, proteomics, and brain organoids to demonstrate that the 'Imbalance of the Immune-Neural Axis' is the core mechanism of MDD accompanied by atypical and psychotic symptoms.
<Integration of clinical symptoms, blood analysis, and patient-derived brain organoid analysis in women with major depressive disorder>
Professor Jinju Han stated, "This achievement presents a new precision medicine model for psychiatric research," adding, "We anticipate that this will actively lead to biomarker discovery and new drug development."
This accomplishment was published online in the world-renowned international scientific journal, Advanced Science, on October 31st.
※ Paper Title: Exploration of Novel Biomarkers through a Precision Medicine Approach Using Multi-omics and Brain Organoids in Patients with Atypical Depression and Psychotic Symptoms DOI: https://doi.org/10.1002/advs.202508383
※ Author Information: Soyeon Chang (Inha University, Co-First Author), Seok-Ho Choi, Jiyoung Lee, Yangsik Kim (Inha University, Corresponding Author), Insook Ahn (KAIST, Co-First Author), and Jinju Han (KAIST, Corresponding Author)
This research was supported by the National Research Foundation of Korea and the Korea Health Industry Development Institute.
2025.11.20 View 18828 -
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 6609 -
3D Worlds from Just a Few Phone Photos
<(From Left) Ph.D candidate Jumin Lee, Ph.D candidate Woo Jae Kim, Ph.D candidate Youngju Na, Ph.D candidate Kyu Beom Han, Professor Sung-eui Yoon>
Existing 3D scene reconstructions require a cumbersome process of precisely measuring physical spaces with LiDAR or 3D scanners, or correcting thousands of photos along with camera pose information. The research team at KAIST has overcome these limitations and introduced a technology enabling the reconstruction of 3D —from tabletop objects to outdoor scenes—with just two to three ordinary photographs. The breakthrough suggests a new paradigm in which spaces captured by camera can be immediately transformed into virtual environments.
KAIST announced on November 6 that the research team led by Professor Sung-Eui Yoon from the School of Computing has developed a new technology called SHARE (Shape-Ray Estimation), which can reconstruct high-quality 3D scenes using only ordinary images, without precise camera pose information.
Existing 3D reconstruction technology has been limited by the requirement of precise camera position and orientation information at the time of shooting to reproduce 3D scenes from a small number of images. This has necessitated specialized equipment or complex calibration processes, making real-world applications difficult and slowing widespread adoption.
To solve these problems, the research team developed a technology that constructs accurate 3D models by simultaneously estimating the 3D scene and the camera orientation using just two to three standard photographs. The technology has been recognized for its high efficiency and versatility, enabling rapid and precise reconstruction in real-world environments without additional training or complex calibration processes.
While existing methods calculate 3D structures from known camera poses, SHARE autonomously extracts spatial information from images themselves and infers both camera pose and scene structure. This enables stable 3D reconstruction without shape distortion by aligning multiple images taken from different positions into a single unified space.
<Representative Image of SHARE Technology>
"The SHARE technology is a breakthrough that dramatically lowers the barrier to entry for 3D reconstruction,” said Professor Sung-Eui Yoon. “It will enable the creation of high-quality content in various industries such as construction, media, and gaming using only a smartphone camera. It also has diverse application possibilities, such as building low-cost simulation environments in the fields of robotics and autonomous driving."
<SHARE Technology, Precise Camera Information and 3D Scene Prediction Technology>
Ph.D. Candidate Youngju Na and M.S candidate Taeyeon Kim participated as co-first authors on the research. The results were presented on September 17th at the IEEE International Conference on Image Processing (ICIP 2025), where the paper received the Best Student Paper Award.
The award, given to only one paper among 643 accepted papers this year—a selection rate of 0.16 percent—once again underscores the excellent research capabilities of the KAIST research team.
Paper Title: Pose-free 3D Gaussian Splatting via Shape-Ray Estimation, DOI: https://arxiv.org/abs/2505.22978
Award Information: https://www.linkedin.com/posts/ieeeicip_congratulations-to-the-icip-2025-best-activity-7374146976449335297-6hXz
This achievement was carried out with support from the Ministry of Science and ICT's SW Star Lab Project under the task 'Development of Perception, Action, and Interaction Algorithms for Unspecified Environments for Open World Robot Services.'
2025.11.10 View 2503 -
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 2878 -
KAIST Welcomes NVIDIA CEO Jensen Huang’s Cooperation Initiative “Strengthening Collaboration in AI and Robotics Innovation”
KAIST (President Kwang Hyung Lee) announced its strong support for the meeting between Korean President Jae-myung Lee and NVIDIA CEO Jensen Huang on October 31, where both sides discussed strategies to advance Korea’s AI ecosystem.
KAIST stated that the meeting marks “a significant turning point for Korea’s AI innovation and global cooperation.” During the discussion, NVIDIA, a global leader in artificial intelligence, explored partnership opportunities with the Korean government to realize its vision of becoming one of the “Top Three AI Nations” and achieving an “AI-based Society.”
NVIDIA also unveiled plans to expand Korea’s AI computing infrastructure by introducing more than 260,000 of its latest GPUs, while strengthening technology cooperation to meet both public and private sector AI demand.
The meeting covered a wide range of potential collaborations, including:
Building advanced AI infrastructure, joint research and technology cooperation in physical AI (AI in robotics, autonomous systems, and manufacturing), and
expanding AI talent development and startup support programs.
At the APEC CEO Summit, NVIDIA CEO Jensen Huang said, “NVIDIA’s goal is not only to provide hardware to Korea, but to help build a sustainable AI ecosystem. And we will work closely with AI researchers in Korea universities, amazing university like KAIST, startups, the government, and research institutions to become the AI Frontier.”
He further emphasized that, “The evolution of AI will inevitably converge with robotics. Realizing autonomous robots and robotic factories that can work alongside humans represents the next stage and ultimate goal of AI technology.”
As Korea’s leading AI research institution, KAIST has long collaborated with government and industry partners in key areas such as AI semiconductors, autonomous driving, robotics, digital twins, and quantum computing.
Building on this dialogue, KAIST plans to further strengthen its partnership with NVIDIA and major domestic industries through next-generation AI semiconductor and HBM (High Bandwidth Memory) research, physical AI applications in robotics and autonomous systems, hands-on AI education and talent development, and global open innovation through academia–industry joint research.
KAIST President Kwang Hyung Lee stated: “AI is the core driver of national competitiveness. Jensen Huang’s visit represents a symbolic milestone as Korea emerges as a global leader in AI.” He added: “Huang’s vision of integrating AI and robotics aligns perfectly with KAIST’s research direction. KAIST will continue to work closely with NVIDIA to build an AI innovation ecosystem that benefits humanity.”
Following CEO Huang’s proposal, KAIST will further concretize its collaboration with NVIDIA and expand partnerships with both global enterprises and domestic industries.
Through these efforts, KAIST aims to advance AI research clusters, develop next-generation AI computing platforms, nurture AI professionals, and foster a vibrant startup ecosystem, contributing continuously to Korea’s global AI competitiveness.
2025.11.03 View 2167 -
Failure in the AI Era? The 3rd Failure Conference Held
< 2025 Failure Conference Poster >
KAIST announced on the 31st of October that it will be holding the '3rd Failure Conference' from Wednesday, November 5th to Friday, November 14th. The event is organized by the KAIST Center for Ambitious Failure (Director Sungho Jo), and, under the theme 'AI times Failure,' it will re-examine the value of humaneness through the sensibility of 'failure' in this era of great transformation led by AI technology.
Composed of lectures, competitions, exhibitions, and networking programs, this conference provides a venue for new introspection on the relationship between humanity, society, and technology through the lens of 'failure.'
Failure Seminar 'AI Era, Asking the Way of Humanity' will be held on November 6th at the Jeong Geun-mo Conference Hall in the Academic and Cultural Complex
Professor Juho Kim of the KAIST School of Computing will discuss the human sensibility and resilience needed in the AI era through the paradox that "AI learns how to fail less, but humans are losing the opportunity to fail. Following this, Professor Sang Wook Lee of the Hanyang University Department of Philosophy will present philosophical and ethical challenges and practical directions for the advancement of AI technology to lead to universal welfare for humanity. The 'AI times Failure Idea Contest' Finals will take place on November 7th at the John Hanner Hall in the Academic and Cultural Complex. 12 teams, selected from preliminaries that included 111 teams from universities and graduate schools nationwide, will demonstrate their ideas in booth form on the theme of 'The Future where AI and Humans Coexist.' Participants will explore AI errors, human limitations, and the possibility of trust and recovery, presenting attempts to convert technological failure into human introspection, and human failure into technological possibility. On the day of the finals, the Grand Prize (KAIST President’s Award), First Prize, and Second Prize will be selected through judging.
The Photography Exhibition '404: Perfection Not Found' will be held on the 1st floor of the Creative Learning Building from November 5th to 14th. This exhibition showcases 'Scenes of Imperfection' captured by KAIST members through the PhotoVoice program and the AI times Failure Snapshot Challenge. It is divided into three sections: ▲ Brain that Mimics Perfection: Failure of AI ▲ Incomplete Connection: Portrait of the Digital Generation ▲ Aesthetics of Imperfection: Warmth of Humanity, providing a space for introspection that illuminates human responsibility and potential through technological failure. The 'Show Off Your Failed Project Contest,' which has garnered great response from KAIST students every year, will be expanded to include general public participation on the 5th at the John Hanner Hall in the Academic and Cultural Complex. Co-planned by the KAIST Center for Ambitious Failure and the student club ICISTS, participants will decorate their own booths with photos and videos to share their failures and the process of overcoming them. Awards such as ▲ Best (Most Votes) ▲ Shining Debris Award (Highly Relatable Failure Story) ▲ Flower of Ash Award (Overcoming Story) ▲ Aesthetics of Failure Award (Creative Expression) ▲ Beautiful Afterimage Award (Sincere Lingering Impression) will be selected through audience voting.
< 2025 Show Off Your Failed Project Contest Poster >
Sungho Jo, KAIST Center for Ambitious Failure (Professor, School of Computing), stated, "As AI technology rapidly evolves and changes the order of the world, humans need to look back at themselves beyond that speed. I hope this Failure Conference will be an opportunity to rediscover the meaning of humaneness amid technological innovation and to imagine a better future." Kwang Hyung Lee, President of KAIST, said, "Failure is another name for challenge, and a seed of innovation. KAIST will lead the AI era and human-centered technological development through a creative spirit of challenge that is not afraid of failure."
All programs for the 2025 Failure Conference are open to anyone interested, and detailed schedules and content can be checked on the webstie of KAIST Center for Ambitious Failure (caf.kaist.ac.kr).
2025.10.29 View 1923 -
“AI,” the New Language of Materials Science and Engineering Spoken at KAIST
<(From Left) M.S candidate Chaeyul Kang, Professor Seumgbum Hong, Ph. D candidate Benediktus Madika, Ph.D candidate Batzorig Buyantogtokh, Ph.D candiate Aditi Saha, >
Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee).
Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization
The era has arrived in which artificial intelligence (AI) autonomously imagines and predicts the structures and properties of new materials. Today, AI functions as a researcher’s “second brain,” actively participating in every stage of research, from idea generation to experimental validation.
KAIST (President Kwang Hyung Lee) announced on October 26 that a comprehensive review paper analyzing the impact of AI, Machine Learning (ML), and Deep Learning (DL) technologies across materials science and engineering has been published in ACS Nano (Impact Factor = 18.7). The paper was co-authored by Professor Seungbum Hong and his team from the Department of Materials Science and Engineering at KAIST, in collaboration with researchers from Drexel University, Northwestern University, the University of St Andrews, and the University of Tennessee in the United States.
The research team proposed a full-cycle utilization strategy for materials innovation through an AI-based catalyst search platform, which embodies the concept of a Self-Driving Lab—a system in which robots autonomously perform materials synthesis and optimization experiments.
Professor Hong’s team categorized materials research into three major stages—Discovery, Development, and Optimization—and detailed the distinctive role of AI in each phase:
In the Discovery Stage, AI designs new structures, predicts properties, and rapidly identifies the most promising materials among vast candidate pools.
In the Development Stage, AI analyzes experimental data and autonomously adjusts experimental processes through Self-Driving Lab systems, significantly shortening research timelines.
In the Optimization Stage, AI employs Reinforcement Learning, which identifies optimal conditions through Bayesian Optimization, which efficiently finds superior results with minimal experimentation, to fine-tune designs and process conditions for maximum performance.
In essence, AI serves as a “smart assistant” that narrows down the most promising materials, reduces experimental trial and error, and autonomously optimizes experimental conditions to achieve the best-performing outcomes.
The paper further highlights how cutting-edge technologies such as Generative AI, Graph Neural Networks (GNNs), and Transformer models are transforming AI from a computational tool into a “thinking researcher.” Nonetheless, the team cautions that AI’s predictions are not error-proof and that key challenges persist, such as imbalanced data quality, limited interpretability of AI predictions, and integration of heterogeneous datasets.
To address these limitations, the authors emphasize the importance of developing AI systems capable of autonomously understanding physical principles and ensuring transparent, verifiable decision-making processes for researchers.
The review also explores the concept of the Self-Driving Lab, where AI autonomously designs experimental plans, analyzes results, and determines the next experimental steps—without manual operation by researchers. The AI-Based Catalyst Search Platform exemplifies this concept, enabling robots to automatically design, execute, and optimize catalyst synthesis experiments.
In particular, the study presents cases in which AI-driven experimentation has dramatically accelerated catalyst development, suggesting that similar approaches could revolutionize research in battery and energy materials.
<AI Driving Innovation Across the Entire Cycle of New Material Discovery, Development, and Optimization>
“This review demonstrates that artificial intelligence is emerging as the new language of materials science and engineering, transcending its role as a mere tool,” said Professor Seungbum Hong. “The roadmap presented by the KAIST team will serve as a valuable guide for researchers in Korea’s national core industries including batteries, semiconductors, and energy materials.”
Benediktus Madika (Ph.D. candidate), Aditi Saha (Ph.D. candidate), Chaeyul Kang (M.S. candidate), and Batzorig Buyantogtokh (Ph.D. candidate) from KAIST’s Department of Materials Science and Engineering contributed as co-first authors.
Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee).
Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization
DOI: 10.1021/acsnano.5c04200
This work was supported by the National Research Foundation of Korea (NRF) with funding from the Ministry of Science and ICT (RS-2023-00247245).
2025.10.27 View 2694 -
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 2606 -
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 3383 -
The Stricter a Country’s Environmental Regulations, the Better Electric Cars Sell
<(From Left) Professor Narae Lee, Professor Heather Berry, Professor Jasmina Chauvin, Porfessor Yuxi Lance Cheng>
A joint international research team has challenged the traditional 'pollution haven' hypothesis—which suggests that companies relocate production to countries with lax environmental regulations—by proposing a new strategy that companies should seek a 'green haven' instead. This finding is attracting attention.
KAIST (President Kwang Hyung Lee) announced on the 17th of October that the research team led by Professor Narae Lee of the KAIST College of Business, through an international joint study with Professors Heather Berry and Jasmina Chauvin of Georgetown University in the U.S., and Professor Lance Cheng of the University of Texas, revealed that 'green products, such as electric vehicles, are more competitive when menufactured in countries with strict environmental regulations.'
'Green products' are eco-friendly products that cause less environmental pollution, including energy-efficient home appliances that consume less electricity, and eco-friendly vehicles (electric cars, hybrid cars) that reduce pollution.
For a long time, the dominant explanation was that multinational corporations primarily concentrated production and export in countries with weak environmental regulations. However, with the recent strengthening of climate change response and ESG (Environmental, Social, and Governance) management, the global trade of green products is rapidly expanding. This has led to new patterns that are difficult to explain with existing theories alone.
The joint research team precisely verified trade patterns by analyzing data from 'UN Comtrade,' the global trade database operated by the UN, covering 92 importing countries, 70 exporting countries, and approximately 5,000 products from 2002 to 2019.
<Figure1. Changes in National EPI Index and Export Volume According to Product Characteristics>
The result confirmed a typical pollution haven effect: the overall trade volume decreased when environmental regulations were strengthened. However, for green products only, trade was found to increase. In other words, the stricter the environmental regulations, the more active the export and sourcing of green products became.
<Figure2. Changes in Global Sourcing According to Product Characteristics>
This shows that companies are not simply moving to regions with loose regulations to save on production costs. Instead, they prefer countries with strong regulations to secure transparency and legitimacy in the production and transaction process of eco-friendly products.
This effect was particularly prominent in the final consumer goods sector, which directly interacts with consumers—i.e., smartphones, clothing, food, cosmetics, home appliances, and automobiles that we use daily—and the tendency was even stronger for products exported to countries with active environmental movements or NGO activities.
Professor Lee emphasized, "This study shows that global supply chains can no longer be explained solely by cost efficiency, and that a company's environmental legitimacy determines its strategic choices." She added, "Strong environmental policies do not restrict corporate activities; they can become the foundation for enhancing the competitiveness of green products."
The research findings were published on September 1st in the Journal of International Business Studies (JIBS), the top academic journal in the field of international business.
Paper Title: The global sourcing of green products. https://doi.org/10.1057/s41267-025-00801-2
This research was made available for free viewing through KAIST's Open Access publication support, and it is expected that the research results will be utilized in academia and policy-making.
2025.10.17 View 2060 -
Chemobiological Platform Enables Renewable Conversion of Sugars into Core Aromatic Hydrocarbons of Petroleum
<(From Left) Professor Sun Kyu Han, Ph.D candidate Tae Wan Kim, Professor Kyeong Rok Choi, Professor Sang Yup Lee>
With growing concerns over fossil fuel depletion and the environmental impacts of petrochemical production, scientists are actively exploring renewable strategies to produce essential industrial chemicals. A collaborative research team—led by Distinguished Professor Sang Yup Lee, Senior Vice President for Research, from the Department of Chemical and Biomolecular Engineering, together with Professor Sunkyu Han from the Department of Chemistry at the Korea Advanced Institute of Science and Technology (KAIST)—has developed an integrated chemobiological platform that converts renewable carbon sources such as glucose and glycerol into oxygenated precursors, which are subsequently deoxygenated in the same solvent system to yield benzene, toluene, ethylbenzene, and p-xylene (BTEX), which are fundamental aromatic hydrocarbons used in fuels, polymers, and consumer products.
<Figure 1. Schematic representation of the chemobiological synthesis of BTEX from glucose or glycerol in Escherichia coli>
From Sugars to Aromatic Hydrocarbons of Petroleum
The researchers designed four metabolically engineered strains of Escherichia coli, each programmed to produce a specific oxygenated precursor—phenol, benzyl alcohol, 2-phenylethanol, or 2,5-xylenol. These intermediates are generated through tailored genetic modifications, such as deletion of feedback-regulated enzymes, overexpression of pathway-specific genes, and introduction of heterologous enzymes to expand metabolic capabilities.
During fermentation, the products were continuously extracted into the organic solvent isopropyl myristate (IPM). Acting as a dual-function solvent, IPM not only mitigated the toxic effects of aromatic compounds on cell growth but also served directly as the reaction medium for downstream chemical upgrading. By eliminating the need for intermediate purification, solvent exchange, or distillation, this solvent-integrated system streamlined the conversion of renewable feedstocks into valuable aromatics.
Overcoming Chemical Barriers in An Unconventional Solvent
A central innovation of this work lies in adapting chemical deoxygenation reactions to function efficiently within IPM—a solvent rarely used in organic synthesis. Traditional catalysts and reagents often proved ineffective under these conditions due to solubility limitations or incompatibility with biologically derived impurities.
Through systematic optimization, the team established mild and selective catalytic strategies compatible with IPM. For example, phenol was successfully deoxygenated to benzene in up to 85% yield using a palladium-based catalytic system, while benzyl alcohol was efficiently converted to toluene after activated charcoal pretreatment of the IPM extract. More challenging transformations, such as converting 2-phenylethanol to ethylbenzene, were achieved through a mesylation–reduction sequence adapted to the IPM phase. Likewise, 2,5-xylenol derived from glycerol was converted to p-xylene in 62% yield via a two-step reaction, completing the renewable synthesis of the full BTEX spectrum.
A Sustainable, Modular Framework
Beyond producing BTEX, the study establishes a generalizable framework for integrating microbial biosynthesis with chemical transformations in a continuous solvent environment. This modular approach reduces energy demand, minimizes solvent waste, and enables process intensification—key factors for scaling up renewable chemical production.
The high boiling point of IPM (>300 °C) simplifies product recovery, as BTEX compounds can be isolated by fractional distillation while the solvent is readily recycled. Such a design is consistent with the principles of green chemistry and the circular economy, providing a practical alternative to fossil-based petrochemical processes.
Toward A Carbon-Neutral Future
Dr. Xuan Zou, the first author of this paper, explaind, “By coupling the selectivity of microbial metabolism with the efficiency of chemical catalysis, this platform establishes a renewable pathway to some of the most widely used building blocks in the chemical industry. Future efforts will focus on optimizing metabolic fluxes, extending the platform to additional aromatic targets, and adopting greener catalytic systems.”
In addition, Distinguished Professor Sang Yup Lee noted “As the global demand for BTEX and related chemicals continues to grow, this innovation provides both a scientific and industrial foundation for reducing reliance on petroleum-based processes. It marks an important step toward lowering the carbon footprint of the fuel and chemical sectors while ensuring a sustainable supply of essential aromatic hydrocarbons.”
This research was supported by the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries Project (2022M3J5A1056117) and the Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry Project (RS-2024-00399424), funded by the National Research Foundation supported by the Korean Ministry of Science and ICT. This study was published in the latest issue of the Proceedings of the National Academy of Sciences of the United States of America (PNAS).
2025.10.13 View 2597 -
Sharing Failures Makes Challenges Easier, Proposal for a National Campaign for Global Failure Day
KAIST announced that it will launch a national campaign on 'Global Failure Day,' October 13th, to encourage anyone in the nation to share their small, everyday failures.
KAIST President Kwang Hyung Lee emphasized, "A culture unafraid of failure is the foundation of innovation. I hope that for just one day, October 13th, people recall and share the small failures they experienced today. That moment can be the starting point for a new challenge."
'Global Failure Day' is a commemorative day that began in 2010 by students at Aalto University in Finland under the spirit of "Failure is inherent in the nature of challenge; let's respect failure." At the time, amid the collapse of Nokia and job insecurity, it garnered significant social support and spread as a national campaign. It continued in countries like Germany, the UK, and Canada, and has now established itself globally as a day for reflecting on failure.
Since the establishment of the KAIST Failure Lab, there has been a clear shift in the perception of failure. According to a survey conducted by the Failure Lab in December last year, 73.9% of KAIST members responded that the atmosphere encourages new challenges, which is twice the Korean social average (35.6%). Furthermore, 52% responded that it is a "place tolerant of failure," much higher than the Korean average (20.5%).
To spread this shift in perception nationwide, President Lee personally posted a message on social media on the 10th, sharing his own story of a failure where he felt embarrassed after having a donation rejected, and proposed participation. Additionally, the KAIST Failure Lab announced a 'Failure Sharing Action Proposal' that anyone can easily participate in on a daily basis.
The main proposals include: △ Sharing 'Today's Failure' with family and friends; △ Having a 'One-Line Failure Sharing' time at work or in a gathering; △ Posting small failure stories on social media; △ Sharing photos/videos of disastrous cooking or silly mistakes; and △ Creating memes that humorously express failure.
Seongho Cho, Director of the Failure Lab, explained, "Simply sharing failures lightly can change the attitude towards them. The fact that the failure acceptance rate among KAIST members is twice as high as the general public is also thanks to this culture."
Since its establishment in 2021, the KAIST Failure Lab has promoted a culture of failure sharing within the university through various programs, such as the 'Failed Project Bragging Contest,' failure essay contests, and 'Failure Photo Voice.' It has been conducting perception surveys among KAIST members every two years since 2022, and in last year's survey, over 80% of respondents said the lab's activities contributed to improving perception, resilience, and flexibility.
Based on these achievements, the scope of the activities is being expanded nationwide this year. Notably, the top 10 teams in the 'AI × Failure Idea Contest' for university/graduate students are scheduled to present their ideas at the 'Failure Conference' to be held at KAIST on November 7th.
President Lee stated, "KAIST will continue to broaden the culture of reflecting on and sharing failure together with the public."
More details can be found on the KAIST Failure Lab website (https://caf.kaist.ac.kr).
2025.10.13 View 1662