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KAIST and the World Bank Launch Digital Innovation Initiative to Boost Youth Employment in East Africa
Daejeon, Republic of Korea — November 2025 — KAIST has joined forces with the World Bank to launch a new initiative aimed at advancing youth employment and social protection systems through digital innovation in East Africa. The project, titled “Enhancing Youth Employment Policies through Digital Technologies,” will be implemented in Rwanda, Kenya, and Tanzania over the next three years.
The initiative is jointly led by Professor Kyung Ryul Park of the KAIST Graduate School of Science and Technology Policy, John Van Dyck, Director of the World Bank’s Social Protection and Labor (SPL) Global Practice, and Yoon Young Cho, Senior Economist at the World Bank. Supported by the Korea–World Bank Partnership Facility (KWPF), the project is funded at approximately KRW 1.4 billion (USD 980,000) and will run through 2028.
The collaboration aims to strengthen youth employment and advance the digital transformation of social protection systems in East Africa. In many developing countries, such systems are still managed manually, resulting in inefficiencies and inaccuracies. To address these challenges, the project will establish AI- and big data–driven digital social registry systems that enhance transparency, accuracy, and efficiency in social service delivery.
Beyond technology transfer, the project will also explore broader social and policy challenges that arise in digital labor markets — including algorithmic bias, ethical considerations in AI, and new forms of inequality. Through this work, the partners aim to develop a new model for an “inclusive AI transition,” ensuring that technological innovation contributes to social inclusion and sustainable development. Findings from the project will be published in World Bank reports and policy briefs.
As a global leader in digital governance and data-driven policymaking, South Korea’s experience is expected to play a key role in helping East African governments design and implement resilient, inclusive, and data-based labor and social protection systems.
The KAIST Global Center for Development and Strategy (G-CODEs) will organize two international workshops in collaboration with the Korea Development Institute (KDI), the Ministry of Employment and Labor of Korea, and the Kenya Advanced Institute of Science and Technology (Kenya-AIST). These workshops will help local officials build capacity in applying digital technologies, while providing KAIST researchers and students with hands-on experience in global development cooperation.
A kickoff workshop was held during the World Bank Annual Meetings earlier this month, with participation from Professors Kyung-Ryul Park and Seok-Kyun Woo (Graduate School of Science and Technology Policy), Dean Ji-Yong Eom (Graduate School of Green Growth and Sustainability), Researcher Seung-Hyun Kim, and Consultant Ji-Su Sim (M.S. Class of 2025, STP).
“This collaboration is not merely a technical project but an innovative effort to digitally connect youth employment and social protection systems,” said John Van Dyck, Director of the World Bank SPL Global Practice. “It will help East African governments design sustainable and inclusive digital labor infrastructures.”
Yoon Young Cho, Senior Economist at the World Bank, added, “The project seeks to digitalize social protection systems in East Africa to promote youth employment and social inclusion, focusing on building sustainable, government-led public digital solutions.”
Professor Kyung-Ryul Park of KAIST stated, “Through this partnership with the World Bank, we hope to support inclusive development in East Africa while offering KAIST researchers and students valuable opportunities to learn and grow through real-world international cooperation.”
2025.11.05 View 1516 -
KAIST Develops Room-Temperature 3D Printing Technology for ‘Electronic Eyes’—Miniaturized Infrared Sensors
<(From Left) Professor Ji Tae Kim of the Department of Mechanical Engineering, Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong>
The “electronic eyes” technology that can recognize objects even in darkness has taken a step forward. Infrared sensors, which act as the “seeing” component in devices such as LiDAR for autonomous vehicles, 3D face recognition systems in smartphones, and wearable healthcare devices, are regarded as key components in next-generation electronics. Now, a research team at KAIST and their collaborators have developed the world’s first room-temperature 3D printing technology that can fabricate miniature infrared sensors in any desired shape and size.
KAIST (President Kwang Hyung Lee) announced on the 3rd of November that the research team led by Professor Ji Tae Kim of the Department of Mechanical Engineering, in collaboration with Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong, has developed a 3D printing technique capable of fabricating ultra-small infrared sensors—smaller than 10 micrometers (µm)—in customized shapes and sizes at room temperature.
Infrared sensors convert invisible infrared signals into electrical signals and serve as essential components in realizing future electronic technologies such as robotic vision. Accordingly, miniaturization, weight reduction, and flexible form-factor design have become increasingly important.
Conventional semiconductor fabrication processes were well suited for mass production but struggled to adapt flexibly to rapidly changing technological demands. They also required high-temperature processing, which limited material choices and consumed large amounts of energy.
To overcome these challenges, the research team developed an ultra-precise 3D printing process that uses metal, semiconductor, and insulator materials in the form of liquid nanocrystal inks, stacking them layer by layer within a single printing platform.
This method enables direct fabrication of core components of infrared sensors at room temperature, allowing for the realization of customized miniature sensors of various shapes and sizes.
Particularly, the researchers achieved excellent electrical performance without the need for high-temperature annealing by applying a “ligand-exchange” process, where insulating molecules on the surface of nanoparticles are replaced with conductive ones.
As a result, the team successfully fabricated ultra-small infrared sensors measuring less than one-tenth the thickness of a human hair (under 10 µm).
<Figure 1. 3D printing of infrared sensors.a. Room-temperature printing process for the electrodes and photoactive layer that make up the infrared sensor.b. Structure and chemical composition of the printed infrared microsensor. c.Printed infrared sensor micropixel array.>
Professor Ji Tae Kim commented, “The developed 3D printing technology not only advances the miniaturization and lightweight design of infrared sensors but also paves the way for the creation of innovative new form-factor products that were previously unimaginable. Moreover, by reducing the massive energy consumption associated with high-temperature processes, this approach can lower production costs and enable eco-friendly manufacturing—contributing to the sustainable development of the infrared sensor industry.”
The research results were published online in Nature Communications on October 16, 2025, under the title “Ligand-exchange-assisted printing of colloidal nanocrystals to enable all-printed sub-micron optoelectronics” (DOI: https://doi.org/10.1038/s41467-025-64596-4).
This research was supported by the Ministry of Science and ICT of Korea through the Excellent Young Researcher Program (RS−2025−00556379), the National Strategic Technology Material Development Program (RS−2024−00407084), and the International Cooperation Research Program for Original Technology Development (RS−2024−00438059).
2025.11.03 View 2140 -
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 893 -
“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 1037 -
Robot-Operated Space Station Construction Goal... 'In-space Servicing and Manufacturing Research Center' Launched
<Plaque Handover Ceremony. (From left) Jae-Hung Han, Director of the Space Research Institute, Ju-won Kang, Head of Engineering Group at the National Research Foundation of Korea Basic Research Headquarters>
KAIST's Space Research Institute announced on the 24th of October that it officially launched the 'Innovative Research Center for the Development of Core Technologies in In-space Servicing and Manufacturing (ISMRC)' at the KAIST Academic Cultural Center on Friday, October 24. About 150 officials from major organizations, including the Korea Aerospace Administration, the National Research Foundation of Korea, and Daejeon Metropolitan City, as well as domestic and foreign space experts, attended the opening ceremony to discuss future cooperation measures. The 'KAIST In-space Servicing and Manufacturing Research Center (ISMRC)' is a large-scale research hub selected for the Ministry of Science and ICT's 2025 Basic Research Project, with a total of 71.2 billion KRW long-term project planned over the next 10 years, including 50 billion KRW in national funding. Daejeon City will also provide a total of 3.6 billion KRW, with 400 million KRW annually starting from 2026. The research goals are to secure core technologies for next-generation space exploration, including: ▲ Construction of Unmanned Space Stations, ▲ Robotics-based In-space Manufacturing, and ▲ Resource Recovery Technology. A team of 14 KAIST professors, led by Director Jae-Hung Han, will spearhead the research, with major domestic and foreign space companies and research institutions participating in joint research. As the 'New Space' era fully commences globally, the In-space Servicing and Manufacturing industry is projected to grow to tens of trillions of Korean won by 2030, driven by the reduction of launch costs and the expansion of private sector participation. This field is evaluated as a core area that will fundamentally change the way humanity engages in space activities, including extending satellite lifespan, on-orbit maintenance and operation, and securing and manufacturing resources in space. Meanwhile, an international symposium was held for two days on October 23-24 at the KAIST Academic Cultural Center and KI Building, coinciding with the opening ceremony.
<Director Jae-Hung Han of the Space Research Institute presenting>
The symposium was composed of a total of six sessions, including: ▲ Exchange Meeting on Additive Manufacturing Tecnology for Aerospace, ▲ International Workshop on Aerospace Composites, ▲ Workshop on Swarm Satellite Development, and ▲ Workshop on In-space Servicing and Manufacturing Robotics. Major domestic and foreign institutions and experts, including the Korea Aerospace Research Institute, Japan Advanced Institute of Science and Technology, and California Institute of Technology (Caltech), attended to discuss the future direction of next-generation space technology development and international cooperation measures. Cheol-woong Son, Director-General of Future Strategy Industry Office at Daejeon City, said, "We will develop the Innovative Research Center into a Daejeon-type space industry innovation platform with KAIST," and "Daejeon City will concentrate its capabilities to help local businesses grow and establish Daejeon as the central city for the Republic of Korea's space industry." Jae-Hung Han, Director of the KAIST Space Research Institute, said, "We will lead the core technologies for in-space servicing and manufacturing through cooperation between industry, academia, research institutes, and government, and contribute to the establishment of a private sector-focused industrial ecosystem," adding, "KAIST will grow into a comprehensive research hub that encompasses R&D, talent nurturing, and technology commercialization."
<Group Photo of Participants at the Opening Ceremony of the In-space Servicing and Manufacturing Research Center>
Kwang Hyung Lee, President of KAIST, said, "The field of in-space servicing and manufacturing is a core area that will change the paradigm of the future space industry," and "KAIST will lead the Republic of Korea to become the center for opening a new era of the space industry through innovative technology development and global cooperation." KAIST plans to perform the role of breaking down the boundaries between academia and industry, focusing on these technologies, and laying the foundation for next-generation space activities.
2025.10.24 View 1571 -
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 1781 -
KAIST Develops AI Technology That Predicts and Assembles Cell Drug Responses Like Lego Blocks
<(From left) Dr. Younghyun Han, (top center) Dr. Chun-Kyung Lee, (bottom center) Prof. Kwang-Hyun Cho,Ph.D. candidate Hyunjin Kim>
Controlling the state of a cell in a desired direction is one of the central challenges in life sciences, including drug development, cancer treatment, and regenerative medicine. However, identifying the right drug or genetic target for that purpose is extremely difficult. To address this, researchers at KAIST have mathematically modeled the interaction between cells and drugs in a modular “Lego block” manner—breaking them down and recombining them—to develop a new AI technology that can predict not only new cell–drug reactions never before tested but also the effects of arbitrary genetic perturbations.
KAIST (President Kwang Hyung Lee) announced on the 16th of October that a research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering has developed a generative AI-based technology capable of identifying drugs and genetic targets that can guide cells toward a desired state.
“Latent space” is an invisible mathematical map used by image-generating AI to organize the essential features of objects or cells. The research team succeeded in separating the representations of cell states and drug effects within this space and then recombining them to predict the reactions of previously untested cell–drug combinations. They further extended this principle to show that the model can also predict how a cell’s state would change when a specific gene is regulated.
The team validated this approach using real experimental data. As a result, the AI identified molecular targets capable of reverting colorectal cancer cells toward a normal-like state, which the team later confirmed through cell experiments.
This finding demonstrates that the method is not limited to cancer treatment—it serves as a general platform capable of predicting various untrained cell-state transitions and drug responses. In other words, the technology not only determines whether or not a drug works but also reveals how it functions inside the cell, making the achievement particularly meaningful.
<Latent Space Direction Vector–Based Cell Transition Modeling>
The research provides a powerful tool for designing methods to induce desired cell-state changes. It is expected to have broad applications in drug discovery, cancer therapy, and regenerative medicine, such as restoring damaged cells to a healthy state.
Professor Kwang-Hyun Cho stated, “Inspired by image-generation AI, we applied the concept of a ‘direction vector,’ an idea that allows us to transform cells in a desired direction.” He added, “This technology enables quantitative analysis of how specific drugs or genes affect cells and even predicts previously unknown reactions, making it a highly generalizable AI framework.”
The study was conducted with Dr. Younghyun Han, Ph.D. candidate Hyunjin Kim, and Dr. Chun-Kyung Lee of KAIST. The research findings were published online in Cell Systems, a journal by Cell Press, on October 15.
※ Paper title: “Identifying an Optimal Perturbation to Induce a Desired Cell State by Generative Deep Learning” (DOI: 10.1016/j.cels.2025.101405)
The study was supported by the National Research Foundation of Korea (NRF) through the Ministry of Science and ICT’s Mid-Career Researcher Program and the Basic Research Laboratory (BRL) Program.
2025.10.16 View 1307 -
Federated Learning AI Developed for Hospitals and Banks Without Personal Information Sharing
< (From bottom left) KAIST Ph.D. Candidate Yoonho Lee, Integrated M.S./Ph.D. Candidate Sein Kim, Ph.D. Candidate Sungwon Kim, Ph.D. Candidate Junseok Lee, Ph.D. Candidate Yunhak Oh, (From top right) Ph.D. Candidate Namkyeong Lee, UNC Chapel Hill Ph.D. Candidate Sukwon Yun, Emory University Professor Carl Yang, KAIST Professor Chanyoung Park >
Federated Learning was devised to solve the problem of difficulty in aggregating personal data, such as patient medical records or financial data, in one place. However, during the process where each institution optimizes the collaboratively trained AI to suit its own environment, a limitation arose: the AI became overly adapted to the specific institution's data, making it vulnerable to new data. Our university research team has presented a solution to this problem and confirmed its stable performance not only in security-critical fields like hospitals and banks but also in rapidly changing environments such as social media and online shopping.
KAIST announced on October 15th that the research team led by Professor Chanyoung Park of the Department of Industrial and Systems Engineering has developed a new learning method that fundamentally solves the chronic performance degradation problem of Federated Learning, significantly enhancing the Generalization performance of AI models.
Federated Learning is a method that allows multiple institutions to jointly train an AI without directly exchanging data. However, a problem occurs when each institution fine-tunes the resulting joint AI model to its local setting. This is because the broad knowledge acquired earlier is diluted, leading to a Local Overfitting problem where the AI becomes excessively adapted only to the data characteristics of a specific institution.
For example, if several banks jointly build a 'Collaborative Loan Review AI,' and one specific bank performs fine-tuning focusing on corporate customer data, that bank's AI becomes strong in corporate reviews but suffers from local overfitting, leading to degraded performance in reviewing individual or startup customers.
Professor Park's team introduced the Synthetic Data method to solve this. They extracted only the core and representative features from each institution's data to generate virtual data that does not contain personal information and applied this during the fine-tuning process. As a result, each institution's AI can strengthen its expertise according to its own data without sharing personal information, while maintaining the broad perspective (generalization performance) gained through collaborative learning.
<Figure 1. Federated Learning is a distributed learning method where multiple institutions collaboratively train a joint Artificial Intelligence model without directly sharing their data. Each institution trains its individual AI model using its local data (Institution 1, 2, 3 Data). Afterward, only the trained model information, not the original data, is securely aggregated to a central server to construct a high-performing 'Joint AI Model.' This method allows for the effect of training with diverse data while protecting the privacy of sensitive information>
< Figure 2. The Local Overfitting problem occurs during the process of fine-tuning the 'Joint AI Model' built through Federated Learning with each institution's data. For example, Institution 3 can fine-tune the joint AI with its own data (Type 0, 2) to create an expert AI for those types, but in the process, it forgets the knowledge about data (Type 1) that other institutions had (Information Loss). In this way, each institution's AI becomes optimized only for its own data, gradually losing the ability (generalization performance) to solve other types of problems that were obtained through collaboration. >
The research results showed that this method is particularly effective in fields where data security is crucial, such as healthcare and finance, and also demonstrated stable performance in environments where new users and products are continuously added, like social media and e-commerce. It proved that the AI could maintain stable performance without confusion even if a new institution joins the collaboration or data characteristics change rapidly.
< Figure 3. The technology proposed by the research team solves the local overfitting problem by utilizing Synthetic Data. When each institution fine-tunes its AI with its own data, it simultaneously trains with 'Global Synthetic Data' created from the data of other institutions. This synthetic data acts as a kind of 'Vaccine' to prevent the AI from forgetting information not present in the local data (e.g., Type 2 in the image), helping the AI to gain expertise on specific data while retaining a broad view (generalization performance) to handle other types of data. >
Professor Chanyoung Park of the Department of Industrial and Systems Engineering said, "This research opens a new path to simultaneously ensure both expertise and versatility for each institution's AI while protecting data privacy," and "It will be a great help in fields where data collaboration is essential but security is important, such as medical AI and financial fraud detection AI."
The research was primarily authored by Graduate School of Data Science student Sungwon Kim and co-authored by Professor Chanyoung Park as the corresponding author. It was recognized for its excellence by being selected for an Oral Presentation, which is reserved for the top 1.8% of outstanding papers, at the International Conference on Learning Representations (ICLR) 2025, a top-tier academic conference in the field of Artificial Intelligence held in Singapore last April.
※ Paper Title: Subgraph Federated Learning for Local Generalization, https://doi.org/10.48550/arXiv.2503.03995
Meanwhile, this research is a result of projects supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) — the 'Robust, Fair, and Scalable Data-Centric Continual Learning' project, the National Research Foundation of Korea (NRF) — the 'Graph Foundation Model: Graph-based Machine Learning Applicable to Various Modalities and Domains' project, and the 'Data Science Convergence Talent Fostering Program.'
2025.10.15 View 975 -
KAIST Develops Multimodal AI That Understands Text and Images Like Humans
<(From Left) M.S candidate Soyoung Choi, Ph.D candidate Seong-Hyeon Hwang, Professor Steven Euijong Whang>
Just as human eyes tend to focus on pictures before reading accompanying text, multimodal artificial intelligence (AI)—which processes multiple types of sensory data at once—also tends to depend more heavily on certain types of data. KAIST researchers have now developed a new multimodal AI training technology that enables models to recognize both text and images evenly, enabling far more accurate predictions.
KAIST (President Kwang Hyung Lee) announced on the 14th that a research team led by Professor Steven Euijong Whang from the School of Electrical Engineering has developed a novel data augmentation method that enables multimodal AI systems—those that must process multiple data types simultaneously—to make balanced use of all input data.
Multimodal AI combines various forms of information, such as text and video, to make judgments. However, AI models often show a tendency to rely excessively on one particular type of data, resulting in degraded prediction performance.
To solve this problem, the research team deliberately trained AI models using mismatched or incongruent data pairs. By doing so, the model learned to rely on all modalities—text, images, and even audio—in a balanced way, regardless of context.
The team further improved performance stability by incorporating a training strategy that compensates for low-quality data while emphasizing more challenging examples. The method is not tied to any specific model architecture and can be easily applied to various data types, making it highly scalable and practical.
<Model Prediction Changes with a Data-Centric Multimodal AI Training Framework>
Professor Steven Euijong Whang explained, “Improving AI performance is not just about changing model architectures or algorithms—it’s much more important how we design and use the data for training.” He continued, “This research demonstrates that designing and refining the data itself can be an effective approach to help multimodal AI utilize information more evenly, without becoming biased toward a specific modality such as images or text.”
The study was co-led by doctoral student Seong-Hyeon Hwang and master’s student Soyoung Choi, with Professor Steven Euijong Whang serving as the corresponding author. The results will be presented at NeurIPS 2025 (Conference on Neural Information Processing Systems), the world’s premier conference in the field of AI, which will be held this December in San Diego, USA, and Mexico City, Mexico.
※ Paper title: “MIDAS: Misalignment-based Data Augmentation Strategy for Imbalanced Multimodal Learning,” Original paper: https://arxiv.org/pdf/2509.25831
The research was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) under the projects “Robust, Fair, and Scalable Data-Centric Continual Learning” (RS-2022-II220157) and “AI Technology for Non-Invasive Near-Infrared-Based Diagnosis and Treatment of Brain Disorders” (RS-2024-00444862).
2025.10.14 View 1799 -
KAIST VIRNECT Sign Agreement to Establish Virtual Convergence Research Center
< (From left) Tae Jin Ha, CEO of VIRNECT, Kwang Hyung Lee, President of KAIST >
An open platform for industry-academia-research collaboration, which has accumulated K-Metaverse technology capabilities that break down the boundaries between reality and virtuality and share experiences beyond the limits of time and space, is expected to be built on our university campus.
Our university announced on the 13th that it’s signing an agreement for the establishment and operation of a 'Virtual Convergence Research Center' with the Graduate School of Metaverse and VIRNECT Co., Ltd. (CEO Tae Jin Ha), a domestic augmented/virtual reality (XR) specialized company and a startup founded by a KAIST alumnus.
The Virtual Convergence Research Center, which will be newly constructed on our university campus, plans to prepare for the future participation of related government-funded research institutes, and is expected to function as a national strategic hub that creates future growth engines for the Republic of Korea, going beyond simple industry-academia cooperation. VIRNECT Co., Ltd. plans to create the research center as an open research collaboration platform in which domestic and international industry, academia, and research institutes jointly participate with KAIST.
This research center is expected to experiment with the convergence of reality and virtuality and establish itself as a global hub for the 'K-Metaverse Innovation Ecosystem' where technology development, talent cultivation, and industrial diffusion are in a virtuous cycle.
VIRNECT Co., Ltd. was founded by KAIST alumnus Tae Jin Ha, listed on KOSDAQ in 2023, and won the CES Innovation Award for developing the industrial AI smart goggles 'VisionX'. It has grown into a representative domestic spatial computing company based on various industrial innovation technologies such as AI/XR solutions and digital twin. Synergistic co-prosperity with KAIST is anticipated through this collaboration.
Spatial computing and XR technology are areas where global big techs like Apple, Meta, Google, Microsoft, and Samsung are engaged in fierce competition for dominance, paying attention to them as the next-generation AI platforms. With major countries such as the US and China investing enormous capital and capabilities, the launch of the KAIST Virtual Convergence Research Center is evaluated as a strategic response for South Korea not to fall behind in the competition of the post-metaverse era.
The research center plans to lead both industrial productivity and social innovation as an R&BD (Research & Business Development) hub that integrates core technologies such as digital twin, metaverse, spatial/physical intelligence, and wearable XR. Furthermore, it will quickly verify the applicability to industrial sites and support the creation of new industries through a full-cycle system covering education, research, demonstration, commercialization, and diffusion.
Moreover, the research center will create national synergy by being closely linked with government policies. Strengthening the link between education and research, fostering a sustainable metaverse ecosystem, and expanding global leadership through an open industry-academia-research platform align with the government's strategy for advancing the virtual convergence industry.
< Executives from both organizations attending the signing ceremony >
VIRNECT CEO Tae Jin Ha said, "The long-term cooperation with KAIST is a stepping stone for us to leap forward as a game-changer in the global XR industry," adding, "We will strengthen virtual convergence technology competitiveness through research and education infrastructure and accelerate commercialization through demonstration."
Professor Woontack Woo, Dean of the Graduate School of Metaverse, emphasized, "The Virtual Convergence Research Center will serve as an open platform where industry, academia, and research institutes jointly experiment with K-Metaverse innovation, and a 'Meta Power Plant' that cultivates future core personnel and disseminates research results to the industry."
KAIST President Kwang Hyung Lee said, "This agreement is a strategic investment to secure global leadership by breaking down the boundaries between research and industry, going beyond simply creating a new research center," and "KAIST will spare no support for the research center."
With the future designation of the KAIST Virtual Convergence Research Center as a government-specialized graduate school/research center for the virtual convergence industry and increased industry cooperation, it will establish itself as a national innovation platform that concentrates South Korea's metaverse capabilities. This is expected to lead to the creation of new value for the future society and the strengthening of national competitiveness, going beyond simple technology development.
2025.10.13 View 778 -
AI Nüshu Wins International Award
< (From left) Dr. Yuqian Sun, Professor Chang-Hee Lee of the Department of Industrial Design, and Ali Asadipour, Director of CSRC at the Royal College of Art >
'Nüshu (女書)' is the world's only women's script, a unique writing system created autonomously by women in Hunan Province, China, starting around the 19th century. These women, excluded from Hanzi education, used it to record their lives and communicate with each other. A research team from KAIST participated in the 'AI Nüshu (女书)' project, which combines the script's significance (creation amidst oppression, female solidarity, linguistic experimentation) with modern technology, winning a prestigious international award often called the 'Academy Award of the media art world.'
KAIST announced on the 10th that the 'AI Nüshu' project, jointly conducted by Professor Chang-Hee Lee's research team from the Department of Industrial Design and Ali Asadipour, Director of the Computer Science Research Center at the Royal College of Art (RCA), was selected for the Honorary Mention in the Digital Humanity category at the 'Prix Ars Electronica 2025,' the world's highest-level media art festival.
< Installation image of 'AI Nüshu' >
The 'Prix Ars Electronica,' known as the 'Academy Award of the media art world,' is the premier international media art competition held annually in Linz, Austria. This competition, which discovers innovative works spanning the boundaries of art and science, saw 3,987 submissions from 98 countries this year, with only two works receiving the honor in the Digital Humanity category.
The award-winning work, 'AI Nüshu (女书),' is based on 'Nüshu,' the world's only women's script created by Chinese women who were excluded from literacy education to record and communicate their lives.
The KAIST research team and collaborators combined this script with Computational Linguistics to create an installation that visitors can directly experience.
The artificial intelligence within the artwork learns the communication methods of pre-modern Chinese women and generates its own new language. This is regarded as a symbol of resistance against the patriarchal order and a feminist endeavor that moves beyond Western-centric views on language.
< Example of the same sentence expressed in English, Chinese, Nüshu, and AI Nüshu >
It also received high praise for artistically presenting the possibility of machines creating new languages, going beyond the preconception that 'only humans create language.'
Dr. Yuqian Sun of the Royal College of Art expressed her feelings, saying, "Although there were many difficulties in my life and research process, I feel great reward and emotion through this award."
Professor Chang-Hee Lee of the KAIST Department of Industrial Design stated, "It is very meaningful that this contemplative art, born from the intersection of history, humanities, art, and technology, has led to such a globally prestigious award."
Detailed information about the project can be found on the official Prix Ars Electronica website (https://ars.electronica.art/prix/en/digitalhumanity/).
2025.10.13 View 1076 -
Physics Informed AI Excels at Large Scale Discovery of New Materials!
<(From left) Ph.D candidates Songho Lee, Donggeun Park, and Hyeonbin Moon, and Professor Seunghwa Ryu from the Department of Mechanical Engineering; (top) Professor Jae Hyuk Lim from Kyung Hee University and Dr. Wabi Demeke from KAIST>
One of the key steps in developing new materials is “property identification,” which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A KAIST research team has introduced a new technique that combines “physical laws,” which govern deformation and interaction of materials and energy, with artificial intelligence. This approach allows for rapid exploration of new materials even under data-scarce conditions and provides a foundation for accelerating design and verification across multiple engineering fields, including materials, mechanics, energy, and electronics.
KAIST (President Kwang Hyung Lee) announced on the 2nd of October that Professor Seunghwa Ryu’s research group in the Department of Mechanical Engineering, in collaboration with Professor Jae Hyuk Lim’s group at Kyung Hee University (President Jinsang Kim) and Dr. Byungki Ryu at the Korea Electrotechnology Research Institute (President Namkyun Kim), proposed a new method that can accurately determine material properties with only limited data. The method uses Physics-Informed Machine Learning (PIML), which directly incorporates physical laws into the AI learning process.
<Schematic Diagram of a Physics-Based Machine Learning Methodology for Understanding Material Properties>
In the first study, the researchers focused on hyperelastic materials, such as rubber. They presented a Physics-Informed Neural Network (PINN) method that can identify both the deformation behavior and the properties of materials using only a small amount of data obtained from a single experiment. Whereas previous approaches required large, complex datasets, this research demonstrated that material characteristics can be reliably reproduced even when data is scarce, limited, or noisy.
In the second study, the team turned to thermoelectric materials—new materials that convert heat into electricity and electricity into heat. They proposed a PINN-based inverse inference technique that can estimate key indicators, such as thermal conductivity (how well heat is transferred) and the Seebeck coefficient (how efficiently electricity is generated), from just a few measurements.
Going further, the researchers introduced a Physics-Informed Neural Operator (PINO), an AI model that understands the physical laws of nature, and showed that it can generalize to previously unseen materials without requiring retraining.
In fact, after training the system on 20 materials, they tested it on 60 entirely new materials, and in all cases it predicted their properties with high accuracy. This breakthrough points to a future where large-scale, high-speed screening of countless candidate materials becomes possible.
This achievement goes beyond simply reducing the need for experiments. By intricately combining physical laws with AI, the researchers provided the first example of improving experimental efficiency while preserving reliability.
Professor Seunghwa Ryu, who led both studies, stated, “This is the first case of applying AI that understands physical laws to real material research. It enables reliable identification of material properties even when data availability is limited, and it is expected to expand into various engineering fields.”
The first paper, co-first-authored by KAIST Mechanical Engineering PhD candidates Hyeonbin Moon and Donggeun Park, was published on August 13 in Computer Methods in Applied Mechanics and Engineering.
※ Paper title: “Physics-informed neural network-based discovery of hyperelastic constitutive models from extremely scarce data”
※ DOI: https://doi.org/10.1016/j.cma.2025.118258
The second paper, co-first-authored by KAIST Mechanical Engineering PhD candidates Hyeonbin Moon and Songho Lee, and Dr. Wabi Demeke, was published on August 22 in npj Computational Materials.
※ Paper title: “Physics-informed neural operators for generalizable and label-free inference of temperature-dependent thermoelectric properties” ※ DOI: https://doi.org/10.1038/s41524-025-01769-1
Meanwhile, the first study was supported by the Korea Research Foundation and the Ministry of Science and ICT’s INNOCore Program, as well as by a research project from the Ministry of Food and Drug Safety. The second study was carried out with support from the Korea Research Foundation and the Ministry of Science and ICT’s INNOCore Program.
2025.10.10 View 1012