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Student Entrepreneur Inseo Chung Donates 1 Billion Won to Foster Inclusive AI Talent
< Photo of the Donation Agreement Ceremony >
KAIST announced on March 11th that Inseo Chung (28), an undergraduate student in the School of Interdisciplinary Studies and CEO of the global music-tech startup MPAG, donated 1 billion won in development funds on the 10th to foster ‘Inclusive AI’ talent. Inclusive AI talent refers to experts who research and develop AI technologies so that the socially vulnerable, including people with disabilities and the technologically marginalized, can also enjoy the benefits of AI technology.
Inseo Chung is a student entrepreneur who has dedicated himself to “solving social problems through technology” while balancing startup activities and research during his studies. Alongside his academic advisor, Professor Hyunwook Ka of the School of Interdisciplinary Studies, he has consistently researched how technology can embrace those who are marginalized.
His research, including software for the hearing impaired, studies for users requiring linguistic support in media, and bidirectional assistive technology devices for the visually and hearing impaired, has garnered attention at domestic and international conferences. This work has also led to several patent applications filed under the name of KAIST.
He founded the global music-tech startup MPAG, which operates a sheet music sales platform and AI music education service with over 4 million members worldwide, and is also developing features to provide braille sheet music for the visually impaired.
The donation will be used to establish a Master’s and Doctoral Education & Research Program in ‘AI-based Assistive Technology’ for the disabled and the technologically and socially vulnerable within the newly established KAIST AI College. This program aims to conduct research on AI-based rehabilitation assistive technology, nurture Master’s and Doctoral-level experts in the field, and build an inclusive technology ecosystem. Professor Hyunwook Ka, an expert in this field, will lead the operation and guidance of the degree program to ensure research continuity and expertise.
Inseo Chung emphasized, “As AI technology advances exponentially, it is absolutely necessary to expand into ‘Inclusive AI’ so that its benefits reach the disabled and the technologically marginalized. I am confident that through a formal graduate program, the number of experts in this field will grow, and KAIST’s specialized AI research capabilities will serve as the catalyst.”
This is not Inseo Chung’s first donation. He previously donated through the KAIST Development Foundation in 2024 and 2025 before contributing an additional 1 billion won this year. The 2024 donation was used to create the ‘Creative Workshop’ for junior students in the School of Interdisciplinary Studies to realize their creative ideas, and the 2025 donation was allocated to the School of Computing.
President Kwang Hyung Lee stated, “The decision by student Inseo Chung to donate the fruits of his startup efforts for the future of his alma mater and the realization of social values serves as a great inspiration to all members of KAIST. We will do our best to nurture inclusive AI talent so that the benefits of technology can spread throughout society, honoring the donor’s intent.”
2026.03.11 View 198 -
Secret to Drug Addiction Relapse Found: Brain's Addiction Circuit Identified
<(From Left) Dr. Minju Jeong,(UCSD), Prof. Byung Kook Lim (UCSD), Prof. Se-Bum Paik (KAIST)>
Drug addiction carries an extremely high risk of relapse, as cravings can be reignited by minor stimuli even long after one has stopped using. Previously, this phenomenon was attributed to a decline in the function of the prefrontal cortex (PFC), which regulates impulses. However, a joint international research team has recently revealed that the cause of addiction relapse is not a simple decline in brain function, but rather an imbalance in specific neural circuits.
KAIST announced on March 9th that a research team led by Prof. Se-Bum Paik from the Department of Brain and Cognitive Sciences and Prof. Byung Kook Lim from the University of California, San Diego (UCSD) has identified the core principle by which specific inhibitory neurons in the prefrontal cortex regulate cocaine-seeking behavior.
In particular, the research team focused on parvalbumin-positive (PV) inhibitory neurons, which regulate the balance of neural signals by suppressing the activity of other neurons in the brain. They confirmed that these cells act as a "brake gate" that controls excitatory signals in the brain and serve as a crucial factor in determining drug-seeking behavior that emerges after withdrawal.
The prefrontal cortex (PFC) of our brain can properly perform its "braking" function to suppress impulses when excitatory and inhibitory signals are in balance. To investigate how chronic drug exposure disrupts this balance, the research team conducted cocaine administration experiments on mice. During this process, they tracked when inhibitory neurons in the PFC were activated and how they sent signals to downstream brain regions.
The experimental results showed that parvalbumin (PV) cells, which account for about 60-70% of the inhibitory neurons in the PFC, were highly active when the mice attempted to seek cocaine. However, when "extinction training"—training to stop seeking the drug—was conducted, the activity of these cells significantly decreased. This demonstrates that the activity patterns of PV cells are not permanently fixed by addiction but can be readjusted through the extinction process.
<Figure 1. Experimental design illustrating cocaine self-administration and longitudinal tracking of prefrontal cortical neural activity during cocaine-seeking behavior>
The research team confirmed that artificially suppressing PV cell activity significantly reduced cocaine-seeking behavior in mice. Conversely, activating these cells caused the drug-seeking behavior to persist even after the extinction process. This effect was specifically observed in drug-addiction behavior and did not appear with general rewards like sugar water. Furthermore, this phenomenon was not observed in somatostatin (SOM) cells—another type of inhibitory neuron—indicating that PV cells selectively regulate drug addiction behavior.
<Figure 2. Comparison of single-neuron activity, population activity patterns, and behavioral modulation of prefrontal inhibitory neurons across different stages of cocaine-seeking behavior>
The team also identified the specific brain circuit through which these PV cells operate. Signals originating from the prefrontal cortex are transmitted to the reward circuit of the Ventral Tegmental Area (VTA), a key brain region related to reward. This pathway emerged as the central channel for regulating addiction behavior, determining whether or not to seek the drug again. In this process, PV neurons act as a "regulatory switch," controlling the flow of signals to influence dopamine signaling and deciding whether to maintain or suppress addictive behavior.
In short, the study revealed that addiction relapse is not due to an overall functional decline of the prefrontal cortex, but is determined by whether PV neurons regulate the neural pathway connecting the PFC to the reward circuit.
<Figure 3. Schematic illustrating the prefrontal–reward circuit mechanism that determines drug-seeking behavior>
Prof. Se-Bum Paik stated, "This research shows that drug addiction is a circuit-level problem arising from a collapse in the regulatory balance of specific neurons and downstream neural circuits. The discovery that parvalbumin (PV) cells act as a 'gate' for addictive behavior will provide a crucial lead for developing precision-targeted treatment strategies in the future."
This study was led by Dr. Minju Jeong (UCSD) as the first author, with Prof. Byung Kook Lim (UCSD) and Prof. Se-Bum Paik (KAIST) serving as co-corresponding authors. The findings were published online on February 26 in Neuron, a premier journal in the field of neuroscience.
Paper Title: Distinct Interneuronal Dynamics Selectively Gate Target-Specific Cortical Projections in Drug Seeking
DOI: 10.1016/j.neuron.2026.01.002
Full Author List: Minju Jeong, Seungdae Baek, Qingdi Wang, Li Yao, Eun Ji Lee, Arturo Marroquin Rivera, Joann Jocelynn Lee, Hyeonseok Jang, Dhananjay Bambah-Mukku, Christine Hyun-Seung Mun, Tyler Boesen, Sumit Nanda, Cheol Ryong Ku, Hong-wei Dong, Benoit Labonté, Se-Bum Paik, and Byung Kook Lim.
This research was conducted with the support of the Basic Research Program in Science and Engineering of the National Research Foundation of Korea.
2026.03.10 View 260 -
KAIST Team Led by Dong-won Lee Wins Grand Prize at the 2nd Global Quantum AI Competition
< (From Left) M.S candidate Dongwon Lee from School of Electrical Engineering, Ph.D candidate Jaehun Han from Graduate School of Quantum Science and Technology >
"Team Yangja-jorim," consisting of Dongwon Lee, Gyungjun Kim and Jaehun Han , has been honored with the Grand Prize at the '2026 2nd Global Quantum AI Competition.' The event was hosted and organized by NORMA, a specialized quantum computing company.
This global competition was designed to expand hands-on experience with quantum cloud services and to discover next-generation talent in the field of quantum artificial intelligence. The event spanned approximately 70 days, beginning with the preliminary opening ceremony held at Korea University’s Hana Square on December 17 last year. The final winners were announced during an awards ceremony held at NORMA's headquarters on the 27th of last month.
The competition attracted significant interest from quantum technology talent worldwide, including university students, developers, and researchers. A total of 137 teams participated in the preliminaries, with the top 10 teams advancing to the finals—a competitive ratio of approximately 13.7 to 1.
< An acquaintance attended the awards ceremony of the 2nd Global Quantum AI Competition to accept the prize on behalf of the team. >
In the final round, participants were presented with four generative problems utilizing the Quantum Circuit Born Machine (QCBM) model. To overcome the current limitations of quantum machine learning, the contestants were tasked with designing and validating Quantum-Classical Hybrid Generative AI models that integrate classical techniques. Notably, the final problem provided an opportunity to verify the proposed methods using a real Quantum Processing Unit (QPU) from Rigetti Computing, a leading global quantum computing firm.
The judging process employed a double-blind system, where the identities of both evaluators and participants remained undisclosed to ensure maximum fairness and credibility.
"Through this competition, we were able to explore the research potential of the quantum AI field more deeply," said KAIST's Team Yangja-jorim in their acceptance speech. "We hope to continue contributing to the advancement of quantum technology through consistent research and new challenges."
2026.03.10 View 201 -
KAIST Develops mRNA Platform That Remains Effective Even in Aging and Obesity
<(From Left) Dr. Subin Yoon, Ph.D candidate Hyeonggon Cho, Prof. Jae-Hwan Nam, Prof. Young-suk Lee>
Since the COVID-19 pandemic, mRNA vaccines have gained attention as a next-generation pharmaceutical technology. mRNA therapeutics work by delivering genetic instructions that enable cells to produce specific proteins for therapeutic effects. However, their efficacy has been reported to decline in elderly individuals or patients with obesity. To address this limitation, Korean researchers have newly designed a key regulatory region of mRNA that improves therapeutic protein production efficiency, developing a next-generation mRNA platform that maintains effectiveness even in aging and obesity conditions.
KAIST (President Kwang Hyung Lee) announced on the 10th of March that a joint research team led by Professor Young-suk Lee of the Department of Bio and Brain Engineering and Professor Jae-Hwan Nam of The Catholic University of Korea (President Jun-Gyu Choi) has developed a new mRNA platform by precisely designing the sequence of the 5′ untranslated region (5′UTR)*, a key regulatory region of mRNA.*5′ untranslated region (5′UTR): A region of mRNA that initiates and regulates protein production. The design of this region influences both the amount and speed of protein synthesis.
The research team analyzed large-scale bioinformatics datasets to identify 5′UTR sequences that enable proteins to be produced more efficiently across diverse cellular environments. When applied, the designed sequences significantly enhanced protein production and immune responses even in preclinical models of aging and obesity.
mRNA is a long single-stranded RNA molecule that serves as the blueprint for producing proteins required by the body. It consists of several components: the 5′UTR, which initiates and regulates the rate of protein production; the coding sequence (CDS), which contains the genetic information for a specific protein; the 3′ untranslated region (3′UTR), which helps maintain mRNA stability within cells; and the poly(A) tail, which further enhances stability and supports protein synthesis.
Among these components, the 5′UTR and 3′UTR do not determine the type of protein produced, but they play a critical role in regulating how efficiently the protein is synthesized. For this reason, these regions are receiving increasing attention as key bioengineering platforms for improving the performance of various mRNA therapeutics, including vaccines and treatments.
<Schematic Diagram of mRNA Therapeutic Design and Validation Using Bioinformatics>
To identify highly efficient 5′UTR sequences capable of promoting protein production across multiple tissues and cellular environments, the team conducted an integrated analysis of large-scale biological datasets. This included multiple analytical approaches such as RNA sequencing (RNA-seq) for analyzing gene activity across tissues, single-cell RNA sequencing (scRNA-seq) for examining gene expression at the individual cell level, and ribosome profiling (Ribo-seq) for measuring actual protein translation efficiency.
The researchers also focused on the fact that in aging or obesity conditions, cells often experience high levels of stress—particularly oxidative stress—which can reduce their ability to synthesize proteins. When the newly designed mRNA therapeutics were applied to preclinical models of aging and obesity, the results showed significantly improved protein production and immune responses compared with existing approaches. This research is expected to be applicable not only to mRNA vaccines but also to a wide range of biopharmaceutical technologies, including gene therapies and immunotherapies.
<Multimodal Bio–Big Data Analysis–Based mRNA Therapeutic Design (AI-Generated Image)>
Professor Young-suk Lee of KAIST Department of Bio and Brain Engineering stated, “This study identified a design strategy that enables mRNA to produce proteins more efficiently by analyzing large-scale biological data,” adding, “This technology will provide an important foundation for ensuring that mRNA vaccines and therapeutics remain effective even in environments where drug efficacy may decline, such as in elderly or obese patients.”
In this study, Dr. Subin Yoon from The Catholic University of Korea and doctoral candidate Hyeonggon Cho from KAIST participated as co-first authors. The research findings were published online on January 2 in the internationally renowned journal Molecular Therapy (IF = 12.0), a leading journal in gene and cell therapy.
(Paper title: ”Designing 5′UTR sequences improves the capacity of mRNA therapeutics in preclinical models of aging and obesity” DOI: https://doi.org/10.1016/j.ymthe.2025.12.060)
This research was supported by the Excellent Young Researcher Program and the Bio-Medical Technology Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, the Infectious Disease Response Innovative Technology Support Program of the Ministry of Food and Drug Safety, and the Infectious Disease Prevention and Therapeutics Technology Development Program of the Korea Health Industry Development Institute.
2026.03.10 View 257 -
KAIST Surpasses the Limits of AlphaFold… AI Now Predicts Whether Drugs Actually Work
<(From Left) Ph.D candidate Hyojin Son, Professor Gwan-su Yi>
Proteins in our body function like switches. When a drug binds to a protein, the structure at the binding site changes, and this structural change propagates throughout the protein, turning its function on or off. Google DeepMind’s AlphaFold3 successfully predicted whether drugs bind to proteins and the three-dimensional structure of binding sites. However, it could not predict how signals propagate inside the protein after drug binding, how the entire structure changes, or whether the protein’s function is ultimately activated or inhibited. KAIST researchers have developed an AI that predicts not only whether a drug binds but whether it actually works.
KAIST (President Kwang Hyung Lee) announced on the 4th of March that a research team led by Professor Gwan-Su Yi of the Department of Bio and Brain Engineering has developed an artificial intelligence model called “GPCRact” that predicts whether candidate molecules not only bind to G-protein-coupled receptors (GPCRs)—a major drug target—but also actually activate the protein.
GPCRs act as “signal receivers” on the surface of cells. When hormones, neurotransmitters, or drugs send signals from outside the cell, GPCRs function as gates that receive these signals and transmit them into the cell. There are about 800 types of GPCRs in the human body, and roughly 30–40% of currently marketed drugs target them. They are key proteins involved in numerous physiological functions, including heart rate regulation, blood pressure control, pain sensing, immune responses, and emotional regulation.
However, a drug binding to a GPCR does not always trigger the desired biological function. Structural changes inside the protein and subsequent signal transmission determine whether the drug actually produces an effect. This process is known as allosteric signal propagation.
The research team designed the AI to learn the drug action process in two stages: ① the drug–target binding stage, and ② the intracellular signal propagation stage within the protein. The three-dimensional protein structure was represented as an atom-level graph, and an attention mechanism was applied to enable the model to learn important signaling pathways. Through this approach, the AI analyzes not only the drug binding signal but also the internal signaling pathways of the protein to predict whether the protein becomes activated.
As a result, the model significantly improved the prediction performance of drug activity even in proteins with complex structures that existing models struggled to analyze. Importantly, the model does not simply output “active” or “inactive.” It also presents the key internal signaling pathways that form the basis of its predictions, overcoming the limitations of so-called “black-box AI.”
<Schematic diagram of drug activity prediction and mechanism interpretation using the GPCRact artificial intelligence model>
This represents an important advance, as it allows researchers to interpret and verify predictions while simultaneously improving the reliability and efficiency of drug discovery. In the future, the model is expected to serve as a precision drug discovery AI platform capable of predicting not only whether drugs bind to GPCRs but also whether they truly activate them in various diseases targeting GPCRs.
<AI-generated image to help illustrate the research>
Professor Gwan-Su Yi explained, “Allosteric structural change refers to a phenomenon in which a drug binds to one part of a protein and its influence propagates internally, altering the function of other regions,” adding, “The key contribution of this research is incorporating this operational principle into deep learning.” He further noted, “We plan to expand the model to various proteins and ultimately develop technologies capable of predicting cellular and whole-body responses.”
Ph.D candidate Hyojin Son participated as the first author in this study. The paper was published on January 15 in the international journal Briefings in Bioinformatics, one of the leading journals in the field of bioinformatics.
※ Paper title: “GPCRact: a hierarchical framework for predicting ligand-induced GPCR activity via allosteric communication modeling”
DOI: https://doi.org/10.1093/bib/bbaf719※ Author information: Hyojin Son (KAIST, first author), Gwan-Su Yi (KAIST, corresponding author)
This research was supported by the Basic Research Program for Individual Research funded by the Ministry of Science and ICT and the National Research Foundation of Korea (RS-2025-24533057).
2026.03.09 View 360 -
AI Developed to Locate Slums Worldwide... Wins Best Paper Award at AAAI 2026
<(From Left) Sumin Lee, Sungwon Park, Prof. Jihee Kim, Prof. Meeyoung Cha, Prof. Jeasurk Yang>
"Cities don't even know where their slums (impoverished areas) are located."
In many developing nations, the most vulnerable citizens are invisible to the state simply because their homes don't appear on any official map. Today, a breakthrough using Artificial Intelligence (AI) is changing that.
A joint research team from KAIST and Chonnam National University in South Korea and MPI-SP in Germany has developed an AI technology that autonomously identifies slum areas using nothing but satellite imagery. This technology is expected to fundamentally transform urban policy-making and public resource allocation in developing countries where data is scarce and has won the Best Paper Award in the ‘AI for Social Impact’ category at the AAAI 2026 (Association for the Advancement of Artificial Intelligence), the world's premiermost prestigious AI academic conference.
Why it Matters
While previous studies struggle to recognize slums across countries due to varying architectural styles, the team introduced a "Mixture-of-Experts (MoE)" structure. In this system, multiple AI models learn different regional characteristics; when a new city is inputted, the system automatically selects the most appropriate model.
<Figure1. Overview of the Mixture-of-Experts(MoE) structure to identify slum areas>
The core of this research is "Test-Time Adaptation (TTA)" technology. Even if humans do not pre-mark slum locations in a new city, the AI reduces its own errors by comparing and verifying the prediction results of multiple models, trusting only the areas where they commonly agree. This ensures stable performance even in regions with insufficient data.
The research team applied this technology to major cities such as Kampala (Uganda) and Maputo (Mozambique) and confirmed that it distinguishes slum areas more precisely than existing state-of-the-art technologies.
This technology is expected to be utilized in various policy fields, including:
Establishing urban infrastructure expansion plans for developing countries.
Identifying areas vulnerable to disasters and infectious diseases in advance.
Selecting targets for housing environment improvement projects.
Monitoring the implementation of UN Sustainable Development Goals (SDGs).
<Figure2. Slum segmentation results in Kampala in 2015 (yellow) and 2023 (red). Over the eight-year period, the slum ratio in the city increased from 8.4% to 8.6%>
Meeyoung Cha, an AI researcher and author, stated, "This research proves that AI is no longer just a tool for analysis. It is a tool for action. Our technology can bridge the data gap to solve the world’s most pressing social challenges." Jihee Kim, an economist and author, added, "It will complement costly field surveys and help effectively allocate limited resources to the areas that need them most."
The research results were presented at AAAI 2026 in Singapore on January 25th.
Paper Title: Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts
Paper Link: https://aaai.org/about-aaai/aaai-awards/aaai-conference-paper-awards-and-recognition/
This research was supported by the National Research Foundation of Korea (NRF) through the Mid-career Researcher Support Program and the Data Science Convergence Human Resources Training Program.
2026.03.08 View 214 -
KAIST Develops Brain-Like AI… Thinks One More Time Even When Predictions Are Wrong
<(From left) Professor Sang Wan Lee, Myoung Hoon Ha, and Dr. Yoondo Sung>
Artificial intelligence now plays Go, paints pictures, and even converses like a human. However, there remains a decisive difference: AI requires far more electricity than the human brain to operate. Scientists have long asked the question, “How can the brain learn so intelligently using so little energy?” KAIST researchers have moved one step closer to the answer.
KAIST (President Kwang Hyung Lee) announced on the 29th that a research team led by Distinguished Professor Sang Wan Lee of the Department of Brain and Cognitive Sciences has developed a new technology that applies the learning principles of the human brain to deep learning, enabling stable training even in deep artificial intelligence models.
Our brain does not passively receive the world. Instead of merely perceiving what is happening in the present, it first predicts what will happen next and, when reality differs from that prediction, adjusts itself to reduce the difference (i.e., prediction error). This is similar to anticipating an opponent’s next move in Go and changing strategy if the prediction turns out to be wrong. This mode of information processing is known as “Predictive Coding.”
< Predictive Coding (PC) Module >
Scientists have attempted to apply this principle to AI, but encountered difficulties. As neural networks become deeper, errors tend to concentrate in specific layers or vanish altogether, repeatedly leading to performance degradation.
The research team mathematically identified the cause of this problem and proposed a new solution. The key idea is simple: instead of predicting only the final outcome, the AI is designed to also predict how its prediction errors will change in the future. The team refers to this as “Meta Prediction.” In simple terms, it is an AI that “thinks once more about its mistakes.” When this method was applied, learning proceeded stably in deep neural networks without halting.
<Analysis of Instability in Predictive Coding Model Errors>
The experimental results were also impressive. In 29 out of 30 experiments, the proposed method achieved higher accuracy than the current standard AI training method, backpropagation. Backpropagation is the representative learning method in which AI “goes backward by the amount of error and corrects it.”
Conventional AI training methods (backpropagation) require tightly interconnected layers, meaning the entire network must be computed and updated simultaneously. In contrast, this new approach demonstrates that, like the brain, large AI models can be effectively trained even when learning occurs in a distributed and partially independent manner.
<Performance Comparison of Predictive Coding Models>
This technology is expected to expand into various fields where power efficiency is critical, including neuromorphic computing, robot AI that must adapt to changing environments, and edge AI operating within devices.
Distinguished Professor Sang Wan Lee stated, “The key to this research is not simply imitating the structure of the brain, but enabling AI to follow the brain’s learning principles themselves,” adding, “We have opened the possibility of artificial intelligence that learns efficiently like the brain.”
This study was conducted with Dr. Myoung Hoon Ha as the first author and Professor Sang Wan Lee as the corresponding author. The paper was accepted to the International Conference on Learning Representations (ICLR 2026) and was published online on January 26.
※ Paper title: “Stable and Scalable Deep Predictive Coding Networks with Meta Prediction Errors”Original paper: https://openreview.net/forum?id=kE5jJUHl9i¬eId=e6T5T9cYqO
This research was supported by the Ministry of Science and ICT and the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the Digital Global Research Support Program (joint research with Microsoft Research), the Samsung Electronics SAIT NPRC Program, and the SW Star Lab Program.
2026.03.06 View 566 -
Designing the Heart of Hydrogen Cars with AI... Development of Next-Generation Super Catalyst
<(From left) KAIST Ph.D. Candidate HyunWoo Chang, Professor EunAe Cho. (Top, from left) Seoul National University Professor Won Bo Lee, Dr. Jae Hyun Ryu.>
In the era of climate crisis, hydrogen vehicles are emerging as an alternative for eco-friendly mobility. However, the fuel cell, known as the ‘heart of the hydrogen car,’ still faces limitations of high cost and short lifespan. The core cause is the platinum catalyst. While it is a decisive material for generating electricity, the reaction is slow, performance degrades over time, and manufacturing costs are high. Korean researchers have presented a clue to solving this difficult problem.
KAIST announced on February 26th that the research team led by Professor EunAe Cho of the Department of Materials Science and Engineering, together with the team of Professor Won Bo Lee of the School of Chemical and Biological Engineering at Seoul National University, has developed a technology that predicts the ‘atomic arrangement’ tendency of catalysts using artificial intelligence (AI).
This technology is akin to calculating beforehand which combination is advantageous for completing a puzzle before putting it together. By having AI calculate the arrangement speed of metal atoms first, it has become possible to efficiently design catalysts with better performance. The core of this research is that ‘AI revealed the fact that zinc plays a decisive role in the platinum-cobalt atomic arrangement.’
<Schematic diagram of AI-based atomic alignment prediction>
Despite the high performance of existing platinum-cobalt (Pt-Co) alloy catalysts, very high-temperature heat treatment was required to create the ‘intermetallic (L1₀)’ structure, where atoms are regularly arranged. In this process, particles would clump together, or the structure would become unstable, posing limitations for actual fuel cell application.
To solve this problem, the research team introduced machine learning-based quantum chemistry simulations. Through AI, they precisely predicted how atoms move and arrange themselves inside the catalyst.
As a result, they discovered that zinc (Zn) acts as a mediating element that promotes atomic arrangement. The principle is that when zinc is introduced, atoms find their places more easily, forming a more sophisticated and stable structure. In other words, AI has found the ‘optimal path for atomic arrangement creation’ in advance.
< Synthesis process of Zinc-introduced Platinum-Cobalt catalyst>
The zinc-platinum-cobalt catalyst, synthesized based on AI predictions, secured both higher activity and superior long-term durability compared to commercial platinum catalysts. This is a case proving that the ‘virtual blueprint’ calculated by artificial intelligence can be implemented as a high-performance catalyst in an actual laboratory.
In particular, this technology is expected to contribute to extending catalyst lifespan and reducing manufacturing costs across core carbon-neutral industries, such as hydrogen passenger cars, hydrogen trucks requiring long-distance operation, hydrogen ships, and energy storage systems (ESS).
< Conceptual diagram of AI-based catalyst development (AI-generated image) >
Professor EunAe Cho stated, “This research is a case of utilizing machine learning to predict the atomic arrangement tendency of catalysts in advance and implementing this through actual synthesis,” and added, “AI-based material design will become a new paradigm for the development of next-generation fuel cell catalysts.”
Ph.D. Candidate HyunWoo Chang from KAIST’s Department of Materials Science and Engineering and Dr. Jae Hyun Ryu from Seoul National University’s School of Chemical and Biological Engineering participated as co-first authors in this research. The research results were published on January 15, 2026, in ‘Advanced Energy Materials,’ a world-renowned academic journal in the energy materials field. ※ Paper Title: Machine Learning-Guided Design of L1₀-PtCo Intermetallic Catalysts: Zn-Mediated Atomic Ordering, DOI: https://doi.org/10.1002/aenm.202505211
This research was conducted with the support of the National Research Foundation of Korea’s Nano & Material Technology Development Program and the Korea Institute of Energy Technology Evaluation and Planning’s Energy Innovation Research Center for Fuel Cell Technology.
2026.02.27 View 576 -
Distinguished Professor Sang Yup Lee, Senior Vice President for Research, to Lead Industry-Academic-Research Cooperation in Synthetic Biology
< Sang Yup Lee, Senior Vice President for Research at KAIST (Inaugural Chairman of the Korea Synthetic Biology Association) >
KAIST announced on February 27th that Sang Yup Lee, Distinguished Professor of the Department of Chemical and Biomolecular Engineering and Senior Vice President for Research, has been appointed as the inaugural chairman of the Korea Synthetic Biology Association (KSBA). This appointment was officially ratified during the association's 5th regular general meeting held on the 23rd.
The KSBA is a specialized consultative body established to promote cooperation in policy, industry, and research within the field of synthetic biology. Based on a network spanning industry, academia, research institutes, and government, the association supports the creation of a synthetic biology ecosystem as a national strategic technology and strengthens industrial competitiveness. It has contributed to the expansion of the domestic synthetic biology foundation through multifaceted activities such as policy proposals, international cooperation, human resource development, and industrial vitalization.
Through this appointment of the inaugural chairman, the association has established a unified leadership system and is set to formalize the operational foundation in line with the enforcement of the "Synthetic Biology Promotion Act."
At the general meeting, the 2025 business performance report was presented, and agendas for the ratification of the next chairman and the appointment of executives were resolved. Officials from the Ministry of Science and ICT also attended, expressing expectations for the association’s political role and future direction.
During the meeting, the ratification of Chairman Sang Yup Lee, who was elected by the Board of Directors in accordance with the articles of incorporation, was finally approved. Chairman Sang Yup Lee is a world-leading researcher who has pioneered the fields of synthetic biology and biotechnology. As the Senior Vice President for Research at KAIST, Head of the Metabolic Engineering National Research Laboratory, and Director of both the BioProcess Engineering Research Center and the Center for Synthetic Biology, he has led research innovation and the expansion of global cooperation. With this ratification, the KSBA has officially launched an integrated leadership system encompassing the establishment of mid-to-long-term strategies, strengthening industry-research links, and expanding global networks.
< Group photo of the Korea Synthetic Biology Association General Meeting (Chairman Sang Yup Lee, 5th from the bottom left) >
Chairman Sang Yup Lee stated, "Synthetic biology is a key strategic technology that will determine national competitiveness. We will ensure close cooperation between industry, academia, research, and government so that the purpose of laws and systems can lead to practical innovation in research and industrial fields."
Furthermore, the Board of Directors approved the appointment of directors and external auditors to enhance the accountability and transparency of organizational management, including Steering Committee Secretary Lee Seung-koo (Korea Research Institute of Bioscience and Biotechnology), as well as Park Han-oh (Bioneer), Kim장성 (Korea Research Institute of Bioscience and Biotechnology), Kim Dong-myung (Chungnam National University), Oh Min-kyu (Korea University), Cho Byung-kwan (KAIST), Yoon Hye-sun (Hanyang University), and Lee Do-heon (KAIST).
At this general meeting, the major contents of the "Synthetic Biology Promotion Act," scheduled for enforcement on April 23, and the corresponding legislative notice of the enforcement decree were shared. Prior to the enforcement of the law, the Ministry of Science and ICT prepared an enforcement decree specifying delegated matters and has been conducting a legislative notice for 40 days starting February 23. The enforcement decree includes: ▲Clarification of procedures for establishing the Basic Plan for Synthetic Biology Promotion and annual implementation plans ▲Establishment of standards for designating and operating research hubs and biofoundries ▲Materialization of safety management systems and inspection procedures.
The KSBA plans to actively pursue implementation strategies for each division so that the legislative intent of the enforcement decree can be practically realized in industrial and research fields.
The Policy and System Division will strengthen its policy advisory function to ensure that field opinions are reflected in the process of establishing basic and annual implementation plans. It will also continue to present directions for synthetic biology development and social infrastructure construction strategies through the ‘KSBA Policy Insight 2026’ report.
The Convergence Division aims to advance research systems based on data, AI, and automation to simultaneously secure research reliability and efficiency. It will also support the strengthening of technology standardization and safety management capabilities through the publication of convergence technology trend reports and the operation of workshops.
The Technology and Industry Division plans to specify industry-research linkage models that meet the standards for designating and operating research infrastructure such as biofoundries. It will also expand corporate cooperation networks to ensure that the implementation of the system leads to technology commercialization and manufacturing innovation.
The Education and Network Division will prepare a competition (IDEA-B) to discover next-generation talent and strengthen safety and ethics education to increase the accountability and expertise of synthetic biology research. Additionally, it plans to expand international cooperation to ensure that domestic systems harmonize with global norms.
The KSBA plans to further enhance the stability of organizational operations through this general meeting and expand its role as a hub institution connecting policy, industry, and research. In particular, under the unified leadership centered on Chairman Sang Yup Lee, it intends to serve as a bridge for private-public cooperation so that the intent of the "Synthetic Biology Promotion Act" can be practically implemented in the field.
< KSBA Policy Insight 2026 Report of the Korea Synthetic Biology Association >
2026.02.27 View 525 -
KAIST Launches Deep-Tech Scale-up Valley, Unveils Execution Strategies for Physical AI
< Progress Report Meeting of the Deep-Tech Scale-up Valley Project >
KAIST announced on February 27th that it held the "Deep-Tech Scale-up Valley Project Progress Report Meeting" at its main campus in Daejeon on the 26th. During the meeting, the university unveiled its Physical AI strategies and execution structures, currently being developed with a focus on robotics.
The Deep-Tech Scale-up Valley Promotion Project is a joint initiative by the Ministry of Science and ICT, Daejeon Metropolitan City, and KAIST. KAIST has secured a total budget of 13.65 billion KRW for a period of three years and six months, starting from 2025. The project aims to commercialize KAIST's deep-tech capabilities in robotics to build a robust robot innovation ecosystem. A "Robot Alliance" has been formed, led by KAIST (headed by Professor Jung Kim) and including KAIST Holdings, Daejeon Techno Park, Daejeon Center for Creative Economy & Innovation, Angel Robotics, and Eurobotics.
The project seeks to foster a virtuous cycle ecosystem and nurture future "Unicorn" companies based on a three-pillar framework: Technology Commercialization, Deep-Tech R&D, and Commercialization Scale-up. In its first year (2025), the project achieved 230 billion KRW in technology transfers and investment attraction through Physical AI lectures, startup pitching sessions, and investment networking.
Physical AI refers to technology that combines robotics with artificial intelligence, allowing machines to make autonomous decisions and act in the real world. While it is gaining traction as a core field of next-generation industry—with increasing government R&D, corporate investment, and startup activity—critics have noted that successful business models applicable to actual industrial sites remain limited.
This report meeting is significant in that it redefined Physical AI not merely as a competition of AI technology, but as a matter of "industrial structure." It emphasized that commercialization is difficult unless R&D, industrial sites, and the investment ecosystem are organically linked.
Specifically, the report stated that for Physical AI to be applied to industrial sites, "meaningful data" generated from real-world operations is required, going beyond virtual environments. The strategy involves collaborating with skilled experts in manufacturing processes to accumulate data reflecting physical sensations and judgment, and establishing an execution system where robots can continuously cooperate with humans without obstructing their tasks.
Professor Kyoungchul Kong of the KAIST Department of Mechanical Engineering stated, "It is now crucial to clarify the mixed concepts of Physical AI and create a concrete platform that anyone can utilize." He added, "For AI learned in virtual environments to function properly with actual robots in the real world, we must not only improve the accuracy of virtual technologies but also ensure that physical variables in the real world are predictable and stably managed." In simpler terms, technology is needed to ensure that a robot's performance in a simulation translates seamlessly to the real world.
Professor Hyun Myung of the KAIST School of Electrical Engineering highlighted, "In the field of AI, research on Physics-Informed Neural Networks (PINN), which incorporate physical laws into the learning process, is actively underway." He emphasized, "The completion of Physical AI is possible only when hardware researchers, who understand actual physical systems, and AI researchers, who implement these into learning structures, are organically integrated. We need AI that understands physical principles, going beyond simply learning massive amounts of data."
Based on this execution structure, KAIST plans to establish a clear Value Chain connecting researchers, industrial experts, and corporations. The strategy is to expand Physical AI from lab-scale demonstrations to technologies that solve real-world industrial problems.
Jung Kim, Head of the KAIST Department of Mechanical Engineering, stated, "We have moved past the era of competing on data volume; now is the time to contemplate how to execute AI in the physical world. Based on KAIST's specific preparations and execution strategies, we will support startups and companies to succeed in the commercialization of Physical AI."
Meanwhile, the Deep-Tech Scale-up Valley Project plans to step-by-step promote the establishment of a Physical AI platform, startup discovery and investment expansion, the creation of verification testbeds, and the expansion of cooperation networks with global robotics companies.
2026.02.27 View 657 -
Three Generations of ‘Giving DNA’ Meets ‘KAIST DNA’
KAIST announced on February 26th that it has received 5.06 billion KRW in development funds, embodying the noble spirit of sharing from a single family. This donation is particularly meaningful as it was completed across three generations: rooted in a grandmother’s legacy, the father decided on the donation, and the daughter put that intention into action.
The donor stated, “I hope the research achievements of KAIST’s young scientists shine brighter than the donor’s name,” and declined all appointment ceremonies or honorary events where their name would be revealed. In accordance with the donor’s wishes, all procedures were conducted simply, and their identity will remain private.
The donor, a Seoul resident in their 70s, achieved success by building a business based on the legacy of their mother, who practiced sharing throughout her life. Recently, they decided to return a portion of their mother’s legacy to society. Having grown up watching their mother practice benevolence, donating was a natural choice. This decision was finalized through the daughter’s concrete execution. The donor’s daughter played a leading role in the entire donation process, serving as a bridge to pass the family’s spirit of sharing down to the next generation.
The donor remarked, “The sharing my mother practiced her whole life was our family’s greatest asset. Now, together with my daughter, I wish to pass that precious value to the leading figures of Korean science.” They added, “If this fund can provide practical help to young scholars, that alone is a sufficiently rewarding thing.”
Our university has decided to establish the ‘Cho Gi-yeop Next-Generation Research Leader Fellowship,’ named after the donor’s mother. The ‘Cho Gi-yeop Fellowship’ is designed as a principal-preserved fund, where the 5 billion KRW principal is maintained and the program is operated using the investment returns. Additionally, expressing the wish to “support young scientists as soon as possible,” the donor contributed an extra 60 million KRW for the first year of the program’s implementation.
Accordingly, starting this year, three ‘Cho Gi-yeop Fellows’ will be selected annually and supported with 20 million KRW each in academic activity expenses for three years. The fellowship is aimed at junior faculty members, specifically assistant and associate professors before tenure. This period is a ‘golden time’ when research capabilities grow explosively and innovative achievements are concentrated, but it is also a time when securing stable research funding is desperately needed. The support funds will be used to enhance research autonomy and scalability, such as for challenging research planning, international academic activities, and the expansion of research infrastructure. KAIST expects this fellowship to serve as a practical foundation for young researchers to make a global leap forward.
2026.02.26 View 432 -
Lotte Group Chairman Dong-Bin Shin Awarded Honorary Doctorate
< Chairman Dong-Bin Shin (left) receives the degree certificate from KAIST President Kwang Hyung Lee (right). >
KAIST announced on the 26th that it awarded an Honorary Doctorate in Business Administration to Lotte Group Chairman Dong-Bin Shin at its main campus in Daejeon on the 25th.
Chairman Dong-Bin Shin, who received the Honorary Doctorate in Business Administration, is a leading South Korean business figure who has led sustainable corporate growth amidst a rapidly changing global business environment. As Chairman of Lotte Group, Chairman Shin has stably sophisticated the group's traditional business foundations centered on retail and consumer goods, while concentrating strategic capabilities on core areas that will determine future competitiveness, such as chemicals/materials, eco-friendly energy, and digital transformation.
KAIST stated, “Chairman Dong-Bin Shin has practiced responsible management by taking Environment, Social, and Governance (ESG) as a core pillar of management under the recognition that corporate performance cannot be separated from society,” and added, “We awarded the honorary doctorate in high evaluation of his efforts in materializing industrial transformation and social value creation based on science and technology through systems and execution.” Furthermore, the importance of the process in which the results of technological innovation are practically diffused to society and users, which has been reflected in his management strategy, was also cited as a background for this conferment decision.
Chairman Dong-Bin Shin has contributed to the creation of science and technology research infrastructure and the establishment of a foundation for convergence research through industry-academia cooperation with KAIST. Lotte Group donated 14 billion KRW in development funds to KAIST to establish the ‘LOTTE–KAIST R&D Center’ and the ‘LOTTE–KAIST Design Center,’ thereby laying the groundwork for multidisciplinary convergence research in core areas required by future society, such as carbon neutrality technology, bio-sustainability, energy/materials, and healthcare.
In particular, this cooperation was evaluated as an execution-oriented industry-academia cooperation model that links research infrastructure construction, medium-to-long-term research agenda setting, and the diffusion of research results into industry and society. Along with technical research centered on the R&D Center, the Design Center has played a role in expanding the social usability of technological research by focusing on the process through which research results are delivered to society and users. This has contributed to strengthening KAIST’s research competitiveness and establishing a virtuous cycle in which research results spread as social values.
Chairman Dong-Bin Shin said, "The convergence of technology and management through industry-academia cooperation is no longer a choice but a survival strategy," and added, "I hope that the journey of Lotte and KAIST, as innovation partners designing the future together, will lead to innovations that change the world for the better."
< Chairman Dong-Bin Shin delivering a speech after receiving his Honorary Doctorate in Business Administration. >
President Kwang Hyung Lee stated, “Chairman Dong-Bin Shin is a person who has presented a new role for companies through responsible management that connects science and technology, industry, and social value,” and added, “We awarded the Honorary Doctorate in Business Administration in high recognition of his contribution to expanding research infrastructure and building a foundation for convergence research through industry-academia cooperation with KAIST.”
2026.02.26 View 476