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A World Led by Scientists and Engineers: The Joy of a Lecture Series
On the September 9th, KAIST announced a lecture series titled "The Joy of a World Led by Scientists and Engineers," where leading professors will share the joy, achievements, and social value they've found in their research. The series will run for a total of nine sessions until October 20th. This series was created to deliver a message of challenge and hope, especially to KAIST students and young people who have chosen, or are dreaming of choosing, a STEM field.
<Professor Dae-sik Kim giving a lecture on the joy of creating thinking machines>
The first lecture was held on the 8th. Professor Dae-Shik Kim of the Department of Electrical and Electronic Engineering spoke on the topic of "The Joy of Creating Thinking Machines" in the auditorium of the Digital Humanities and Social Sciences Building (N4).
September 10th: Professor Ha-woong Jeong from the Department of Physics will show how seemingly difficult physics applies to real life through various examples. Professor Jeong plans to introduce fascinating research cases in complex systems physics, including election prediction using Google search, epidemic prevention and new drug development through network analysis, fusion industry trend prediction based on patent data, and even analyzing the flocking of birds and hidden patterns in artworks with AI. He will emphasize that "complex systems, which are called 21st-century science, make physics approachable and enjoyable."
September 15th: Professor Hyun Myung of the Department of Electrical and Electronic Engineering will give a lecture on "The Joy of Making Robots Move." Professor Myung will share the story of his lifelong dream of robotics and the journey that led him to serious research. He will also share his experiences developing a cleaning robot at the Samsung Advanced Institute of Technology and creating robots that solve social problems, such as jellyfish-eradication and algae-removal robots, after joining KAIST. He will also tell the story of his recent successful startup, which developed "Dreamwalk," a controller for autonomous bipedal robots.
September 24th: Professor Jaeseung Jeong of the Department of Brain and Cognitive Sciences will present "The Joy of Brain Science: Reflecting on Happiness by Looking into the Brain." He will scientifically explore the essence of happiness, introducing recent research that shows happiness is not just a simple emotion but is deeply connected to brain neural circuits, chemical regulation, social relationships, and life attitudes. He plans to share insights from a brain science perspective on the conditions for happiness, which money and success alone can't provide.
The final lecture in the series will feature Professor Hyun-jeong Seok of the Department of Industrial Design. She will share the successful story of the KAIST mascot "Neopjuk-i" and how this once-ignored content grew into a beloved national character. This lecture aims to not only present scientific achievements but also to vividly share the joy and challenges felt by researchers, broadly publicizing the various ways science and engineering can make the world a more joyful place.
<Poster of A World Led by Scientists and Engineers: The Joy of a Lecture Series>
Young-chul Kim, Director of Student Policy, who planned the event, said, "This lecture series was organized to share the joy and value of science through the research journeys of our professors and to provide new inspiration to students and the public."
KAIST President Kwang-hyung Lee stated, "Students will feel a sense of pride in their decision to choose KAIST after directly listening to lectures from our leading professors. I hope this lecture series will be a meaningful opportunity to inspire students currently in or aspiring to a STEM field, and to show the achievements and successes that naturally result from professors enjoying their research."
Except for lectures where the entire or a portion of the content cannot be made public due to the nature of the research, a highlight video of the key contents will be produced and made available for public viewing on KAIST's official YouTube channel.
2025.09.09 View 644 -
Batteries Make 12Minute Charge for 800km Drive a Reality
<Photo 1. (From left in the front row) Dr. Hyeokjin Kwon from Chemical and Biomolecular Engineering, Professor Hee Tak Kim, and Professor Seong Su Kim from Mechanical Engineering>
Korean researchers have ushered in a new era for electric vehicle (EV) battery technology by solving the long-standing dendrite problem in lithium-metal batteries. While conventional lithium-ion batteries are limited to a maximum range of 600 km, the new battery can achieve a range of 800 km on a single charge, a lifespan of over 300,000 km, and a super-fast charging time of just 12 minutes.
KAIST (President Kwang Hyung Lee) announced on the 4th of September that a research team from the Frontier Research Laboratory (FRL), a joint project between Professor Hee Tak Kim from the Department of Chemical and Biomolecular Engineering, and LG Energy Solution, has developed a "cohesion-inhibiting new liquid electrolyte" original technology that can dramatically increase the performance of lithium-metal batteries.
Lithium-metal batteries replace the graphite anode, a key component of lithium-ion batteries, with lithium metal. However, lithium metal has a technical challenge known as dendrite, which makes it difficult to secure the battery's lifespan and stability. Dendrites are tree-like lithium crystals that form on the anode surface during battery charging, negatively affecting battery performance and stability.
This dendrite phenomenon becomes more severe during rapid charging and can cause an internal short-circuit, making it very difficult to implement a lithium-metal battery that can be recharged under fast-charging conditions.
The FRL joint research team has identified that the fundamental cause of dendrite formation during rapid charging of lithium metal is due to non-uniform interfacial cohesion on the surface of the lithium metal. To solve this problem, they developed a "cohesion-inhibiting new liquid electrolyte."
The new liquid electrolyte utilizes an anion structure with a weak binding affinity to lithium ions (Li⁺), minimizing the non-uniformity of the lithium interface. This effectively suppresses dendrite growth even during rapid charging.
This technology overcomes the slow charging speed, which was a major limitation of existing lithium-metal batteries, while maintaining high energy density. It enables a long driving range and stable operation even with fast charging.
Je-Young Kim, CTO of LG Energy Solution, said, "The four years of collaboration between LG Energy Solution and KAIST through FRL are producing meaningful results. We will continue to strengthen our industry-academia collaboration to solve technical challenges and create the best results in the field of next-generation batteries."
<Figure 1. Infographic on the KAIST-LGES FRL Lithium-Metal Battery Technology>
Hee Tak Kim, Professor from Chemical and Biomolecular Engineering at KAIST, commented, "This research has become a key foundation for overcoming the technical challenges of lithium-metal batteries by understanding the interfacial structure. It has overcome the biggest barrier to the introduction of lithium-metal batteries for electric vehicles."
The study, with Dr. Hyeokjin Kwon from the KAIST Department of Chemical and Biomolecular Engineering as the first author, was published in the prestigious journal Nature Energy on September 3.
Nature Energy: According to the Journal Impact Factor announced by Clarivate Analytics in 2024, it ranks first among 182 energy journals and 23rd among more than 21,000 journals overall.
Article Title: Covariance of interphasic properties and fast chargeability of energy-dense lithium metal batteries
DOI: 10.1038/s41560-025-01838-1
The research was conducted through the Frontier Research Laboratory (FRL, Director Professor Hee Tak Kim), which was established in 2021 by KAIST and LG Energy Solution to develop next-generation lithium-metal battery technology.
2025.09.04 View 4501 -
KAIST Wins Bid for ‘Physical AI Core Technology Demonstration’ Pilot Project
KAIST (President Kwang Hyung Lee) announced on the 28th of August that, together with Jeonbuk State, Jeonbuk National University, and Sungkyunkwan University, it has jointly won the Ministry of Science and ICT’s pilot project for the “Physical AI Core Technology Proof of Concept (PoC)”, with KAIST serving as the overall research lead. The consortium also plans to participate in a full-scale demonstration project that is expected to reach a total scale of 1 trillion KRW in the future.
In this project, KAIST led the research planning under the theme of “Collaborative Intelligence Physical AI.” Based on this, Jeonbuk National University and Jeonbuk State will carry out joint research and establish a collaborative intelligence physical AI industrial ecosystem within the province. The pilot project will begin on September 1 this year and will run until the end of the year over the next five years. Through this effort, Jeonbuk State aims to be built into a global hub for physical AI.
KAIST will take charge of developing original research technologies, creating a research environment through the establishment of a testbed, and promoting industrial diffusion. Professor Young Jae Jang of the Department of Industrial and Systems Engineering at KAIST, who is the overall project director, has been leading research on collaborative intelligence physical AI since 2016. His “Collaborative Intelligence-Based Smart Manufacturing Innovation Technology” was selected as one of KAIST’s “Top 10 Research Achievements” in 2019.
“Physical AI” refers to cutting-edge artificial intelligence technology that enables physical devices such as robots, autonomous vehicles, and factory automation equipment to perform tasks without human instruction by understanding spatiotemporal concepts.
In particular, collaborative intelligence physical AI is a technology in which numerous robots and automated devices in a factory environment work together to achieve goals. It is attracting attention as a key foundation for realizing “dark factories” in industries such as semiconductors, secondary batteries, and automobile manufacturing.
Unlike existing manufacturing AI, this technology does not necessarily require massive amounts of historical data. Through real-time, simulation-based learning, it can quickly adapt even to manufacturing environments with frequent changes and has been deemed a next-generation technology that overcomes the limitations of data dependency.
Currently, the global AI industry is led by LLMs that simulate linguistic intelligence. However, physical AI must go beyond linguistic intelligence to include spatial intelligence and virtual environment learning, requiring the organic integration of hardware such as robots, sensors, and motors with software. As a manufacturing powerhouse, Korea is well-positioned to build such an ecosystem and seize the opportunity to lead global competition.
In fact, in April 2025, KAIST won first place at INFORMS (Institute for Operations Research and the Management Sciences), the world’s largest industrial engineering society, with its case study on collaborative intelligence physical AI, beating MIT and Amazon. This achievement is recognized as proof of Korea’s global competitiveness in the physical AI technology realm.
Professor Young Jae Jang, KAIST’s overall project director, said, “Winning this large-scale national project is the result of KAIST’s collaborative intelligence physical AI research capabilities accumulated over the past decade being recognized both domestically and internationally. This will be a turning point for establishing Korea’s manufacturing industry as a global leading ‘Physical AI Manufacturing Innovation Model.’”
KAIST President Kwang Hyung Lee emphasized that “KAIST is taking on the role of leading not only academic research but also the practical industrialization of national strategic technologies. Building on this achievement, we will collaborate with Jeonbuk National University and Jeonbuk State to develop Korea into a world-class hub for physical AI innovation.”
Through this project, KAIST, Jeonbuk National University, and Jeonbuk State plan to develop Korea into a global industrial hub for physical AI.
2025.08.29 View 2238 -
KAIST Develops AI that Automatically Detects Defects in Smart Factory Manufacturing Processes Even When Conditions Change
Recently, defect detection systems using artificial intelligence (AI) sensor data have been installed in smart factory manufacturing sites. However, when the manufacturing process changes due to machine replacement or variations in temperature, pressure, or speed, existing AI models fail to properly understand the new situation and their performance drops sharply. KAIST researchers have developed AI technology that can accurately detect defects even in such situations without retraining, achieving performance improvements up to 9.42%. This achievement is expected to contribute to reducing AI operating costs and expanding applicability in various fields such as smart factories, healthcare devices, and smart cities.
KAIST (President Kwang Hyung Lee) announced on the 26th of August that a research team led by Professor Jae-Gil Lee from the School of Computing has developed a new “time-series domain adaptation” technology that allows existing AI models to be utilized without additional defect labeling, even when manufacturing processes or equipment change.
Time-series domain adaptation technology enables AI models that handle time-varying data (e.g., temperature changes, machine vibrations, power usage, sensor signals) to maintain stable performance without additional training, even when the training environment (domain) and the actual application environment differ.
Professor Lee’s team paid attention to the fact that the core problem of AI models becoming confused by environmental (domain) changes lies not only in differences in data distribution but also in changes in defect occurrence patterns (label distribution) themselves. For example, in semiconductor wafer processes, the ratio of ring-shaped defects and scratch defects may change due to equipment modifications.
The research team developed a method for decomposing new process sensor data into three components—trends, non-trends, and frequencies—to analyze their characteristics individually. Just as humans detect anomalies by combining pitch, vibration patterns, and periodic changes in machine sounds, AI was enabled to analyze data from multiple perspectives.
In other words, the team developed TA4LS (Time-series domain Adaptation for mitigating Label Shifts) technology, which applies a method of automatically correcting predictions by comparing the results predicted by the existing model with the clustering information of the new process data. Through this, predictions biased toward the defect occurrence patterns of the existing process can be precisely adjusted to match the new process.
In particular, this technology is highly practical because it can be easily combined like an additional plug-in module inserted into existing AI systems without requiring separate complex development. That is, regardless of the AI technology currently being used, it can be applied immediately with only simple additional procedures.
In experiments using four benchmark datasets of time-series domain adaptation (i.e., four types of sensor data in which changes had occurred), the research team achieved up to 9.42% improvement in accuracy compared to existing methods.[TT1]
Especially when process changes caused large differences in label distribution (e.g., defect occurrence patterns), the AI demonstrated remarkable performance improvement by autonomously correcting and distinguishing such differences. These results proved that the technology can be used more effectively without defects in environments that produce small batches of various products, one of the main advantages of smart factories.
Professor Jae-Gil Lee, who supervised the research, said, “This technology solves the retraining problem, which has been the biggest obstacle to the introduction of artificial intelligence in manufacturing. Once commercialized, it will greatly contribute to the spread of smart factories by reducing maintenance costs and improving defect detection rates.”
This research was carried out with Jihye Na, a Ph.D. student at KAIST, as the first author, with Youngeun Nam, a Ph.D. student, and Junhyeok Kang, a researcher at LG AI Research, as co-authors. The research results were presented in August 2025 at KDD (the ACM SIGKDD Conference on Knowledge Discovery and Data Mining), the world’s top academic conference in artificial intelligence and data.
※Paper Title: “Mitigating Source Label Dependency in Time-Series Domain Adaptation under Label Shifts”
※DOI: https://doi.org/10.1145/3711896.3737050
This technology was developed as part of the research outcome of the SW Computing Industry Original Technology Development Program’s SW StarLab project (RS-2020-II200862, DB4DL: Development of Highly Available and High-Performance Distributed In-Memory DBMS for Deep Learning), supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP).
2025.08.26 View 2308 -
KAIST Takes the Lead in Developing Core Technologies for Generative AI National R&D Project
KAIST (President Kwang Hyung Lee) is leading the transition to AI Transformation (AX) by advancing research topics based on the practical technological demands of industries, fostering AI talent, and demonstrating research outcomes in industrial settings. In this context, KAIST announced on the 13th of August that it is at the forefront of strengthening the nation's AI technology competitiveness by developing core AI technologies via national R&D projects for generative AI led by the Ministry of Science and ICT.
In the 'Generative AI Leading Talent Cultivation Project,' KAIST was selected as a joint research institution for all three projects—two led by industry partners and one by a research institution—and will thus be tasked with the dual challenge of developing core generative AI technologies and cultivating practical, core talent through industry-academia collaborations.
Moreover, in the 'Development of a Proprietary AI Foundation Model' project, KAIST faculty members are participating as key researchers in four out of five consortia, establishing the university as a central hub for domestic generative AI research.
Each project in the Generative AI Leading Talent Cultivation Project will receive 6.7 billion won, while each consortium in the proprietary AI foundation model development project will receive a total of 200 billion won in government support, including GPU infrastructure.
As part of the 'Generative AI Leading Talent Cultivation Project,' which runs until the end of 2028, KAIST is collaborating with LG AI Research. Professor Noseong Park from the School of Computing will participate as the principal investigator for KAIST, conducting research in the field of physics-based generative AI (Physical AI). This project focuses on developing image and video generation technologies based on physical laws and developing a 'World Model.'
In particular, research being conducted by Professor Noseong Park's team and Professor Sung-Eui Yoon's team proposes a model structure designed to help AI learn the real-world rules of the physical world more precisely. This is considered a core technology for Physical AI.
Professors Noseong Park, Jae-gil Lee, Jiyoung Hwang, Sung-Eui Yoon, and Hyun-Woo Kim from the School of Computing, who have been globally recognized for their achievements in the AI field, are jointly participating in this project. This year, they have presented work at top AI conferences such as ICLR, ICRA, ICCV, and ICML, including: ▲ Research on physics-based Ollivier Ricci-flow (ICLR 2025, Prof. Noseong Park) ▲ Technology to improve the navigation efficiency of quadruped robots (ICRA 2025, Prof. Sung-Eui Yoon) ▲ A multimodal large language model for text-video retrieval (ICCV 2025, Prof. Hyun-Woo Kim) ▲ Structured representation learning for knowledge generation (ICML 2025, Prof. Jiyoung Whang).
In the collaboration with NC AI, Professor Tae-Kyun Kim from the School of Computing is participating as the principal investigator to develop multimodal AI agent technology. The research will explore technologies applicable to the entire gaming industry, such as 3D modeling, animation, avatar expression generation, and character AI. It is expected to contribute to training practical AI talents by giving them hands-on experience in the industrial field and making the game production pipeline more efficient.
As the principal investigator, Professor Tae-Kyun Kim, a renowned scholar in 3D computer vision and generative AI, is developing key technologies for creating immersive avatars in the virtual and gaming industries. He will apply a first-person full-body motion diffusion model, which he developed through a joint research project with Meta, to VR and AR environments.
Professor Tae-Kyun Kim, Minhyeok Seong, and Tae-Hyun Oh from the School of Computing, and Professors Sung-Hee Lee, Woon-Tack Woo, Jun-Yong Noh, and Kyung-Tae Lim from the Graduate School of Culture Technology, are participating in the NC AI project. They have presented globally recognized work at CVPR 2025 and ICLR 2025, including: ▲ A first-person full-body motion diffusion model (CVPR 2025, Prof. Tae-Kyun Kim) ▲ Stochastic diffusion synchronization technology for image generation (ICLR 2025, Prof. Minhyeok Seong) ▲ The creation of a large-scale 3D facial mesh video dataset (ICLR 2025, Prof. Tae-Hyun Oh) ▲ Object-adaptive agent motion generation technology, InterFaceRays (Eurographics 2025, Prof. Sung-Hee Lee) ▲ 3D neural face editing technology (CVPR 2025, Prof. Jun-Yong Noh) ▲ Research on selective search augmentation for multilingual vision-language models (COLING 2025, Prof. Kyung-Tae Lim).
In the project led by the Korea Electronics Technology Institute (KETI), Professor Seungryong Kim from the Kim Jae-chul Graduate School of AI is participating in generative AI technology development. His team recently developed new technology for extracting robust point-tracking information from video data in collaboration with Adobe Research and Google DeepMind, proposing a key technology for clearly understanding and generating videos.
Each industry partner will open joint courses with KAIST and provide their generative AI foundation models for education and research. Selected outstanding students will be dispatched to these companies to conduct practical research, and KAIST faculty will also serve as adjunct professors at the in-house AI graduate school established by LG AI Research.
Meanwhile, KAIST showed an unrivaled presence by participating in four consortia for the Ministry of Science and ICT's 'Proprietary AI Foundation Model Development' project.
In the NC AI Consortium, Professors Tae-Kyun Kim, Sung-Eui Yoon, Noseong Park, Jiyoung Hwang, and Minhyeok Seong from the School of Computing are participating, focusing on the development of multimodal foundation models (LMMs) and robot-based models. They are particularly concentrating on developing LMMs that learn common sense about space, physics, and time. They have formed a research team optimized for developing next-generation, multimodal AI models that can understand and interact with the physical world, equipped with an 'all-purpose AI brain' capable of simultaneously understanding and processing diverse information such as text, images, video, and sound.
In the Upstage Consortium, Professors Jae-gil Lee and Hyeon-eon Oh from the School of Computing, both renowned scholars in data AI and NLP (natural language processing), along with Professor Kyung-Tae Lim from the Graduate School of Culture Technology, an LLM expert, are responsible for developing vertical models for industries such as finance, law, and manufacturing. The KAIST researchers will concentrate on developing practical AI models that are directly applicable to industrial settings and tailored to each specific industry.
The Naver Consortium includes Professor Tae-Hyun Oh from the School of Computing, who has developed key technology for multimodal learning and compositional language-vision models, Professor Hyun-Woo Kim, who has proposed video reasoning and generation methods using language models, and faculty from the Kim Jae-chul Graduate School of AI and the Department of Electrical Engineering.
In the SKT Consortium, Professor Ki-min Lee from the Kim Jae-chul Graduate School of AI, who has achieved outstanding results in text-to-image generation, human preference modeling, and visual robotic manipulation technology development, is participating. This technology is expected to play a key role in developing personalized services and customized AI solutions for telecommunications companies.
This outcome is considered a successful culmination of KAIST's strategy for developing AI technology based on industry demand and centered on on-site demonstrations.
KAIST President Kwang Hyung Lee said, "For AI technology to go beyond academic achievements and be connected to and practical for industry, continuous government support, research, and education centered on industry-academia collaboration are essential. KAIST will continue to strive to solve problems in industrial settings and make a real contribution to enhancing the competitiveness of the AI ecosystem."
He added that while the project led by Professor Sung-Ju Hwang from the Kim Jae-chul Graduate School of AI, which had applied as a lead institution for the proprietary foundation model development project, was unfortunately not selected, it was a meaningful challenge that stood out for its original approach and bold attempts. President Lee further commented, "Regardless of whether it was selected or not, such attempts will accumulate and make the Korean AI ecosystem even richer."
2025.08.13 View 2706 -
KAIST School of Transdisciplinary Studies Is Driving Innovation in Korean Education
<(From Left) Professor Jaeseung Jeong, haed of the School of Transdiciplinary Studies, Dr, Albert Chau, Vice President of Hong Kong Baptist University>
KAIST (President Kwang Hyung Lee) announced on the 24th of July that its School of Transdisciplinary Studies has been consistently showcasing the results of its experiments and practices for educational innovation both domestically and abroad.
On June 27, Professor Jaeseung Jeong, head of the School of Transdisciplinary Studies, was invited to speak at the “Pacific Asia Summit on Transdisciplinary Education 2025 (PASTE 2025)” held at Hong Kong Baptist University. He presented the Korean model of transdisciplinary education under the title “The Philosophy and Achievements of the KAIST School of Transdisciplinary Studies.”
In his talk, Professor Jeong pointed out the limitations of conventional education systems that rely on answer-centered evaluation, perfectionism, and competitiveness, claiming that they hinder creativity and integrative thinking. He then introduced the philosophy and operational practices of the School of Transdisciplinary Studies, which was established in 2019 to overcome these issues.
Professor Jeong outlined five key principles that define the school's educational philosophy: ①a broad and integrative academic foundation, ②student-driven and customized education, ③creativity and execution, ④a sense of social responsibility and global citizenship, and ⑤learning driven by intrinsic motivation and curiosity. He explained that students are admitted without a declared major, allowed to design their own learning plans, and evaluated under a P/NR system* that focuses on growth rather than competition.
*P/NR system: A non-competitive grading system led by KAIST’s School of Transdisciplinary Studies. Instead of traditional letter grades (A/B/C/Fail), students receive Pass (P) or No Record (NR), with the latter not appearing as a failure and not affecting GPA.
Professor Jeong emphasized, “This experiment at KAIST represents a new educational paradigm that values questions over knowledge, culture over structure, and inquiry over competition. Students are bridging academic learning and real-world practice by addressing societal challenges through technology, which could lead to a fundamental shift in global higher education.”
His presentation provided an opportunity to spotlight how KAIST’s experimental approach to nurturing transdisciplinary talent is pointing to new directions for the global education community beyond Korea.
< Hyungjoon Jang, a student at the School of Transdisciplinary Studies>
The achievements of KAIST’s transdisciplinary education model are also reflected in students’ academic accomplishments. Hyungjoon Jang, a student at the School of Transdisciplinary Studies, participated in a collaborative study led by his mentor, Professor Jaekyung Kim in the Department of Mathematical Sciences, along with researchers from Chungnam National University and the Institute for Basic Science (IBS). Their groundbreaking analytical method enables the accurate estimation of inhibition constants using only a single inhibitor concentration. The paper was published in the prestigious journal Nature Communications in June, with Jang listed as co–first author.
Jang played a leading role throughout the research process by developing the experimental methodology, creating a software package to support the method, drafting the manuscript, and engaging in peer review. He also effectively communicated mathematical and statistical models to pharmaceutical experts by mastering presentation techniques and visual explanation strategies, thereby setting a strong example of interdisciplinary collaboration.
He emphasized that “the School of Transdisciplinary Studies’ mentor system allowed regular research feedback and the systematic acquisition of essential knowledge and analytical skills through courses in biochemistry and computational neuroscience.”
This example demonstrates how undergraduate students at the School of Transdisciplinary Studies can take leading roles in cutting-edge interdisciplinary research.
The school’s educational philosophy is also reflected in students’ practical actions. Inseo Jeong, a current student and founder of the startup MPAge Inc., made a meaningful donation to help establish a creative makerspace in the school.
<Inseo Jeong, founder of MPAG>
Inseo Jeong explained that the decision was made to express gratitude for the knowledge gained and the mentorship received from professors, saying that at the School of Transdisciplinary Studies, she learned not only how to solve problems with technology but also how to view society, and that learning has helped her grow. She added, “The deep understanding of humanity and the world emphasized by Professor Jaeseung Jeong will be a great asset not only to entrepreneurs but to all students pursuing diverse paths,” expressing support for her fellow students.
Inseo Jeong collaborated for over two years with Professor Hyunwook Ka of the School of Transdisciplinary Studies on software research for individuals with hearing impairments. After numerous algorithm designs and experimental iterations, their work, which considered the social scalability of technology, was presented at the world-renowned CSUN Assistive Technology Conference held at California State University, Northridge. The project has filed for a patent under KAIST’s name.
※ Presentation title: Evidence-Based Adaptive Transcription for Sign Language Users
KAIST is now working to complete the makerspace on the third floor of the Administrative Annex (N2) in Room 314 with a size of approximately 33 m2 during the summer. The makerspace is expected to serve as a hands-on, integrative learning environment where various ideas can be realized and implemented, playing a key role in fostering students’ creative problem-solving and integrative thinking skills.
KAIST President Kwang Hyung Lee stated, “The School of Transdisciplinary Studies is both an experimental ground and a practical field for overcoming the limitations of traditional education and nurturing global talents with creative problem-solving skills and integrative thinking, which are essential for the future.” He added, “KAIST will continue to lead efforts to cultivate question-asking, inquiry-driven, transdisciplinary talents and propose new paradigms for education and research.”
2025.07.24 View 1918 -
KAIST Enhances Immunotherapy for Difficult-to-Treat Brain Tumors with Gut Microbiota
< Photo 1.(From left) Prof. Heung Kyu Lee, Department of Biological Sciences,
and Dr. Hyeon Cheol Kim>
Advanced treatments, known as immunotherapies that activate T cells—our body's immune cells—to eliminate cancer cells, have shown limited efficacy as standalone therapies for glioblastoma, the most lethal form of brain tumor. This is due to their minimal response to glioblastoma and high resistance to treatment.
Now, a KAIST research team has now demonstrated a new therapeutic strategy that can enhance the efficacy of immunotherapy for brain tumors by utilizing gut microbes and their metabolites. This also opens up possibilities for developing microbiome-based immunotherapy supplements in the future.
KAIST (President Kwang Hyung Lee) announced on July 1 that a research team led by Professor Heung Kyu Lee of the Department of Biological Sciences discovered and demonstrated a method to significantly improve the efficiency of glioblastoma immunotherapy by focusing on changes in the gut microbial ecosystem.
The research team noted that as glioblastoma progresses, the concentration of ‘tryptophan’, an important amino acid in the gut, sharply decreases, leading to changes in the gut microbial ecosystem. They discovered that by supplementing tryptophan to restore microbial diversity, specific beneficial strains activate CD8 T cells (a type of immune cell) and induce their infiltration into tumor tissues. Through a mouse model of glioblastoma, the research team confirmed that tryptophan supplementation enhanced the response of cancer-attacking T cells (especially CD8 T cells), leading to their increased migration to tumor sites such as lymph nodes and the brain.
In this process, they also revealed that ‘Duncaniella dubosii’, a beneficial commensal bacterium present in the gut, plays a crucial role. This bacterium helped T cells effectively redistribute within the body, and survival rates significantly improved when used in combination with immunotherapy (anti-PD-1).
Furthermore, it was demonstrated that even when this commensal bacterium was administered alone to germ-free mice (mice without any commensal microbes), the survival rate for glioblastoma increased. This is because the bacterium utilizes tryptophan to regulate the gut environment, and the metabolites produced in this process strengthen the ability of CD8 T cells to attack cancer cells.
Professor Heung Kyu Lee explained, "This research is a meaningful achievement, showing that even in intractable brain tumors where immune checkpoint inhibitors had no effect, a combined strategy utilizing gut microbes can significantly enhance treatment response."
Dr. Hyeon Cheol Kim of KAIST (currently a postdoctoral researcher at the Institute for Biological Sciences) participated as the first author. The research findings were published online in Cell Reports, an international journal in the life sciences, on June 26.
This research was conducted as part of the Basic Research Program and Bio & Medical Technology Development Program supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
※Paper Title: Gut microbiota dysbiosis induced by brain tumor modulates the efficacy of immunotherapy
※DOI: https://doi.org/10.1016/j.celrep.2025.115825
2025.07.02 View 4340 -
KAIST Research Team Develops an AI Framework Capable of Overcoming the Strength-Ductility Dilemma in Additive-manufactured Titanium Alloys
<(From Left) Ph.D. Student Jaejung Park and Professor Seungchul Lee of KAIST Department of Mechanical Engineering and , Professor Hyoung Seop Kim of POSTECH, and M.S.–Ph.D. Integrated Program Student Jeong Ah Lee of POSTECH. >
The KAIST research team led by Professor Seungchul Lee from Department of Mechanical Engineering, in collaboration with Professor Hyoung Seop Kim’s team at POSTECH, successfully overcame the strength–ductility dilemma of Ti 6Al 4V alloy using artificial intelligence, enabling the production of high strength, high ductility metal products. The AI developed by the team accurately predicts mechanical properties based on various 3D printing process parameters while also providing uncertainty information, and it uses both to recommend process parameters that hold high promise for 3D printing.
Among various 3D printing technologies, laser powder bed fusion is an innovative method for manufacturing Ti-6Al-4V alloy, renowned for its high strength and bio-compatibility. However, this alloy made via 3D printing has traditionally faced challenges in simultaneously achieving high strength and high ductility. Although there have been attempts to address this issue by adjusting both the printing process parameters and heat treatment conditions, the vast number of possible combinations made it difficult to explore them all through experiments and simulations alone.
The active learning framework developed by the team quickly explores a wide range of 3D printing process parameters and heat treatment conditions to recommend those expected to improve both strength and ductility of the alloy. These recommendations are based on the AI model’s predictions of ultimate tensile strength and total elongation along with associated uncertainty information for each set of process parameters and heat treatment conditions. The recommended conditions are then validated by performing 3D printing and tensile tests to obtain the true mechanical property values. These new data are incorporated into further AI model training, and through iterative exploration, the optimal process parameters and heat treatment conditions for producing high-performance alloys were determined in only five iterations. With these optimized conditions, the 3D printed Ti-6Al-4V alloy achieved an ultimate tensile strength of 1190 MPa and a total elongation of 16.5%, successfully overcoming the strength–ductility dilemma.
Professor Seungchul Lee commented, “In this study, by optimizing the 3D printing process parameters and heat treatment conditions, we were able to develop a high-strength, high-ductility Ti-6Al-4V alloy with minimal experimentation trials. Compared to previous studies, we produced an alloy with a similar ultimate tensile strength but higher total elongation, as well as that with a similar elongation but greater ultimate tensile strength.” He added, “Furthermore, if our approach is applied not only to mechanical properties but also to other properties such as thermal conductivity and thermal expansion, we anticipate that it will enable efficient exploration of 3D printing process parameters and heat treatment conditions.”
This study was published in Nature Communications on January 22 (https://doi.org/10.1038/s41467-025-56267-1), and the research was supported by the National Research Foundation of Korea’s Nano & Material Technology Development Program and the Leading Research Center Program.
2025.02.21 View 10281 -
KAIST Uncovers the Principles of Gene Expression Regulation in Cancer and Cellular Functions
< (From left) Professor Seyun Kim, Professor Gwangrog Lee, Dr. Hyoungjoon Ahn, Dr. Jeongmin Yu, Professor Won-Ki Cho, and (below) PhD candidate Kwangmin Ryu of the Department of Biological Sciences>
A research team at KAIST has identified the core gene expression networks regulated by key proteins that fundamentally drive phenomena such as cancer development, metastasis, tissue differentiation from stem cells, and neural activation processes. This discovery lays the foundation for developing innovative therapeutic technologies.
On the 22nd of January, KAIST (represented by President Kwang Hyung Lee) announced that the joint research team led by Professors Seyun Kim, Gwangrog Lee, and Won-Ki Cho from the Department of Biological Sciences had uncovered essential mechanisms controlling gene expression in animal cells.
Inositol phosphate metabolites produced by inositol metabolism enzymes serve as vital secondary messengers in eukaryotic cell signaling systems and are broadly implicated in cancer, obesity, diabetes, and neurological disorders.
The research team demonstrated that the inositol polyphosphate multikinase (IPMK) enzyme, a key player in the inositol metabolism system, acts as a critical transcriptional activator within the core gene expression networks of animal cells. Notably, although IPMK was previously reported to play an important role in the transcription process governed by serum response factor (SRF), a representative transcription factor in animal cells, the precise mechanism of its action was unclear.
SRF is a transcription factor directly controlling the expression of at least 200–300 genes, regulating cell growth, proliferation, apoptosis, and motility, and is indispensable for organ development, such as in the heart.
The team discovered that IPMK binds directly to SRF, altering the three-dimensional structure of the SRF protein. This interaction facilitates the transcriptional activity of various genes through the SRF activated by IPMK, demonstrating that IPMK acts as a critical regulatory switch to enhance SRF's protein activity.
< Figure 1. The serum response factor (SRF) protein, a key transcription factor in animal cells, directly binds to inositol polyphosphate multikinase (IPMK) enzyme and undergoes structural change to acquire DNA binding ability, and precisely regulates growth and differentiation of animal cells through transcriptional activation. >
The team further verified that disruptions in the direct interaction between IPMK and SRF lead to the reduced functionality and activity of SRF, causing severe impairments in gene expression.
By highlighting the significance of the intrinsically disordered region (IDR) in SRF, the researchers underscored the biological importance of intrinsically disordered proteins (IDPs). Unlike most proteins that adopt distinct structures through folding, IDPs, including those with IDRs, do not exhibit specific structures but play crucial biological roles, attracting significant attention in the scientific community.
Professor Seyun Kim commented, "This study provides a vital mechanism proving that IPMK, a key enzyme in the inositol metabolism system, is a major transcriptional activator in the core gene expression network of animal cells. By understanding fundamental processes such as cancer development and metastasis, tissue differentiation from stem cells, and neural activation through SRF, we hope this discovery will lead to the broad application of innovative therapeutic technologies."
The findings were published on January 7th in the international journal Nucleic Acids Research (IF=16.7, top 1.8% in Biochemistry and Molecular Biology), under the title “Single-molecule analysis reveals that IPMK enhances the DNA-binding activity of the transcription factor SRF" (DOI: 10.1093/nar/gkae1281).
This research was supported by the National Research Foundation of Korea's Mid-career Research Program, Leading Research Center Program, and Global Research Laboratory Program, as well as by the Suh Kyungbae Science Foundation and the Samsung Future Technology Development Program.
2025.01.24 View 15870 -
KAIST Wins CES 2025 Innovation Award, Showcasing Innovative Technologies
KAIST will showcase innovative technologies at the world’s largest technology fair, the Consumer Electronics Show (CES 2025). In addition, KAIST startups VIRNECT Inc., Standard Energy Inc., A2US Inc., and Panmnesia, Inc. won the 2025 CES Innovation Awards.
< Image 1. 3D-Graphical Profile of CES 2025 KAIST Exhibition Booth >
KAIST (President Kwang-Hyung Lee) announced on the 31st that it will operate a 140㎡ standalone booth at CES Eureka Park, which will be held in Las Vegas, USA from January 7th to 10th next year, to showcase KAIST's innovative technologies to global companies and investors.
KAIST startups VIRNECT, Standard Energy, A2US, and Panmnesia, Inc. won the 2025 CES Innovation Awards. ▴VIRNECT won the Innovation Award in the ‘Industrial Equipment and Machinery’ category for ‘VisionX’, an AI-based smart glass for industrial sites; ▴Standard Energy Co., Ltd. won the Innovation Award in the ‘Smart City’ category for developing the world’s first vanadium-ion battery; ▴A2US won the Innovation Award in the ‘Environment & Energy’ category for its portable air purifier that eliminates bacteria, odors, and fine dust in the air with just water droplets; ▴Panmnesia, Inc. won the Innovation Award in the ‘Computer Peripherals and Accessories’ category for its ‘CXL-based GPU Memory Expansion Kit’ that can drastically reduce the cost of building AI infrastructure.
< Image 2. (From left on the top row) VIRNECT, Standard Energy, (From left on the bottom row) A2US, Panmnesia, Inc. >
This exhibition will feature 15 startups that are standing out in cutting-edge technologies such as artificial intelligence (AI), robotics, mobility, and sustainability. In particular, AI-based deep tech startups in various industries such as logistics, architecture, and medicine will take up half of the total, showcasing the companies’ innovative AI technologies.
Polyphenol Factory Co.,Ltd introduces ‘Grabity’, a hair loss shampoo launched domestically, which applies the patented ingredient ‘LiftMax 308™’ that forms an instantaneous protective layer on the hair during the shampooing process. A real-time demonstration will be held at this exhibition hall so that visitors can experience the effects of the ingredient directly, and plans to enter the global market starting with the launch on Amazon in the US in January 2025.
VIRNECT will present ‘VisionX’, a prototype that won the Innovation Award this time. The product provides a chatbot AI through an AI voice interface, and has a function that allows users to check the status of the equipment in real time through conversations with the AI and receive troubleshooting guidance through voice conversations, so users can experience it directly at the KAIST Hall.
‘Standard Energy’ plans to exhibit ‘Energy Tile’, an indoor ESS that utilizes the world’s first vanadium ion battery (hereinafter referred to as VIB). VIB is absolutely safe from fire and has high installation flexibility, so it can be applied to smart cities and AI data centers.
‘A2US’ is the only company in the world that has hydroxyl radical water production technology, and won the Innovation Award for its first product, an air purifier. In the future, it is expected to be widely commercialized in air and water purification, smart farms, food tech, and semiconductor cleaning using safe and environmentally friendly hydroxyl radical water.
Panmnesia, Inc. won the CES Innovation Award for its GPU memory expansion solution equipped with its CXL 3.1 IP. By connecting a memory expansion device using Panmnesia’s CXL IP, the GPU’s memory capacity can be expanded to the terabyte level. Following the Innovation Award for ‘CXL-equipped AI Accelerator’ at CES 2024 last year, it is the only company to have won the Innovation Award for its AI-oriented CXL solution for two consecutive years.
In addition, technologies from a total of 15 companies will be introduced, including ▴Omelet ▴NEXTWAVE ▴Planby Technologies ▴Cosmo Bee ▴ImpactAI ▴Roen Surgical ▴DIDEN Roboticss ▴Autopedia ▴OAQ ▴HydroXpand ▴BOOKEND ▴Sterri.
On the central stage of the KAIST Hall, KAIST students selected as CES Student Supporters will conduct interviews with participating companies and promote the companies' innovative technologies and solutions. On the 8th, from 5 PM to 7 PM, a KAIST NIGHT event will be held where pre-invited investors and participating companies can network.
Keon Jae Lee, the head of the Institute of Technology Value Creation, said, “Through CES 2025, we will showcase innovative technologies and solutions from startups based on KAIST’s deep science and deep tech, and lead commercialization in cutting-edge technology fields such as AI, robotics, mobility, and environment/energy. KAIST plans to further promote technology commercialization by supporting the growth and marketing of innovative startups through the Institute of Technology Value Creation and by strengthening global networks and expanding cooperation opportunities.”
2024.12.31 View 12223 -
KAIST Proposes AI Training Method that will Drastically Shorten Time for Complex Quantum Mechanical Calculations
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model
- Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices
The close relationship between AI and high-performance scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for their AI-related research contributions in their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel AI approach.
KAIST (President Kwang-Hyung Lee) announced on the 30th of October that Professor Yong-Hoon Kim's team from the School of Electrical Engineering developed a 3D computer vision artificial neural network-based computation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations traditionally performed using supercomputers to derive the properties of materials.
< Figure 1. Various methodologies are utilized in the simulation of materials and materials, such as quantum mechanical calculations at the nanometer (nm) level, classical mechanical force fields at the scale of tens to hundreds of nanometers, continuum dynamics calculations at the macroscopic scale, and calculations that mix simulations at different scales. These simulations are already playing a key role in a wide range of basic research and application development fields in combination with informatics techniques. Recently, there have been active efforts to introduce machine learning techniques to radically accelerate simulations, but research on introducing machine learning techniques to quantum mechanical electronic structure calculations, which form the basis of high-scale simulations, is still insufficient. >
The quantum mechanical density functional theory (DFT) calculations using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow fast and accurate prediction of material properties.
*Density functional theory (DFT): A representative theory of ab initio (first principles) calculations that calculate quantum mechanical properties from the atomic level.
However, practical DFT calculations require generating 3D electron density and solving quantum mechanical equations through a complex, iterative self-consistent field (SCF)* process that must be repeated tens to hundreds of times. This restricts its application to systems with only a few hundred to a few thousand atoms.
*Self-consistent field (SCF): A scientific computing method widely used to solve complex many-body problems that must be described by a number of interconnected simultaneous differential equations.
Professor Yong-Hoon Kim’s research team questioned whether recent advancements in AI techniques could be used to bypass the SCF process. As a result, they developed the DeepSCF model, which accelerates calculations by learning chemical bonding information distributed in a 3D space using neural network algorithms from the field of computer vision.
< Figure 2. The deepSCF methodology developed in this study provides a way to rapidly accelerate DFT calculations by avoiding the self-consistent field process (orange box) that had to be performed repeatedly in traditional quantum mechanical electronic structure calculations through artificial neural network techniques (green box). The self-consistent field process is a process of predicting the 3D electron density, constructing the corresponding potential, and then solving the quantum mechanical Cohn-Sham equations, repeating tens to hundreds of times. The core idea of the deepSCF methodology is that the residual electron density (δρ), which is the difference between the electron density (ρ) and the sum of the electron densities of the constituent atoms (ρ0), corresponds to chemical bonding information, so the self-consistent field process is replaced with a 3D convolutional neural network model. >
The research team focused on the fact that, according to density functional theory, electron density contains all quantum mechanical information of electrons, and that the residual electron density — the difference between the total electron density and the sum of the electron densities of the constituent atoms — contains chemical bonding information. They used this as the target for machine learning.
They then adopted a dataset of organic molecules with various chemical bonding characteristics, and applied random rotations and deformations to the atomic structures of these molecules to further enhance the model’s accuracy and generalization capabilities. Ultimately, the research team demonstrated the validity and efficiency of the DeepSCF methodology on large, complex systems.
< Figure 3. An example of applying the deepSCF methodology to a carbon nanotube-based DNA sequence analysis device model (top left). In addition to classical mechanical interatomic forces (bottom right), the residual electron density (top right) and quantum mechanical electronic structure properties such as the electronic density of states (DOS) (bottom left) containing information on chemical bonding are rapidly predicted with an accuracy corresponding to the standard DFT calculation results that perform the SCF process. >
Professor Yong-Hoon Kim, who supervised the research, explained that his team had found a way to map quantum mechanical chemical bonding information in a 3D space onto artificial neural networks. He noted, “Since quantum mechanical electron structure calculations underpin materials simulations across all scales, this research establishes a foundational principle for accelerating material calculations using artificial intelligence.”
Ryong-Gyu Lee, a PhD candidate in the School of Electrical Engineering, served as the first author of this research, which was published online on October 24 in Npj Computational Materials, a prestigious journal in the field of material computation. (Paper title: “Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints”)
This research was conducted with support from the KAIST High-Risk Research Program for Graduate Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.
2024.10.30 View 10335 -
KAIST Professor Uichin Lee Receives Distinguished Paper Award from ACM
< Photo. Professor Uichin Lee (left) receiving the award >
KAIST (President Kwang Hyung Lee) announced on the 25th of October that Professor Uichin Lee’s research team from the School of Computing received the Distinguished Paper Award at the International Joint Conference on Pervasive and Ubiquitous Computing and International Symposium on Wearable Computing (Ubicomp / ISWC) hosted by the Association for Computing Machinery (ACM) in Melbourne, Australia on October 8.
The ACM Ubiquitous Computing Conference is the most prestigious international conference where leading universities and global companies from around the world present the latest research results on ubiquitous computing and wearable technologies in the field of human-computer interaction (HCI).
The main conference program is composed of invited papers published in the Proceedings of the ACM (PACM) on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), which covers the latest research in the field of ubiquitous and wearable computing.
The Distinguished Paper Award Selection Committee selected eight papers among 205 papers published in Vol. 7 of the ACM Proceedings (PACM IMWUT) that made outstanding and exemplary contributions to the research community. The committee consists of 16 prominent experts who are current and former members of the journal's editorial board which made the selection after a rigorous review of all papers for a period that stretched over a month.
< Figure 1. BeActive mobile app to promote physical activity to form active lifestyle habits >
The research that won the Distinguished Paper Award was conducted by Dr. Junyoung Park, a graduate of the KAIST Graduate School of Data Science, as the 1st author, and was titled “Understanding Disengagement in Just-in-Time Mobile Health Interventions”
Professor Uichin Lee’s research team explored user engagement of ‘Just-in-Time Mobile Health Interventions’ that actively provide interventions in opportune situations by utilizing sensor data collected from health management apps, based on the premise that these apps are aptly in use to ensure effectiveness.
< Figure 2. Traditional user-requested digital behavior change intervention (DBCI) delivery (Pull) vs. Automatic transmission (Push) for Just-in-Time (JIT) mobile DBCI using smartphone sensing technologies >
The research team conducted a systematic analysis of user disengagement or the decline in user engagement in digital behavior change interventions. They developed the BeActive system, an app that promotes physical activities designed to help forming active lifestyle habits, and systematically analyzed the effects of users’ self-control ability and boredom-proneness on compliance with behavioral interventions over time.
The results of an 8-week field trial revealed that even if just-in-time interventions are provided according to the user’s situation, it is impossible to avoid a decline in participation. However, for users with high self-control and low boredom tendency, the compliance with just-in-time interventions delivered through the app was significantly higher than that of users in other groups.
In particular, users with high boredom proneness easily got tired of the repeated push interventions, and their compliance with the app decreased more quickly than in other groups.
< Figure 3. Just-in-time Mobile Health Intervention: a demonstrative case of the BeActive system: When a user is identified to be sitting for more than 50 mins, an automatic push notification is sent to recommend a short active break to complete for reward points. >
Professor Uichin Lee explained, “As the first study on user engagement in digital therapeutics and wellness services utilizing mobile just-in-time health interventions, this research provides a foundation for exploring ways to empower user engagement.” He further added, “By leveraging large language models (LLMs) and comprehensive context-aware technologies, it will be possible to develop user-centered AI technologies that can significantly boost engagement."
< Figure 4. A conceptual illustration of user engagement in digital health apps. Engagement in digital health apps consists of (1) engagement in using digital health apps and (2) engagement in behavioral interventions provided by digital health apps, i.e., compliance with behavioral interventions. Repeated adherences to behavioral interventions recommended by digital health apps can help achieve the distal health goals. >
This study was conducted with the support of the 2021 Biomedical Technology Development Program and the 2022 Basic Research and Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT.
< Figure 5. A conceptual illustration of user disengagement and engagement of digital behavior change intervention (DBCI) apps. In general, user engagement of digital health intervention apps consists of two components: engagement in digital health apps and engagement in behavioral interventions recommended by such apps (known as behavioral compliance or intervention adherence). The distinctive stages of user can be divided into adoption, abandonment, and attrition. >
< Figure 6. Trends of changes in frequency of app usage and adherence to behavioral intervention over 8 weeks, ● SC: Self-Control Ability (High-SC: user group with high self-control, Low-SC: user group with low self-control) ● BD: Boredom-Proneness (High-BD: user group with high boredom-proneness, Low-BD: user group with low boredom-proneness). The app usage frequencies were declined over time, but the adherence rates of those participants with High-SC and Low-BD were significantly higher than other groups. >
2024.10.25 View 14449