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KAIST Awarded Presidential Commendation for Contributions in Software Industry
- At the “25th Software Industry Day” celebration held in the afternoon on Monday, December 2nd, 2024 at Yangjae L Tower in Seoul
- KAIST was awarded the “Presidential Commendation” for its contributions for the advancement of the Software Industry in the Group Category
- Korea’s first AI master’s and doctoral degree program opened at KAIST Kim Jaechul Graduate School of AI
- Focus on training non-major developers through SW Officer Training Academy "Jungle", Machine Learning Engineer Bootcamp, etc., talents who can integrate development and collaboration, and advanced talents in the latest AI technologies.
- Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI received Prime Minister’s Commendation for his contributions for the advancement of the software industry.
< Photo 1. Professor Kyung-soo Kim, the Senior Vice President for Planning and Budget (second from the left) and the Manager of Planning Team, Mr. Sunghoon Jung, stand at the stage after receiving the Presidential Commendation as KAIST was selected as one of the groups that contributed to the advancement of the software industry at the "25th Software Industry Day" celebration. >
“KAIST has been leading the way in achieving the grand goal of fostering 1 million AI talents in Korea by services that pan from providing various educational opportunities, from developing the capabilities of experts with no computer science specialty to fostering advanced professionals. I would like to thank all members of KAIST community who worked hard to achieve the great feat of receiving the Presidential Commendations.” (KAIST President Kwang Hyung Lee)
KAIST (President Kwang Hyung Lee) announced on December 3rd that it was selected as a group that contributed to the advancement of the software industry at the “2024 Software Industry Day” celebration held at the Yangjae El Tower in Seoul on the 2nd of December and received a presidential commendation.
The “Software Industry Day”, hosted by the Ministry of Science and ICT and organized by the National IT Industry Promotion Agency and the Korea Software Industry Association, is an event designed to promote the status of software industry workers in Korea and to honor their achievements.
Every year, those who have made significant contributions to policy development, human resource development, and export growth for industry revitalization are selected and awarded the ‘Software Industry Development Contribution Award.’
KAIST was recognized for its contribution to developing a demand-based, industrial field-centric curriculum and fostering non-major developers and convergence talents with the goal of expanding software value and fostering excellent human resources.
< Photo 2. Senior Vice President for Planning and Budget Kyung-soo Kim receiving the commendation as the representative of KAIST >
Specifically, it first opened the SW Officer Training Academy "Jungle" to foster convergent program developers equipped with the abilities to handle both the computer coding and human interactions for collaborations. This is a non-degree program that provides intensive study and assignments for 5 months for graduates and intellectuals without prior knowledge of computer science.
KAIST Kim Jaechul Graduate School of AI opened and operated Korea’s first master's and doctoral degree program in the field of artificial intelligence. In addition, it planned a “Machine Learning Engineers’ Boot Camp” and conducted lectures and practical training for a total of 16 weeks on the latest AI technologies such as deep learning basics and large language models. It aims to strengthen the practical capabilities of start-up companies while lowering the threshold for companies to introduce AI technology.
Also, KAIST was selected to participate in the 1st and 2nd stages of the Software-centered University Project and has been taking part in the project since 2016. Through this, it was highly evaluated for promoting curriculum based on latest technology, an autonomous system where students directly select integrated education, and expansion of internships.
< Photo 3. Professor Minjoon Seo of Kim Jaechul Graduate School of AI, who received the Prime Minister's Commendation for his contribution to the advancement of the software industry on the same day >
At the awards ceremony that day, Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI also received the Prime Minister's Commendation for his contribution to the advancement of the software industry. Professor Seo was recognized for his leading research achievements in the fields of AI and natural language processing by publishing 28 papers in top international AI conferences over the past four years.
At the same time, he was noted for his contributions to enhancing the originality and innovation of language model research, such as △knowledge encoding, △knowledge access and utilization, and △high-dimensional inference performance, and for demonstrating leadership in the international academic community.
President Kwang Hyung Lee of KAIST stated, “Our university will continue to do its best to foster software talents with global competitiveness through continuous development of cutting-edge curriculum and innovative degree systems.”
2024.12.03 View 3845 -
KAIST Develops a Multifunctional Structural Battery Capable of Energy Storage and Load Support
Structural batteries are used in industries such as eco-friendly, energy-based automobiles, mobility, and aerospace, and they must simultaneously meet the requirements of high energy density for energy storage and high load-bearing capacity. Conventional structural battery technology has struggled to enhance both functions concurrently. However, KAIST researchers have succeeded in developing foundational technology to address this issue.
< Photo 1. (From left) Professor Seong Su Kim, PhD candidates Sangyoon Bae and Su Hyun Lim of the Department of Mechanical Engineering >
< Photo 2. (From left) Professor Seong Su Kim and Master's Graduate Mohamad A. Raja of KAIST Department of Mechanical Engineering >
KAIST (represented by President Kwang Hyung Lee) announced on the 19th of November that Professor Seong Su Kim's team from the Department of Mechanical Engineering has developed a thin, uniform, high-density, multifunctional structural carbon fiber composite battery* capable of supporting loads, and that is free from fire risks while offering high energy density.
*Multifunctional structural batteries: Refers to the ability of each material in the composite to simultaneously serve as a load-bearing structure and an energy storage element.
Early structural batteries involved embedding commercial lithium-ion batteries into layered composite materials. These batteries suffered from low integration of their mechanical and electrochemical properties, leading to challenges in material processing, assembly, and design optimization, making commercialization difficult.
To overcome these challenges, Professor Kim's team explored the concept of "energy-storing composite materials," focusing on interface and curing properties, which are critical in traditional composite design. This led to the development of high-density multifunctional structural carbon fiber composite batteries that maximize multifunctionality.
The team analyzed the curing mechanisms of epoxy resin, known for its strong mechanical properties, combined with ionic liquid and carbonate electrolyte-based solid polymer electrolytes. By controlling temperature and pressure, they were able to optimize the curing process.
The newly developed structural battery was manufactured through vacuum compression molding, increasing the volume fraction of carbon fibers—serving as both electrodes and current collectors—by over 160% compared to previous carbon-fiber-based batteries.
This greatly increased the contact area between electrodes and electrolytes, resulting in a high-density structural battery with improved electrochemical performance. Furthermore, the team effectively controlled air bubbles within the structural battery during the curing process, simultaneously enhancing the battery's mechanical properties.
Professor Seong Su Kim, the lead researcher, explained, “We proposed a framework for designing solid polymer electrolytes, a core material for high-stiffness, ultra-thin structural batteries, from both material and structural perspectives. These material-based structural batteries can serve as internal components in cars, drones, airplanes, and robots, significantly extending their operating time with a single charge. This represents a foundational technology for next-generation multifunctional energy storage applications.”
< Figure 2. Supplementary cover of ACS Applied Materials & Interfaces >
Mohamad A. Raja, a master’s graduate of KAIST’s Department of Mechanical Engineering, participated as the first author of this research, which was published in the prestigious journal ACS Applied Materials & Interfaces on September 10. The paper was recognized for its excellence and selected as a supplementary cover article. (Paper title: “Thin, Uniform, and Highly Packed Multifunctional Structural Carbon Fiber Composite Battery Lamina Informed by Solid Polymer Electrolyte Cure Kinetics.” https://doi.org/10.1021/acsami.4c08698)
This research was supported by the National Research Foundation of Korea’s Mid-Career Researcher Program and the National Semiconductor Research Laboratory Development Program.
2024.11.27 View 3838 -
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 3974 -
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 5200 -
KAIST Develops Technology for the Precise Diagnosis of Electric Vehicle Batteries Using Small Currents
Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.
KAIST (represented by President Kwang Hyung Lee) announced on the 17th of October that a research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.
EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.
However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.
< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >
To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.
In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.
Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”
This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)
< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >
This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.
2024.10.17 View 4422 -
KAIST Industrial Design’s Professor Sangmin Bae’s team selected as Top 20 of James Dyson Award 2024
KAIST (President Kwang-Hyung Lee) announced that the 'Oxynizer', a non-electrical medical oxygen generator for developing countries designed by Professor Sangmin Bae's team in the Department of Industrial Design, has been selected to be the Top 20 of the James Dyson Award 2024. At the same time, it was announced on the 16th that it was selected as one of the top 100 ‘Prototypes for Humanity’ 2024 and will be exhibited in Dubai in November.
< Photo 1. Photo of the award-winning team of Professor Sangmin Bae’s students of KAIST Department of Industrial Designs at the James Dyson Award 2024 announcement of the National Winners >
The James Dyson Award is a design award hosted by Sir James Dyson, founder of Dyson, and receives ideas for solving everyday problems from next-generation engineers and designers around the world, and selects and awards innovative and excellent designs every year.
The ‘Oxynizer’ developed by Professor Sangmin Bae’s team was selected as the winner of the screening within Korea in September after competing with 122 domestic teams, and was awarded a prize of 5,000 pounds for idea advancement, product development, and commercialization.
< Photo 2. A photo of Professor Sangmin Bae’s students’ award-winning achievement, ‘Oxynizer’ >
In addition, on October 16th, it was selected as one of the top 20 international winners among 1,911 competing works from 29 countries around the world. The international winner will be selected by Sir James Dyson and announced on November 13th. The international competition winner will receive a prize of £5,000, and the winner will receive an additional £30,000, giving them the opportunity to commercialize their idea.
‘Prototype for Humanity’ is a global project hosted by Art Dubai Group and carried out in collaboration with Dubai Future Foundation, Dubai Arts & Culture Authority, and Dubai International Financial Center. It is a forum for international cooperation where leading universities around the world, including Harvard University and MIT, participate to discuss global problems and solutions.
‘Oxynizer’ was selected on September 11 as one of the top 100 out of 3,000 entries submitted by universities in over 100 countries, and will be exhibited at the Jumeirah Emirates Towers of Dubai Future Foundation from November 17 to 22. The organizers will select the top five during the exhibition period, and will award a total of $100,000 in prize money to the winners to support their research.
The ‘Oxynizer’ is a device developed by students Jiwon Kim, Kyeongho Park, Seung-Jun Lee, Jiwon Lee, Yeohyeon Jeong, and Jungwoo Kim under the guidance of Professor Sangmin Bae of KAIST, and is the result of research conducted in the ‘Design Project 1’ class for the graduate students of the Department of Industrial Design at KAIST.
< Photo 3. A photo of Professor Sangmin Bae’s students’ award-winning achievement, ‘Oxynizer’ >
This device was designed to solve the problem of difficulty in supplying oxygen in developing countries due to high installation and maintenance costs. The device was designed to create concentrated oxygen to supply it to a patient in urgent need using an air pump for bicycles, which should be found more easily than a medical oxygen tank.
Professor Sangmin Bae said, “This device creates oxygen using a bicycle air pump and supplies it to patients, and it can separate water vapor and nitrogen in the air using silica gel and zeolite, which are the main materials of the filter, to supply oxygen with a concentration of up to 50%.” “In addition, the filter can be heated and reused after 120 hours of use, so it has the advantage of being able to be used semi-permanently,” he emphasized.
< Photo 4. A photo of Professor Sangmin Bae’s students’ award-winning achievement, ‘Oxynizer’ >
The results of the self-research derived from the KAIST Industrial Design Department class were selected as a world-class award winner and exhibition piece in competition with excellent universities around the world, once again proving the global competitiveness of the KAIST Industrial Design Department.
2024.10.16 View 4381 -
KAIST Succeeds in the Real-time Observation of Organoids using Holotomography
Organoids, which are 3D miniature organs that mimic the structure and function of human organs, play an essential role in disease research and drug development. A Korean research team has overcome the limitations of existing imaging technologies, succeeding in the real-time, high-resolution observation of living organoids.
KAIST (represented by President Kwang Hyung Lee) announced on the 14th of October that Professor YongKeun Park’s research team from the Department of Physics, in collaboration with the Genome Editing Research Center (Director Bon-Kyoung Koo) of the Institute for Basic Science (IBS President Do-Young Noh) and Tomocube Inc., has developed an imaging technology using holotomography to observe live, small intestinal organoids in real time at a high resolution.
Existing imaging techniques have struggled to observe living organoids in high resolution over extended periods and often required additional treatments like fluorescent staining.
< Figure 1. Overview of the low-coherence HT workflow. Using holotomography, 3D morphological restoration and quantitative analysis of organoids can be performed. In order to improve the limited field of view, which is a limitation of the microscope, our research team utilized a large-area field of view combination algorithm and made a 3D restoration by acquiring multi-focus holographic images for 3D measurements. After that, the organoids were compartmentalized to divide the parts necessary for analysis and quantitatively evaluated the protein concentration measurable from the refractive index and the survival rate of the organoids. >
The research team introduced holotomography technology to address these issues, which provides high-resolution images without the need for fluorescent staining and allows for the long-term observation of dynamic changes in real time without causing cell damage.
The team validated this technology using small intestinal organoids from experimental mice and were able to observe various cell structures inside the organoids in detail. They also captured dynamic changes such as growth processes, cell division, and cell death in real time using holotomography.
Additionally, the technology allowed for the precise analysis of the organoids' responses to drug treatments, verifying the survival of the cells.
The researchers believe that this breakthrough will open new horizons in organoid research, enabling the greater utilization of organoids in drug development, personalized medicine, and regenerative medicine.
Future research is expected to more accurately replicate the in vivo environment of organoids, contributing significantly to a more detailed understanding of various life phenomena at the cellular level through more precise 3D imaging.
< Figure 2. Real-time organoid morphology analysis. Using holotomography, it is possible to observe the lumen and villus development process of intestinal organoids in real time, which was difficult to observe with a conventional microscope. In addition, various information about intestinal organoids can be obtained by quantifying the size and protein amount of intestinal organoids through image analysis. >
Dr. Mahn Jae Lee, a graduate of KAIST's Graduate School of Medical Science and Engineering, currently at Chungnam National University Hospital and the first author of the paper, commented, "This research represents a new imaging technology that surpasses previous limitations and is expected to make a major contribution to disease modeling, personalized treatments, and drug development research using organoids."
The research results were published online in the international journal Experimental & Molecular Medicine on October 1, 2024, and the technology has been recognized for its applicability in various fields of life sciences. (Paper title: “Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids via low-coherence holotomography”)
This research was supported by the National Research Foundation of Korea, KAIST Institutes, and the Institute for Basic Science.
2024.10.14 View 3453 -
Professor Jimin Park and Dr. Inho Kim join the ranks of the 2024 "35 Innovators Under 35" by the MIT Technology Review
< (From left) Professor Jimin Park of the Department of Chemical and Biomolecular Engineering and Dr. Inho Kim, a graduate of the Department of Materials Science and Engineering >
KAIST (represented by President Kwang-Hyung Lee) announced on the 13th of September that Professor Jimin Park from KAIST’s Department of Chemical and Biomolecular Engineering and Dr. Inho Kim, a graduate from the Department of Materials Science and Engineering (currently a postdoctoral researcher at Caltech), were selected by the MIT Technology Review as the 2024 "35 Innovators Under 35”.
The MIT Technology Review, first published in 1899 by the Massachusetts Institute of Technology, is the world’s oldest and most influential magazine on science and technology, offering in-depth analysis across various technology fields, expanding knowledge and providing insights into cutting-edge technology trends.
Since 1999, the magazine has annually named 35 innovators under the age of 35, recognizing young talents making groundbreaking contributions in modern technology fields. The recognition is globally considered a prestigious honor and a dream for young researchers in the science and technology community.
< Image 1. Introduction for Professor Jimin Park at the Meet 35 Innovators Under 35 Summit 2024 >
Professor Jimin Park is developing next-generation bio-interfaces that link artificial materials with living organisms, and is engaged in advanced research in areas such as digital healthcare and carbon-neutral compound manufacturing technologies. In 2014, Professor Park was also recognized as one of the ‘Asia Pacific Innovators Under 35’ by the MIT Technology Review, which highlights young scientists in the Asia-Pacific region.
Professor Park responded, “It’s a great honor to be named as one of the young innovators by the MIT Technology Review, a symbol of innovation with a long history. I will continue to pursue challenging, interdisciplinary research to develop next-generation interfaces that seamlessly connect artificial materials and living organisms, from atomic to system levels.”
< Image 2. Introduction for Dr. Inho Kim as the 2024 Innovator of Materials Science for 35 Innovators Under 35 >
Dr. Inho Kim, who earned his PhD from KAIST in 2020 under the supervision of Professor Sang Ouk Kim from the Department of Materials Science and Engineering, recently succeeded in developing a new artificial muscle using composite fibers. This new material is considered the most human-like muscle ever reported in scientific literature, while also being 17 times stronger than natural human muscle.
Dr. Kim is researching the application of artificial muscle fibers in next-generation wearable assistive devices that move more naturally, like humans or animals, noting that the fibers are lightweight, flexible, and exhibit conductivity during contraction, enabling real-time feedback. Recognized for this potential, Dr. Inho Kim was named one of the '35 Innovators Under 35' this year, making him the first researcher to win the honor with the research conducted at KAIST and a PhD earned from Korea.
Dr. Kim stated, “I aim to develop robots using these new materials that can replace today’s expensive and heavy exoskeleton suits by eliminating motors and rigid frames. This will significantly reduce costs and allow for better customization, making cutting-edge technology more accessible to those who need it most, like children with cerebral palsy.”
2024.09.13 View 5390 -
KAIST and NYU set out to Install Korea's First Joint Degree Program in AI
< (From left) New York University President Linda Mills and President Kwang-Hyung Lee >
KAIST (President Kwang-Hyung Lee) and New York University (NYU, President Linda G. Mills) signed an MOU in the afternoon of the 9th to introduce a graduate program for a joint degree in the field of artificial intelligence.
This agreement was promoted based on the consensus between the two universities that strengthening capabilities in the field of AI and fostering global talent are essential elements that can lead to great development in the entire future society beyond simple technical education.
The two universities have been operating joint research groups in various industrial fields related to AI and convergence with it, and based on this agreement, they plan to establish an operating committee within this year to design a joint degree program for graduate school courses related to artificial intelligence.
A KAIST official said, “If the joint degree program in AI is implemented, it is expected to be an unprecedented innovative experiment in which KAIST and NYU join forces to create ‘a single AI degree.’
The committee will consist of an equal number of faculty members from both schools, and will discuss the overall strategic planning of the joint degree program, including ▴curriculum structure and course composition ▴course completion roadmap ▴calculation of faculty and student population ▴calculation of budget size ▴calculation of operating facility size and details ▴legal matters regarding certification. In addition, the development of a new logo symbolizing the joint degree of KAIST and NYU in AI will also be carried out.
The two schools expect that the joint degree program being promoted this time will contribute to advancing education and research capabilities in the field of artificial intelligence, jointly discovering and fostering talent in related fields that are currently lacking worldwide, and will become an exemplary case of global education and research cooperation.
The faculty members of both schools, who possess excellent capabilities, will provide innovative and creative education in the field of artificial intelligence. Students will receive support to gain top-level research experience by participating in various international joint research projects promoted by the faculty members of both schools. Through this, the core of this joint degree program promoted by both schools is to continuously cultivate excellent human resources who will lead the future global society.
Since signing a cooperation agreement for the establishment of a joint campus in June 2022, KAIST and NYU have been promoting campus sharing, joint research, and joint bachelor's degree programs. Including this, they are developing an innovative joint campus model and establishing an active international cooperation model.
In particular, the exchange student system for undergraduate students will be implemented starting from the second semester of the 2023 academic year. 30 students from KAIST and 11 students from NYU were selected through a competitive selection process and are participating. In the case of KAIST students, if they complete one of the six minor programs at NYU, they will receive a degree that states the completion of the minor upon graduation.
Based on the performance of the undergraduate exchange student operation, the two schools have also agreed to introduce a dual degree system for master's and doctoral students, and specific procedures are currently in progress.
In addition, from 2023 to the present, we are carrying out future joint research projects in 15 fields that are integrated with AI, and we plan to begin international joint research in 10 fields centered on AI and bio from the fourth quarter of this year.
NYU President Linda Mills said, “AI technology can play a significant role in addressing various social challenges such as climate change, health care, and education inequality,” and added that, “The global talent cultivated through our two schools will also go on to make innovative contributions to solving these social problems.”
Kwang-Hyung Lee, the president of KAIST, said, “In the era of competition for global hegemony in technology, the development of AI technology is an essential element for countries and companies to secure competitiveness,” and “Through long-term cooperation with NYU, we will take the lead in fostering world-class, advanced talents who can innovatively apply and develop AI in various fields.”
The signing ceremony held at the Four Seasons Hotel in Seoul was attended by KAIST officials including President Kwang-Hyung Lee, Hyun Deok Yeo, the Director of G-School, NYU officials including President Linda Mills, Kyunghyun Cho, a Professor of Computer Science and Data Science, and Dr. Karin Pavese, the Executive Director of NYU-KAIST Innovation Research Institute, amid attendance by other key figures from the industries situated in Korea. (End)
2024.09.10 View 4689 -
Unraveling Mitochondrial DNA Mutations in Human Cells
Throughout our lifetime, cells accumulate DNA mutations, which contribute to genetic diversity, or “mosaicism”, among cells. These genomic mutations are pivotal for the aging process and the onset of various diseases, including cancer. Mitochondria, essential cellular organelles involved in energy metabolism and apoptosis, possess their own DNA, which are susceptible to mutations. However, studies on mtDNA mutations and mosaicism have been limited due to a variety of technical challenges.
Genomic scientists from KAIST have revealed the genetic mosaicism characterized by variations in mitochondrial DNA (mtDNA) across normal human cells. This study provides fundamental insights into understanding human aging and disease onset mechanisms.
The study, “Mitochondrial DNA mosaicism in normal human somatic cells,” was published in Nature Genetics on July 22. It was led by graduate student Jisong An under the supervision of Professor Young Seok Ju from the Graduate School of Medical Science and Engineering.
Researchers from Seoul National University College of Medicine, Yonsei University College of Medicine, Korea University College of Medicine, Washington University School of Medicine National Cancer Center, Seoul National University Hospital, Gangnam Severance Hospital and KAIST faculty startup company Inocras Inc. also participated in this study.
< Figure 1. a. Flow of experiment. b. Schematic diagram illustrating the origin and dynamics of mtDNA alterations across a lifetime. >
The study involved a bioinformatic analysis of whole-genome sequences from 2,096 single cells obtained from normal human colorectal epithelial tissue, fibroblasts, and blood collected from 31 individuals. The study highlights an average of three significant mtDNA differences between cells, with approximately 6% of these variations confirmed to be inherited as heteroplasmy from the mother.
Moreover, mutations significantly increased during tumorigenesis, with some mutations contributing to instability in mitochondrial RNA. Based on these findings, the study illustrates a computational model that comprehensively elucidates the evolution of mitochondria from embryonic development to aging and tumorigenesis.
This study systematically reveals the mechanisms behind mitochondrial DNA mosaicism in normal human cells, establishing a crucial foundation for understanding the impact of mtDNA on aging and disease onset.
Professor Ju remarked, “By systematically utilizing whole-genome big data, we can illuminate previously unknown phenomena in life sciences.” He emphasized the significance of the study, adding, “For the first time, we have established a method to systematically understand mitochondrial DNA changes occurring during human embryonic development, aging, and cancer development.”
This work was supported by the National Research Foundation of Korea and the Suh Kyungbae Foundation.
2024.07.24 View 4150 -
KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 5096 -
KAIST President Kwang-Hyung Lee receives honorary doctorate from Université de Montréal
KAIST (President Kwang-Hyung Lee) announced on June 16th that President Kwang-Hyung Lee received an honorary doctorate on the 15th, local time, from the Université de Montréal in Canada, one of the largest French-speaking universities in North America.
< Image. (from left) Mr. Pierre Lassonde, Chairman of the Board of Polytechnique Montréal, President Maud Cohen of Polytechnique Montréal, President Kwang-Hyung Lee of KAIST, Chancellor Frantz Saintellemy of Université de Montréal and Mr. Alexandre Chabot, Secretary General of Université de Montéal. >
President Lee was selected as the recipient of the honorary doctorate from the Université de Montréal upon the recommendation of Polytechnique Montréal in recognition of his contributions in pioneering the multidisciplinary approach to integrate a number of fields studies including computer science, biology, and nanotechnology.
Polytechnique Montréal is a university in affiliation with the University of Montréal and is one of the largest engineering education and research institutions of Canada. President Lee's honorary doctorate was awarded at the Convocation Ceremony of Polytechnique Montréal held for the Class of 2024. On this day, Mr. Serge Gendron, a businessman, a philanthropist and an alum of Polytechnique Montréal, also had the honor of receiving an honorary doctorate along with President Lee.
President Kwang-Hyung Lee is internationally recognized for his contributions in various fields, including engineering education, multidisciplinary research, strategy establishment, and future prospects. President Lee is also well known to have had significant influence on the first-generation venture entrepreneurs, a large portion of which are from KAIST, who have now grown into full-fledged entrepreneurs. For these activities, President Lee received numerous decorations and commendations within Korea, including the National Order of Civil Merit - Camellia Medal, and in 2003, he received the ‘Légion d’Honneur Chevalier’ from the French government as well.
Through his speech at the ceremony, KAIST President Kwang-Hyung Lee expressed his gratitude to the Université de Montréal and Polytechnique Montréal, while congratulating and encouraging the graduates who are poised to start anew as they part from the school. “Hold on to your dreams, try looking at the world from a different perspective, and enjoy the challenges without being afraid of failures.” With these three pieces of advice, President Lee cheered on the graduates saying, “The future belongs to those of you who challenge them.”
Maud Cohen, the President of Polytechnique Montréal, commented on President Kwang-Hyung Lee's honorary doctorate, that Polytechnique Montréal is proud to award an honorary doctorate to Mr. Lee for his exceptional career path, his holistic, multidisciplinary and undeniably forward-looking vision, which strongly echoes the values of Polytechnique Montréal, and for his involvement in and commitment to education, research and the future of the next generation.
* Established in 1873, Polytechnique Montréal is one of Canada’s largest engineering education and research universities, and is located on the Université de Montréal campus – North America’s largest Francophone university campus. Joshua Bengio, who won the Turing Award for establishing the foundations of deep learning, is gaining international recognition in artificial intelligence and other related fields at Polytechnique Montréal. Polytechnique Montréal chose KAIST as the first Korean university establish partnership with and has continued to build up close cooperative relationship since 1998.
* The Université de Montréal (UdeM) is a public university founded in 1878. It is located in Montréal, in the French-speaking province of Québec, Canada. It is one of Canada's five major universities, and the second largest in terms of student enrollment. The Université de Montréal is the largest in the French-speaking world in terms of both student enrollment and research. The Université de Montréal enjoys an excellent reputation as one of the best French-language post-secondary institutions. Its rector is Mr. Daniel Jutras.
2024.06.16 View 5276