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KAIST Holds 2025 Commencement Ceremony
KAIST (President Kwang-Hyung Lee) held its 2025 Commencement Ceremony at the Lyu Keun-Chul Sports Complex on the Daejeon Main Campus at 2 p.m. on the 14th of February. < A scene from KAIST Commencement 2025 - Guests of Honor and Administrative Professors Entering the Stage headed by the color guards of the ELKA (Encouraging Leaders of KAIST) > At this ceremony, a total of 3,144 degrees were conferred, including 785 doctorates, 1,643 masters, and 716 bachelors. With this, KAIST has produced a total of 81,156 advanced science and technology personnel, including 17,313 doctorates, 41,566 masters, and 22,277 bachelors since its establishment in 1971. Changyu Lee from the School of Computing received the Minister of Science and ICT Award, and the Chairman of the KAIST Board of Trustees Award went to Lance Khizner Dabu Gragasin, an international student from the Philippines of the Department of Chemical and Biological Engineering. The President’s Award was given to Seoyeong Yang of the Department of Biological Sciences, and the Alumni Association President’s Award and the Development Foundation Chairman’s Award was given to Gahyeon Bae of the Department of Industrial Design and Buyeon Kim of the Department of Mechanical Engineering, respectively. Minister of Science and ICT Sang-Im Yoo joined the ceremony to deliver a congratulatory speech and to present the awards to outstanding graduates. < Minister Sang-Im Yoo of the Ministry of Science, Technology and ICT giving his congratulatory message at KAIST Commencement 2025 > The valedictorian speeches were given by Minjae Kim of the School of Computing, who has practiced the value of sharing that learning is not competition but cooperation, and Mohammed Haruna Hamza of the Department of Aerospace Engineering, a Nigerian international student. Mr. Hamza is the first foreign student to represent the graduating class as valedictorian since the founding of KAIST. Hamza lost his home and school in his home country due to a terrorist group’s bombing and moved south, but despite the adversity, he continued his studies while pursuing his dream of becoming an aerospace engineer. As a result of his efforts, Hamza was invited by the Korean government to study at KAIST. He expressed his determination to pursue his dream by saying, “I am grateful for the people and experiences that helped me overcome my adversity. The future is the result of the decisions we make today.” A Pakistani international student was chosen as one of this year's "Most Talked about Graduates of the Year". It is Ali Syed Sheraz who wore his doctoral cap at this year’s commencement ceremony. Ali, a single father who left his one-year-old son behind in his home country, working as a university lecturer. He joined the Ph.D. program in mechanical engineering in 2019 with a passion for mechanical energy. Ali’s academic journey was full of challenges and growth. Due to COVID-19, his research was suspended for six months, and he had difficulty continuing his studies undergoing three surgeries after a bicycle accident, including a surgery for a fractured elbow, a nose surgery, and removal of kidney stones. However, he accepted these failure and hardship as a process of growth and participated in the ‘Failed Project Showcase’ and ‘Failure Essay Contest’ held by the KAIST Failure Society, sharing his experiences and growing into a more solid researcher. < Most Talked about Graduate Graduate of the Year - Syed Sheraz Ali > Despite experiencing various hardships, he found lessons to learn from them and changed his perspective, which made him unafraid of taking on new challenges. He showed through his own example that failure is not just stumbling blocks but can be a stepping stone to success by looking at his studies and personal life positively. Furthermore, after becoming the president of the Muslim Student Association, Ali introduced halal menus to the cafeteria on campus so that more Muslim students could eat comfortably. Thanks to this change, his time at KAIST has become an opportunity to understand and experience various cultures more. Ali is researching artificial muscles (soft actuators) with the world's highest bending strain using MXene, an artificial muscle nanomaterial that can move smoothly, in Professor Il-kwon Oh's lab. Ali said, "After completing my Ph.D., I plan to develop soft robots, healthcare electronics, and next-generation tactile technology based on MXene, a next-generation 2D material. It is important for my juniors not to be afraid of failure and to have a challenging attitude." Another 'Most Talked about Graduate of the Year', Mr. Sung-Hyun Jung, who graduated with a master's degree from the Graduate School of Bio Innvation Management, is the CEO of Promedius, a medical AI startup, and has commercialized an osteoporosis diagnosis software based on chest X-rays using AI, and grown it into a leading company in the bone health field. CEO Jung's challenge shows that KAIST's management education is not just theoretical but practical enough to be applied immediately in the field. CEO Jung, who is also the father of three daughters, experienced business failure in China during the period when the conflict between Korea and China was intensifying. He moved to Silicon Valley in the United States to revive his business and tried to acquire even small businesses, but the reality was not easy. He worked hard, standing 14 hours a day in a kimchi factory and a restaurant kitchen to make a living. After finishing his life in the United States, CEO Jung returned to Korea and had the opportunity to join Lunit, a global medical AI leader founded by KAIST graduates. CEO Jung experienced the growth of the global medical AI market firsthand with unit Chairman Seungwook Paek. When he entered the Master's Program at the Graduate School of Bio Innvation Management in 2023 to acquire more specialized knowledge, CEO Jung had just transferred to Promedius and was in a crisis situation with only about 6 months left before the company's funds were exhausted. While considering a change in business direction because he judged that it would be difficult to survive with existing business items, he learned keywords and investment review perspectives that venture capital (VC) pays attention to in Professor Hoonje Cho’s ‘Bio-innovation Business Startup Strategy and Practice’ class. He attracted 11.4 billion won in investment by applying the investment proposal he wrote based on what he learned from the class to actual practice. < Most Talked about Graduate of the Year - Sung-Hyun Jung > In addition, he applied the innovation strategy in the medical field he learned in Professor Kihwan Park’s ‘Innovation and Marketing in Bio and Pharmaceutics’ to the field of osteoporosis, and achieved the result of being selected as the first Asian company to be a corporate advisory committee member of the International Osteoporosis Foundation (IOF). Through this, he established the company as a representative global entity in the osteoporosis field in just one year. CEO Jung, who applied what he learned from KAIST to actual management and achieved results in the global market in a short period of time, said, “I want to prove that KAIST education is not limited to theory, but is very practical.” He said, “I want to let people know that my life, once full of hardship, got on the track toward success after encountering KAIST,” and expressed his ambition, saying, “My long-term goal is to create a world-class company that is recognized globally.” In addition, an honorary doctorate was awarded to Chairman Joong Keun Lee of Booyoung Group at the commencement ceremony. Chairman Joong Keun Lee, who is an entrepreneur that led Booyoung Group, a leading general construction company, received the honorary doctorate in business administration, for leading the development of domestic housing welfare, education, and culture. KAIST Provost Gyunmin Lee said, “Chairman Joong Keun Lee spared no effort in providing dedicated support for the development of domestic science and technology and the cultivation of future talents. He is awarded the honorary doctorate in recognition of his social responsibility in various fields, including scholarships and support for educational facilities, as well as domestic and international education, culture, veterans affairs, and overseas support.” Since founding Booyoung Group in 1983, Chairman Lee has boldly entered the rental housing business, a field that large construction companies had avoided, and has played a significant role in improving the quality of life of ordinary citizens by supplying 230,000 households out of 383 complexes and approximately 300,000 households nationwide as rental housing, thereby contributing greatly to the stability of national housing. < Chairman Joong Keun Lee giving his acceptance speech for his honorary Doctorate > Chairman Joong Keun Lee, who has been offering hope for a sustainable future, said, “I am honored to receive an honorary doctorate from KAIST, and I hope that KAIST students will nurture their dreams and talents and grow into global talents who will contribute to national development.” President Kwang-Hyung Lee said, “Chairman Joong Keun Lee has been carrying out various social contribution activities, and in particular, through supporting academic infrastructure, which is the core of national competitiveness, we can see his deep interest in and sense of responsibility for the development of science and technology in our country.” He added, “I am truly delighted to have him as a member of the KAIST family, and I congratulate him on behalf of all members, including our students.” President Kwang-Hyung Lee also delivered a message of encouragement at the ceremony to charge the graduates to, “Find and keep a dream of your own, be on the lookout for opportunities, don’t be afraid of making mistakes, and do not shy away from taking on challenging tasks.” He added, “Even if you fail, don’t give up. Keep on trying so that you will get to that stage of radiate your own light on the stages where anything is possible.” (End)
2025.02.14
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KAIST Develops Wearable Carbon Dioxide Sensor to Enable Real-time Apnea Diagnosis
- Professor Seunghyup Yoo’s research team of the School of Electrical Engineering developed an ultralow-power carbon dioxide (CO2) sensor using a flexible and thin organic photodiode, and succeeded in real-time breathing monitoring by attaching it to a commercial mask - Wearable devices with features such as low power, high stability, and flexibility can be utilized for early diagnosis of various diseases such as chronic obstructive pulmonary disease and sleep apnea < Photo 1. From the left, School of Electrical Engineering, Ph.D. candidate DongHo Choi, Professor Seunghyup Yoo, and Department of Materials Science and Engineering, Bachelor’s candidate MinJae Kim > Carbon dioxide (CO2) is a major respiratory metabolite, and continuous monitoring of CO2 concentration in exhaled breath is not only an important indicator for early detection and diagnosis of respiratory and circulatory system diseases, but can also be widely used for monitoring personal exercise status. KAIST researchers succeeded in accurately measuring CO2 concentration by attaching it to the inside of a mask. KAIST (President Kwang-Hyung Lee) announced on February 10th that Professor Seunghyup Yoo's research team in the Department of Electrical and Electronic Engineering developed a low-power, high-speed wearable CO2 sensor capable of stable breathing monitoring in real time. Existing non-invasive CO2 sensors had limitations in that they were large in size and consumed high power. In particular, optochemical CO2 sensors using fluorescent molecules have the advantage of being miniaturized and lightweight, but due to the photodegradation phenomenon of dye molecules, they are difficult to use stably for a long time, which limits their use as wearable healthcare sensors. Optochemical CO2 sensors utilize the fact that the intensity of fluorescence emitted from fluorescent molecules decreases depending on the concentration of CO2, and it is important to effectively detect changes in fluorescence light. To this end, the research team developed a low-power CO2 sensor consisting of an LED and an organic photodiode surrounding it. Based on high light collection efficiency, the sensor, which minimizes the amount of excitation light irradiated on fluorescent molecules, achieved a device power consumption of 171 μW, which is tens of times lower than existing sensors that consume several mW. < Figure 1. Structure and operating principle of the developed optochemical carbon dioxide (CO2) sensor. Light emitted from the LED is converted into fluorescence through the fluorescent film, reflected from the light scattering layer, and incident on the organic photodiode. CO2 reacts with a small amount of water inside the fluorescent film to form carbonic acid (H2CO3), which increases the concentration of hydrogen ions (H+), and the fluorescence intensity due to 470 nm excitation light decreases. The circular organic photodiode with high light collection efficiency effectively detects changes in fluorescence intensity, lowers the power required light up the LED, and reduces light-induced deterioration. > The research team also elucidated the photodegradation path of fluorescent molecules used in CO2 sensors, revealed the cause of the increase in error over time in photochemical sensors, and suggested an optical design method to suppress the occurrence of errors. Based on this, the research team developed a sensor that effectively reduces errors caused by photodegradation, which was a chronic problem of existing photochemical sensors, and can be used continuously for up to 9 hours while existing technologies based on the same material can be used for less than 20 minutes, and can be used multiple times when replacing the CO2 detection fluorescent film. < Figure 2. Wearable smart mask and real-time breathing monitoring. The fabricated sensor module consists of four elements (①: gas-permeable light-scattering layer, ②: color filter and organic photodiode, ③: light-emitting diode, ④: CO2-detecting fluorescent film). The thin and light sensor (D1: 400 nm, D2: 470 nm) is attached to the inside of the mask to monitor the wearer's breathing in real time. > The developed sensor accurately measured CO2 concentration by being attached to the inside of a mask based on the advantages of being light (0.12 g), thin (0.7 mm), and flexible. In addition, it showed fast speed and high resolution that can monitor respiratory rate by distinguishing between inhalation and exhalation in real time. < Photo 2. The developed sensor attached to the inside of the mask > Professor Seunghyup Yoo said, "The developed sensor has excellent characteristics such as low power, high stability, and flexibility, so it can be widely applied to wearable devices, and can be used for the early diagnosis of various diseases such as hypercapnia, chronic obstructive pulmonary disease, and sleep apnea." He added, "In particular, it is expected to be used to improve side effects caused by rebreathing in environments where dust is generated or where masks are worn for long periods of time, such as during seasonal changes." This study, in which KAIST's Department of Materials Science and Engineering's undergraduate student Minjae Kim and School of Electrical Engineering's doctoral student Dongho Choi participated as joint first authors, was published in the online version of Cell's sister journal, Device, on the 22nd of last month. (Paper title: Ultralow-power carbon dioxide sensor for real-time breath monitoring) DOI: https://doi.org/10.1016/j.device.2024.100681 < Photo 3. From the left, Professor Seunghyup Yoo of the School of Electrical Engineering, MinJae Kim, an undergraduate student in the Department of Materials Science and Engineering, and Dongho Choi, a doctoral student in the School of Electrical Engineering > This study was supported by the Ministry of Trade, Industry and Energy's Materials and Components Technology Development Project, the National Research Foundation of Korea's Original Technology Development Project, and the KAIST Undergraduate Research Participation Project. This work was supported by the (URP) program.
2025.02.13
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KAIST Discovers Molecular Switch that Reverses Cancerous Transformation at the Critical Moment of Transition
< (From left) PhD student Seoyoon D. Jeong, (bottom) Professor Kwang-Hyun Cho, (top) Dr. Dongkwan Shin, Dr. Jeong-Ryeol Gong > Professor Kwang-Hyun Cho’s research team has recently been highlighted for their work on developing an original technology for cancer reversal treatment that does not kill cancer cells but only changes their characteristics to reverse them to a state similar to normal cells. This time, they have succeeded in revealing for the first time that a molecular switch that can induce cancer reversal at the moment when normal cells change into cancer cells is hidden in the genetic network. KAIST (President Kwang-Hyung Lee) announced on the 5th of February that Professor Kwang-Hyun Cho's research team of the Department of Bio and Brain Engineering has succeeded in developing a fundamental technology to capture the critical transition phenomenon at the moment when normal cells change into cancer cells and analyze it to discover a molecular switch that can revert cancer cells back into normal cells. A critical transition is a phenomenon in which a sudden change in state occurs at a specific point in time, like water changing into steam at 100℃. This critical transition phenomenon also occurs in the process in which normal cells change into cancer cells at a specific point in time due to the accumulation of genetic and epigenetic changes. The research team discovered that normal cells can enter an unstable critical transition state where normal cells and cancer cells coexist just before they change into cancer cells during tumorigenesis, the production or development of tumors, and analyzed this critical transition state using a systems biology method to develop a cancer reversal molecular switch identification technology that can reverse the cancerization process. They then applied this to colon cancer cells and confirmed through molecular cell experiments that cancer cells can recover the characteristics of normal cells. This is an original technology that automatically infers a computer model of the genetic network that controls the critical transition of cancer development from single-cell RNA sequencing data, and systematically finds molecular switches for cancer reversion by simulation analysis. It is expected that this technology will be applied to the development of reversion therapies for other cancers in the future. Professor Kwang-Hyun Cho said, "We have discovered a molecular switch that can revert the fate of cancer cells back to a normal state by capturing the moment of critical transition right before normal cells are changed into an irreversible cancerous state." < Figure 1. Overall conceptual framework of the technology that automatically constructs a molecular regulatory network from single-cell RNA sequencing data of colon cancer cells to discover molecular switches for cancer reversion through computer simulation analysis. Professor Kwang-Hyun Cho's research team established a fundamental technology for automatic construction of a computer model of a core gene network by analyzing the entire process of tumorigenesis of colon cells turning into cancer cells, and developed an original technology for discovering the molecular switches that can induce cancer cell reversal through attractor landscape analysis. > He continued, "In particular, this study has revealed in detail, at the genetic network level, what changes occur within cells behind the process of cancer development, which has been considered a mystery until now." He emphasized, "This is the first study to reveal that an important clue that can revert the fate of tumorigenesis is hidden at this very critical moment of change." < Figure 2. Identification of tumor transition state using single-cell RNA sequencing data from colorectal cancer. Using single-cell RNA sequencing data from colorectal cancer patient-derived organoids for normal and cancerous tissues, a critical transition was identified in which normal and cancerous cells coexist and instability increases (a-d). The critical transition was confirmed to show intermediate levels of major phenotypic features related to cancer or normal tissues that are indicative of the states between the normal and cancerous cells (e). > The results of this study, conducted by KAIST Dr. Dongkwan Shin (currently at the National Cancer Center), Dr. Jeong-Ryeol Gong, and doctoral student Seoyoon D. Jeong jointly with a research team at Seoul National University that provided the organoids (in vitro cultured tissues) from colon cancer patient, were published as an online paper in the international journal ‘Advanced Science’ published by Wiley on January 22nd. (Paper title: Attractor landscape analysis reveals a reversion switch in the transition of colorectal tumorigenesis) (DOI: https://doi.org/10.1002/advs.202412503) < Figure 3. Reconstruction of a dynamic network model for the transition state of colorectal cancer. A new technology was established to build a gene network computer model that can simulate the dynamic changes between genes by integrating single-cell RNA sequencing data and existing experimental results on gene-to-gene interactions in the critical transition of cancer. (a). Using this technology, a gene network computer model for the critical transition of colorectal cancer was constructed, and the distribution of attractors representing normal and cancer cell phenotypes was investigated through attractor landscape analysis (b-e). > This study was conducted with the support of the National Research Foundation of Korea under the Ministry of Science and ICT through the Mid-Career Researcher Program and Basic Research Laboratory Program and the Disease-Centered Translational Research Project of the Korea Health Industry Development Institute (KHIDI) of the Ministry of Health and Welfare. < Figure 4. Quantification of attractor landscapes and discovery of transcription factors for cancer reversibility through perturbation simulation analysis. A methodology for implementing discontinuous attractor landscapes continuously from a computer model of gene networks and quantifying them as cancer scores was introduced (a), and attractor landscapes for the critical transition of colorectal cancer were secured (b-d). By tracking the change patterns of normal and cancer cell attractors through perturbation simulation analysis for each gene, the optimal combination of transcription factors for cancer reversion was discovered (e-h). This was confirmed in various parameter combinations as well (i). > < Figure 5. Identification and experimental validation of the optimal target gene for cancer reversion. Among the common target genes of the discovered transcription factor combinations, we identified cancer reversing molecular switches that are predicted to suppress cancer cell proliferation and restore the characteristics of normal colon cells (a-d). When inhibitors for the molecular switches were treated to organoids derived from colon cancer patients, it was confirmed that cancer cell proliferation was suppressed and the expression of key genes related to cancer development was inhibited (e-h), and a group of genes related to normal colon epithelium was activated and transformed into a state similar to normal colon cells (i-j). > < Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed an original technology to systematically discover key molecular switches that can induce reversion of colon cancer cells through a systems biology approach using an attractor landscape analysis of a genetic network model for the critical transition at the moment of transformation from normal cells to cancer cells, and verified the reversing effect of actual colon cancer through cellular experiments. >
2025.02.05
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KAIST Develops AI-Driven Performance Prediction Model to Advance Space Electric Propulsion Technology
< (From left) PhD candidate Youngho Kim, Professor Wonho Choe, and PhD candidate Jaehong Park from the Department of Nuclear and Quantum Engineering > Hall thrusters, a key space technology for missions like SpaceX's Starlink constellation and NASA's Psyche asteroid mission, are high-efficiency electric propulsion devices using plasma technology*. The KAIST research team announced that the AI-designed Hall thruster developed for CubeSats will be installed on the KAIST-Hall Effect Rocket Orbiter (K-HERO) CubeSat to demonstrate its in-orbit performance during the fourth launch of the Korean Launch Vehicle called Nuri rocket (KSLV-2) scheduled for November this year. *Plasma is one of the four states of matter, where gases are heated to high energies, causing them to separate into charged ions and electrons. Plasma is used not only in space electric propulsion but also in semiconductor manufacturing, display processes, and sterilization devices. On February 3rd, the research team from the KAIST Department of Nuclear and Quantum Engineering’s Electric Propulsion Laboratory, led by Professor Wonho Choe, announced the development of an AI-based technique to accurately predict the performance of Hall thrusters, the engines of satellites and space probes. Hall thrusters provide high fuel efficiency, requiring minimal propellant to achieve significant acceleration of spacecrafts or satellites while producing substantial thrust relative to power consumption. Due to these advantages, Hall thrusters are widely used in various space missions, including the formation flight of satellite constellations, deorbiting maneuvers for space debris mitigation, and deep space missions such as asteroid exploration. As the space industry continues to grow during the NewSpace era, the demand for Hall thrusters suited to diverse missions is increasing. To rapidly develop highly efficient, mission-optimized Hall thrusters, it is essential to predict thruster performance accurately from the design phase. However, conventional methods have limitations, as they struggle to handle the complex plasma phenomena within Hall thrusters or are only applicable under specific conditions, leading to lower prediction accuracy. The research team developed an AI-based performance prediction technique with high accuracy, significantly reducing the time and cost associated with the iterative design, fabrication, and testing of thrusters. Since 2003, Professor Wonho Choe’s team has been leading research on electric propulsion development in Korea. The team applied a neural network ensemble model to predict thruster performance using 18,000 Hall thruster training data points generated from their in-house numerical simulation tool. The in-house numerical simulation tool, developed to model plasma physics and thrust performance, played a crucial role in providing high-quality training data. The simulation’s accuracy was validated through comparisons with experimental data from ten KAIST in-house Hall thrusters, with an average prediction error of less than 10%. < Figure 1. This research has been selected as the cover article for the March 2025 issue (Volume 7, Issue 3) of the AI interdisciplinary journal, Advanced Intelligent Systems. > The trained neural network ensemble model acts as a digital twin, accurately predicting the Hall thruster performance within seconds based on thruster design variables. Notably, it offers detailed analyses of performance parameters such as thrust and discharge current, accounting for Hall thruster design variables like propellant flow rate and magnetic field—factors that are challenging to evaluate using traditional scaling laws. This AI model demonstrated an average prediction error of less than 5% for the in-house 700 W and 1 kW KAIST Hall thrusters and less than 9% for a 5 kW high-power Hall thruster developed by the University of Michigan and the U.S. Air Force Research Laboratory. This confirms the broad applicability of the AI prediction method across different power levels of Hall thrusters. Professor Wonho Choe stated, “The AI-based prediction technique developed by our team is highly accurate and is already being utilized in the analysis of thrust performance and the development of highly efficient, low-power Hall thrusters for satellites and spacecraft. This AI approach can also be applied beyond Hall thrusters to various industries, including semiconductor manufacturing, surface processing, and coating, through ion beam sources.” < Figure 2. The AI-based prediction technique developed by the research team accurately predicts thrust performance based on design variables, making it highly valuable for the development of high-efficiency Hall thrusters. The neural network ensemble processes design variables, such as channel geometry and magnetic field information, and outputs key performance metrics like thrust and prediction accuracy, enabling efficient thruster design and performance analysis. > Additionally, Professor Choe mentioned, “The CubeSat Hall thruster, developed using the AI technique in collaboration with our lab startup—Cosmo Bee, an electric propulsion company—will be tested in orbit this November aboard the K-HERO 3U (30 x 10 x 10 cm) CubeSat, scheduled for launch on the fourth flight of the KSLV-2 Nuri rocket.” This research was published online in Advanced Intelligent Systems on December 25, 2024 with PhD candidate Jaehong Park as the first author and was selected as the journal’s cover article, highlighting its innovation. < Figure 3. Image of the 150 W low-power Hall thruster for small and micro satellites, developed in collaboration with Cosmo Bee and the KAIST team. The thruster will be tested in orbit on the K-HERO CubeSat during the KSLV-2 Nuri rocket’s fourth launch in Q4 2025. > This research was supported by the National Research Foundation of Korea’s Space Pioneer Program (200mN High Thrust Electric Propulsion System Development). (Paper Title: Predicting Performance of Hall Effect Ion Source Using Machine Learning, DOI: https://doi.org/10.1002/aisy.202400555 ) < Figure 4. Graphs of the predicted thrust and discharge current of KAIST’s 700 W Hall thruster using the AI model (HallNN). The left image shows the Hall thruster operating in KAIST Electric Propulsion Laboratory’s vacuum chamber, while the center and right graphs present the prediction results for thrust and discharge current based on anode mass flow rate. The red lines represent AI predictions, and the blue dots represent experimental results, with a prediction error of less than 5%. >
2025.02.03
View 992
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
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A Way for Smartwatches to Detect Depression Risks Devised by KAIST and U of Michigan Researchers
- A international joint research team of KAIST and the University of Michigan developed a digital biomarker for predicting symptoms of depression based on data collected by smartwatches - It has the potential to be used as a medical technology to replace the economically burdensome fMRI measurement test - It is expected to expand the scope of digital health data analysis The CORONA virus pandemic also brought about a pandemic of mental illness. Approximately one billion people worldwide suffer from various psychiatric conditions. Korea is one of more serious cases, with approximately 1.8 million patients exhibiting depression and anxiety disorders, and the total number of patients with clinical mental diseases has increased by 37% in five years to approximately 4.65 million. A joint research team from Korea and the US has developed a technology that uses biometric data collected through wearable devices to predict tomorrow's mood and, further, to predict the possibility of developing symptoms of depression. < Figure 1. Schematic diagram of the research results. Based on the biometric data collected by a smartwatch, a mathematical algorithm that solves the inverse problem to estimate the brain's circadian phase and sleep stages has been developed. This algorithm can estimate the degrees of circadian disruption, and these estimates can be used as the digital biomarkers to predict depression risks. > KAIST (President Kwang Hyung Lee) announced on the 15th of January that the research team under Professor Dae Wook Kim from the Department of Brain and Cognitive Sciences and the team under Professor Daniel B. Forger from the Department of Mathematics at the University of Michigan in the United States have developed a technology to predict symptoms of depression such as sleep disorders, depression, loss of appetite, overeating, and decreased concentration in shift workers from the activity and heart rate data collected from smartwatches. According to WHO, a promising new treatment direction for mental illness focuses on the sleep and circadian timekeeping system located in the hypothalamus of the brain, which directly affect impulsivity, emotional responses, decision-making, and overall mood. However, in order to measure endogenous circadian rhythms and sleep states, blood or saliva must be drawn every 30 minutes throughout the night to measure changes in the concentration of the melatonin hormone in our bodies and polysomnography (PSG) must be performed. As such treatments requires hospitalization and most psychiatric patients only visit for outpatient treatment, there has been no significant progress in developing treatment methods that take these two factors into account. In addition, the cost of the PSG test, which is approximately $1000, leaves mental health treatment considering sleep and circadian rhythms out of reach for the socially disadvantaged. The solution to overcome these problems is to employ wearable devices for the easier collection of biometric data such as heart rate, body temperature, and activity level in real time without spatial constraints. However, current wearable devices have the limitation of providing only indirect information on biomarkers required by medical staff, such as the phase of the circadian clock. The joint research team developed a filtering technology that accurately estimates the phase of the circadian clock, which changes daily, such as heart rate and activity time series data collected from a smartwatch. This is an implementation of a digital twin that precisely describes the circadian rhythm in the brain, and it can be used to estimate circadian rhythm disruption. < Figure 2. The suprachiasmatic nucleus located in the hypothalamus of the brain is the central biological clock that regulates the 24-hour physiological rhythm and plays a key role in maintaining the body’s circadian rhythm. If the phase of this biological clock is disrupted, it affects various parts of the brain, which can cause psychiatric conditions such as depression. > The possibility of using the digital twin of this circadian clock to predict the symptoms of depression was verified through collaboration with the research team of Professor Srijan Sen of the Michigan Neuroscience Institute and Professor Amy Bohnert of the Department of Psychiatry of the University of Michigan. The collaborative research team conducted a large-scale prospective cohort study involving approximately 800 shift workers and showed that the circadian rhythm disruption digital biomarker estimated through the technology can predict tomorrow's mood as well as six symptoms, including sleep problems, appetite changes, decreased concentration, and suicidal thoughts, which are representative symptoms of depression. < Figure 3. The circadian rhythm of hormones such as melatonin regulates various physiological functions and behaviors such as heart rate and activity level. These physiological and behavioral signals can be measured in daily life through wearable devices. In order to estimate the body’s circadian rhythm inversely based on the measured biometric signals, a mathematical algorithm is needed. This algorithm plays a key role in accurately identifying the characteristics of circadian rhythms by extracting hidden physiological patterns from biosignals. > Professor Dae Wook Kim said, "It is very meaningful to be able to conduct research that provides a clue for ways to apply wearable biometric data using mathematics that have not previously been utilized for actual disease management." He added, "We expect that this research will be able to present continuous and non-invasive mental health monitoring technology. This is expected to present a new paradigm for mental health care. By resolving some of the major problems socially disadvantaged people may face in current treatment practices, they may be able to take more active steps when experiencing symptoms of depression, such as seeking counsel before things get out of hand." < Figure 4. A mathematical algorithm was devised to circumvent the problems of estimating the phase of the brain's biological clock and sleep stages inversely from the biodata collected by a smartwatch. This algorithm can estimate the degree of daily circadian rhythm disruption, and this estimate can be used as a digital biomarker to predict depression symptoms. > The results of this study, in which Professor Dae Wook Kim of the Department of Brain and Cognitive Sciences at KAIST participated as the joint first author and corresponding author, were published in the online version of the international academic journal npj Digital Medicine on December 5, 2024. (Paper title: The real-world association between digital markers of circadian disruption and mental health risks) DOI: 10.1038/s41746-024-01348-6 This study was conducted with the support of the KAIST's Research Support Program for New Faculty Members, the US National Science Foundation, the US National Institutes of Health, and the US Army Research Institute MURI Program.
2025.01.20
View 1733
KAIST Develops Neuromorphic Semiconductor Chip that Learns and Corrects Itself
< Photo. The research team of the School of Electrical Engineering posed by the newly deveoped processor. (From center to the right) Professor Young-Gyu Yoon, Integrated Master's and Doctoral Program Students Seungjae Han and Hakcheon Jeong and Professor Shinhyun Choi > - Professor Shinhyun Choi and Professor Young-Gyu Yoon’s Joint Research Team from the School of Electrical Engineering developed a computing chip that can learn, correct errors, and process AI tasks - Equipping a computing chip with high-reliability memristor devices with self-error correction functions for real-time learning and image processing Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time. KAIST (President Kwang Hyung Lee) announced on the 17th of January that the joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed a next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own. < Figure 1. Scanning electron microscope (SEM) image of a computing chip equipped with a highly reliable selector-less 32×32 memristor crossbar array (left). Hardware system developed for real-time artificial intelligence implementation (right). > What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time. This self-learning ability has been proven by achieving accuracy comparable to ideal computer simulations in real-time image processing. The research team's main achievement is that it has completed a system that is both reliable and practical, beyond the development of brain-like components. The research team has developed the world's first memristor-based integrated system that can adapt to immediate environmental changes, and has presented an innovative solution that overcomes the limitations of existing technology. < Figure 2. Background and foreground separation results of an image containing non-ideal characteristics of memristor devices (left). Real-time image separation results through on-device learning using the memristor computing chip developed by our research team (right). > At the heart of this innovation is a next-generation semiconductor device called a memristor*. The variable resistance characteristics of this device can replace the role of synapses in neural networks, and by utilizing it, data storage and computation can be performed simultaneously, just like our brain cells. *Memristor: A compound word of memory and resistor, next-generation electrical device whose resistance value is determined by the amount and direction of charge that has flowed between the two terminals in the past. The research team designed a highly reliable memristor that can precisely control resistance changes and developed an efficient system that excludes complex compensation processes through self-learning. This study is significant in that it experimentally verified the commercialization possibility of a next-generation neuromorphic semiconductor-based integrated system that supports real-time learning and inference. This technology will revolutionize the way artificial intelligence is used in everyday devices, allowing AI tasks to be processed locally without relying on remote cloud servers, making them faster, more privacy-protected, and more energy-efficient. “This system is like a smart workspace where everything is within arm’s reach instead of having to go back and forth between desks and file cabinets,” explained KAIST researchers Hakcheon Jeong and Seungjae Han, who led the development of this technology. “This is similar to the way our brain processes information, where everything is processed efficiently at once at one spot.” The research was conducted with Hakcheon Jeong and Seungjae Han, the students of Integrated Master's and Doctoral Program at KAIST School of Electrical Engineering being the co-first authors, the results of which was published online in the international academic journal, Nature Electronics, on January 8, 2025. *Paper title: Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array ( https://doi.org/10.1038/s41928-024-01318-6 ) This research was supported by the Next-Generation Intelligent Semiconductor Technology Development Project, Excellent New Researcher Project and PIM AI Semiconductor Core Technology Development Project of the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute Research and Development Support Project of the Institute of Information & communications Technology Planning & Evaluation.
2025.01.17
View 2040
KAIST Develops Insect-Eye-Inspired Camera Capturing 9,120 Frames Per Second
< (From left) Bio and Brain Engineering PhD Student Jae-Myeong Kwon, Professor Ki-Hun Jeong, PhD Student Hyun-Kyung Kim, PhD Student Young-Gil Cha, and Professor Min H. Kim of the School of Computing > The compound eyes of insects can detect fast-moving objects in parallel and, in low-light conditions, enhance sensitivity by integrating signals over time to determine motion. Inspired by these biological mechanisms, KAIST researchers have successfully developed a low-cost, high-speed camera that overcomes the limitations of frame rate and sensitivity faced by conventional high-speed cameras. KAIST (represented by President Kwang Hyung Lee) announced on the 16th of January that a research team led by Professors Ki-Hun Jeong (Department of Bio and Brain Engineering) and Min H. Kim (School of Computing) has developed a novel bio-inspired camera capable of ultra-high-speed imaging with high sensitivity by mimicking the visual structure of insect eyes. High-quality imaging under high-speed and low-light conditions is a critical challenge in many applications. While conventional high-speed cameras excel in capturing fast motion, their sensitivity decreases as frame rates increase because the time available to collect light is reduced. To address this issue, the research team adopted an approach similar to insect vision, utilizing multiple optical channels and temporal summation. Unlike traditional monocular camera systems, the bio-inspired camera employs a compound-eye-like structure that allows for the parallel acquisition of frames from different time intervals. < Figure 1. (A) Vision in a fast-eyed insect. Reflected light from swiftly moving objects sequentially stimulates the photoreceptors along the individual optical channels called ommatidia, of which the visual signals are separately and parallelly processed via the lamina and medulla. Each neural response is temporally summed to enhance the visual signals. The parallel processing and temporal summation allow fast and low-light imaging in dim light. (B) High-speed and high-sensitivity microlens array camera (HS-MAC). A rolling shutter image sensor is utilized to simultaneously acquire multiple frames by channel division, and temporal summation is performed in parallel to realize high speed and sensitivity even in a low-light environment. In addition, the frame components of a single fragmented array image are stitched into a single blurred frame, which is subsequently deblurred by compressive image reconstruction. > During this process, light is accumulated over overlapping time periods for each frame, increasing the signal-to-noise ratio. The researchers demonstrated that their bio-inspired camera could capture objects up to 40 times dimmer than those detectable by conventional high-speed cameras. The team also introduced a "channel-splitting" technique to significantly enhance the camera's speed, achieving frame rates thousands of times faster than those supported by the image sensors used in packaging. Additionally, a "compressed image restoration" algorithm was employed to eliminate blur caused by frame integration and reconstruct sharp images. The resulting bio-inspired camera is less than one millimeter thick and extremely compact, capable of capturing 9,120 frames per second while providing clear images in low-light conditions. < Figure 2. A high-speed, high-sensitivity biomimetic camera packaged in an image sensor. It is made small enough to fit on a finger, with a thickness of less than 1 mm. > The research team plans to extend this technology to develop advanced image processing algorithms for 3D imaging and super-resolution imaging, aiming for applications in biomedical imaging, mobile devices, and various other camera technologies. Hyun-Kyung Kim, a doctoral student in the Department of Bio and Brain Engineering at KAIST and the study's first author, stated, “We have experimentally validated that the insect-eye-inspired camera delivers outstanding performance in high-speed and low-light imaging despite its small size. This camera opens up possibilities for diverse applications in portable camera systems, security surveillance, and medical imaging.” < Figure 3. Rotating plate and flame captured using the high-speed, high-sensitivity biomimetic camera. The rotating plate at 1,950 rpm was accurately captured at 9,120 fps. In addition, the pinch-off of the flame with a faint intensity of 880 µlux was accurately captured at 1,020 fps. > This research was published in the international journal Science Advances in January 2025 (Paper Title: “Biologically-inspired microlens array camera for high-speed and high-sensitivity imaging”). DOI: https://doi.org/10.1126/sciadv.ads3389 This study was supported by the Korea Research Institute for Defense Technology Planning and Advancement (KRIT) of the Defense Acquisition Program Administration (DAPA), the Ministry of Science and ICT, and the Ministry of Trade, Industry and Energy (MOTIE).
2025.01.16
View 2781
KAIST Alumni Association to Honor Alumni of the Year Award Winners
Photo 1. Photo of the KAIST Alumni of the Year Award Recipients (From left) UST President Lee-whan Kim, CEO Han Chung of iThree Systems Co., Ltd., CEO Dong Myung Kim of LG Energy Solution Co., Ltd., and Professor Hyun Myung of the School of Electrical Engineering at KAIST KAIST (President Kwang Hyung Lee) announced on Monday, the 13th of January that the Alumni Association (President Yun-Tae Lee) has selected its Alumni of the Year. This year’s honorees are: ▴ President Lee-whan Kim of the Korea National University of Science and Technology (UST), ▴ CEO Han Chung of i3 Systems, ▴ CEO Dong Myung Kim of LG Energy Solution, and ▴ Professor Hyun Myung of the School of Electrical Engineering at KAIST. The honorees were selected based on their achievements over the past year, and the award ceremony will be held at the 2025 KAIST Alumni Association New Year’s Gathering to be held at the L Tower in Seoul at 5 PM on Friday the 17th. The KAIST Alumni of the Year Award is an award presented by the Alumni Association to alumni who have contributed to the development of the country and the society or have brought honor to their alma mater through outstanding academic achievements and community service. Since its establishment in 1992, 126 recipients have been awarded. Lee-whan Kim (Master's graduate of Mechanical Engineering, 82), the President of the Korea National University of Science and Technology (UST), established a leading foundation for national science and technology policy and strategy, and played a leading role in innovating national science and technology capabilities through the advancement of the national research and development system and the advancement of science and technology personnel training. In particular, he played a pivotal role in the establishment of UST and the Korea Science Academy (KSA), and greatly contributed to establishing a foundation for the training and utilization of science and technology personnel. Han Chung (Master's graduate of Electrical Engineering, 91, with Ph.D. degree in 96), the CEO of i3 Systems, is a first-generation researcher in the field of domestic infrared detectors. He developed military detectors for over 30 years and founded i3 Systems, a specialized infrared detector company, in 1998. Currently, he supplies more than 80% of the infrared detectors used by the Korean military, and has also achieved export results to over 20 countries. Dong Myung Kim (Master's graduate of Materials Science and Engineering, 94, with Ph.D. degree in 98) the CEO of LG Energy Solution Co., Ltd. has led innovation in the battery field with his ceaseless exploration and challenging spirit, and is known as an authority in the secondary battery industry. He played a leading role in establishing K-Battery as a global leader, strengthened the country's future industrial competitiveness, and greatly contributed to the development of science and technology. Hyun Myung (Bachelor's graduate of Electrical Engineering, 92, with Master's degree in 94, and Ph.D. degree in 98) a Professor of Electrical Engineering, KAIST, won first place in the world at the Quadruped Robot Challenge (QRC) hosted by the IEEE’s International Conference on Robotics and Automation (ICRA) 2023 with the 'DreamWaQ' system, an AI walking technology based on deep reinforcement learning that utilizes non-video sensory technologies. He contributed to enhancing the competitiveness of the domestic robot industry by developing his own fully autonomous walking technology that recognizes the environment around the robot and finds the optimal path. Yun-Tae Lee, the 27th president of the KAIST Alumni Association, said, “KAIST alumni have been the driving force behind the growth of industries in all walks of life by continuously conducting research and development in the field of advanced science and technology for a long time,” and added, “I am very proud of the KAIST alumni award recipients who are leading science and technology on the world stage beyond Korea, and I sincerely thank them for their efforts and achievements.”
2025.01.15
View 1055
KAIST Develops CamBio - a New Biotemplating Method
- Professor Jae-Byum Chang and Professor Yeon Sik Jung’s joint research team of the Department of Materials Science and Engineering developed a highly tunable bio-templating method “CamBio” that makes use of intracellular protein structures - Substrate performance improvement of up to 230% demonstrated via surface-enhanced Raman spectroscopy (SERS) - Expected to have price competitiveness over bio-templating method as it expands the range of biological samples - Expected to expand the range of application of nanostructure synthesis technology by utilizing various biological structures < Photo 1. (From left) Professor Yeon Sik Jung, Ph.D. candidate Dae-Hyeon Song, Professor Jae-Byum Chang, and (from top right) Dr. Chang Woo Song and Dr. Seunghee H. Cho of the Department of Materials Science and Engineering > Biological structures have complex characteristics that are difficult to replicate artificially, but biotemplating methods* that directly utilize these biological structures have been used in various fields of application. The KAIST research team succeeded in utilizing previously unusable biological structures and expanding the areas in which biotemplate methods can be applied. *Biotemplating: A method of using biotemplates as a mold to create functional structural materials, utilizing the functions of these biological structures, from viruses to the tissues and organs that make up our bodies KAIST (President Kwang Hyung Lee) announced on the 10th that a joint research team of Professors Jae-Byum Chang and Professor Yeon Sik Jung of the Department of Materials Science and Engineering developed a biotemplating method that utilizes specific intracellular proteins in biological samples and has high tunability. Existing biotemplate methods mainly utilize only the external surface of biological samples or have limitations in utilizing the structure-function correlation of various biological structures due to limited dimensions and sample sizes, making it difficult to create functional nanostructures. To solve this problem, the research team studied a way to utilize various biological structures within the cells while retaining high tunability. < Figure 1. CamBio utilizing microtubules, a intracellular protein structure. The silver nanoparticle chains synthesized along the microtubules that span the entire cell interior can be observed through an electron microscope, and it is shown that this can be used as a successful SERS substrate. > As a result of the research, the team developed the “Conversion to advanced materials via labeled Biostructure”, shortened as “CamBio”, which enables the selective synthesis of nanostructures with various characteristics and sizes from specific protein structures composed of diverse proteins within biological specimens. The CamBio method secures high tunability of functional nanostructures that can be manufactured from biological samples by merging various manufacturing and biological technologies. Through the technology of repeatedly attaching antibodies, arranging cells in a certain shape, and thinly slicing tissue, the functional nanostructures made with CamBio showed improved performance on the surface-enhanced Raman spectroscopy (SERS)* substrate used for material detection. *Surface-enhanced Raman spectroscopy (SERS): A technology that can detect very small amounts of substances using light, based on the principle that specific substances react to light and amplifies signals on surfaces of metals such as gold or silver. The research team found that the nanoparticle chains made using the intracellular protein structures through the process of repeated labeling with antibodies allowed easier control, and improved SERS performance by up to 230%. In addition, the research team expanded from utilizing the structures inside cells to obtaining samples of muscle tissues inside meat using a cryostat and successfully producing a substrate with periodic bands made of metal particles by performing the CamBio process. This method of producing a substrate not only allows large-scale production using biological samples, but also shows that it is a cost-effective method. < Figure 2. A method for securing tunability using CamBio at the cell level. Examples of controlling characteristics by integrating iterative labeling and cell pattering techniques with CamBio are shown. > The CamBio developed by the research team is expected to be used as a way to solve problems faced by various research fields as it is to expand the range of bio-samples that can be produced for various usage. The first author, Dae-Hyeon Song, a Ph.D. candidate of KAIST Department of Materials Science and Engineering said, “Through CamBio, we have comprehensively accumulated biotemplating methods that can utilize more diverse protein structures,” and “If combined with the state-of-the-art biological technologies such as gene editing and 3D bioprinting and new material synthesis technologies, biostructures can be utilized in various fields of application.” < Figure 3. A method for securing tunability using CamBio at the tissue level. In order to utilize proteins inside muscle tissue, the frozen tissue sectioning technology is combined, and through this, a substrate with a periodic nanoparticle band pattern is successfully produced, and it is shown that large-area acquisition of samples and price competitiveness can be achieved. > This study, in which the Ph.D. candidate Dae-Hyeon Song along with Dr. Chang Woo Song, and Dr. Seunghee H. Cho of the same department participated as the first authors, was published online in the international academic journal, Advanced Science, on November 13th, 2024. (Paper title: Highly Tunable, Nanomaterial-Functionalized Structural Templating of Intracellular Protein Structures Within Biological Species) https://doi.org/10.1002/advs.202406492 This study was conducted with a combination of support from various programs including the National Convergence Research of Scientific Challenges (National Research Foundation of Korea (NRF) 2024), Engineering Reseach Center (ERC) (Wearable Platform Materials Technology Center, NRF 2023), ERC (Global Bio-integrated Materials Center, NRF 2024), and the National Advanced Program for Biological Research Resources (Bioimaging Data Curation Center, NRF 2024) funded by Ministry of Science and ICT.
2025.01.10
View 1244
“Cross-Generation Collaborative Labs” for Semiconductor, Chemistry, and Computer Science Opened
< Photo of Professor Hoi-Jun Yoo (center) of the School of Electrical Engineering at the signboard unveiling ceremony > KAIST held a ceremony to mark the opening of three additional ‘Cross-Generation Collaborative Labs’ on the morning of January 7th, 2025. The “Next-Generation AI Semiconductor System Lab” by Professor Hoi-Jun Yoo of the School of Electrical Engineering, the “Molecular Spectroscopy and Chemical Dynamics Lab” by Professor Sang Kyu Kim of the Department of Chemistry, and the “Advanced Data Computing Lab” by Professor Sue Bok Moon of the School of Computer Science are the three new labs given the honored titled of the “Cross-Generation Collaborative Lab”. The Cross-Generation Collaborative Lab is KAIST’s unique system that was set up to facilitate the collaboration between retiring professors and junior professors to continue the achievements and know-how the elders have accumulated over their academic career. Since its introduction in 2018, nine labs have been named to be the Cross-Generation Labs, and this year’s new addition brings the total up to twelve. The ‘Next-Generation AI Semiconductor System Lab’ led by Professor Hoi-Jun Yoo will be operated by Professor Joo-Young Kim of the same school. Professor Hoi-Jun Yoo is a world-renowned scholar with outstanding research achievements in the field of on-device AI semiconductor design. Professor Joo-Young Kim is an up-and-coming researcher studying large language models and design of AI semiconductors for server computers, and is currently researching technologies to design PIM (Processing-in-Memory), a core technology in the field of AI semiconductors. Their research goal is to systematically collaborate and transfer next-generation AI semiconductor design technology, including brain-mimicking AI algorithms such as deep neural networks and generative AI, to integrate core technologies, and to maximize the usability of R&D outputs, thereby further solidifying the position of Korean AI semiconductor companies in the global market. Professor Hoi-Jun Yoo said, “I believe that, we will be able to present a development direction of for the next-generation AI semiconductors industries at home and abroad through collaborative research and play a key role in transferring and expanding global leadership.” < Professor Sang Kyu Kim of the Department of Chemistry (middle), at the signboard unveiling ceremony for his laboratory > The “Molecular Spectroscopy and Chemical Dynamics Laboratory”, where Professor Sang Kyu Kim of the Department of Chemistry is in charge, will be operated by Professor Tae Kyu Kim of the same department, and another professor in the field of spectroscopy and dynamics will join in the future. Professor Sang Kyu Kim has secured technologies for developing unique experimental equipment based on ultrashort lasers and supersonic molecular beams, and is a world leader who has been creatively pioneering new fields of experimental physical chemistry. The research goal is to describe chemical reactions and verify from a quantum mechanical perspective and introduce new theories and technologies to pursue a complete understanding of the principles of chemical reactions. In addition, the accompanying basic scientific knowledge will be applied to the design of new materials. Professor Sang Kyu Kim said, “I am very happy to be able to pass on the research infrastructure to the next generation through this system, and I will continue to nurture it to grow into a world-class research lab through trans-generational collaborative research.” < Photo of Professor Sue Bok Moon (center) at the signboard unveiling ceremony by the School of Computing > Lastly, the “Advanced Data Computing Lab” led by Professor Sue Bok Moon is joined by Professor Mee Young Cha of the same school and Professor Wonjae Lee of the Graduate School of Culture Technology. Professor Sue Bok Moon showed the infinite possibilities of large-scale data-based social network research through Cyworld, YouTube, and Twitter, and had a great influence on related fields beyond the field of computer science. Professor Mee Young Cha is a data scientist who analyzes difficult social issues such as misinformation, poverty, and disaster detection using big data-based AI. She is the first Korean to be recognized for her achievements as the director of the Max Planck Institute in Germany, a world-class basic science research institute. Therefore, there is high expectation for synergy effects from overseas collaborative research and technology transfer and sharing among the participating professors of the collaborative research lab. Professor Wonjae Lee is researching dynamic interaction analysis between science and technology using structural topic models. They plan to conduct research aimed at improving the analysis and understanding of negative influences occurring online, and in particular, developing a hateful precursor detection model using emotions and morality to preemptively block hateful expressions. Professor Sue Bok Moon said, “Through this collaborative research lab, we will play a key role in conducting in-depth collaborative research on unexpected negative influences in the AI era so that we can have a high level of competitiveness worldwide.” The ceremonies for the unveiling of the new Cross-Generation Collaborative Lab signboard were held in front of each lab from 10:00 AM on the 7th, in the attendance of President Kwang Hyung Lee, Senior Vice President for Research Sang Yup Lee, and other key officials of KAIST and the new staff members to join the laboratories.
2025.01.07
View 1032
Dongwon Group Honorary Chairman Kim Jae-chul Donates a Total of 54.4 Billion Won to KAIST
- Dongwon Group Honorary Chairman Kim Jae-chul, following his 2020 donation of 50 billion won, donates an additional 4.4 billion won to strengthen the AI education and research infrastructure - The additional donation of 4.4 billion won will be used to build a new AI education and research building - “In the AI era, there will be a new future in the sea of data. Please become the world’s No. 1 research group.” - Honorary Chairman Kim Jae-chul < Photo 1. Kim Jae-chul, Honorary Chairman of Dongwon Group > KAIST (President Kwang Hyung Lee) announced on the 6th of January that Dongwon Group's Honorary Chairman Kim Jae-chul has pledged an additional development fund of 4.4 billion won to strengthen the AI education and research infrastructure. This is his second donation following the 50 billion won donated in 2020. In 2020, Chairman Kim expressed his hope that KAIST acquire the highest level of capabilities in the field of AI by establishing the "Kim Jaechul Graduate School of AI" with his donation. Upon hearing that KAIST's AI research level was ranked fifth in the world, Chairman Kim asked that it be raised to first. In response to Chairman Kim's request, President Kwang Hyung Lee explained, "The number of AI professors at Carnegie Mellon University (CMU), currently ranked first in the world, is 45. To surpass this, the KAIST AI Graduate School's faculty should be expanded from its current level of 20 to 50, and a research building should be built so that they can focus on research." Chairman Kim responded, "I'll build that building for you, so don’t worry about that." KAIST will use 48.3 billion won of the donated funds to build an education and research building with a total floor area of 18,182㎡ (5,500 pyeong) on eight floors above ground and one floor below ground. The new building, which is scheduled to be completed in February 2028, is expected to be a world-class educational research facility that can house 50 professors and 1,000 students. Chairman Kim said, “When I was young, I looked for the future of Korea in the blue ocean of the world, but in the AI era, a new future will be in the ocean of data,” and explained the purpose of the donation, saying, “I hope that Korea will be able to lead the era of the 4th industrial revolution by fostering global core talents who can leap forward as leaders in the era of data exploration.” President Kwang Hyung Lee said, “I respect Chairman Kim’s decision to open a new horizon for fostering next-generation scientific talents who will lead the world. KAIST will grow the Kim Jaechul Graduate School of AI into the world’s No. 1 AI research group, just as Chairman Kim wishes.” Meanwhile, with this donation, President Kwang Hyung Lee has raised 261.2 billion won in donations during his tenure (1,400 days), raising an average of 186 million won per day.
2025.01.06
View 956
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