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KAIST Turns an Unprecedented Idea into Reality: Quantum Computing with Magnets
What started as an idea under KAIST’s Global Singularity Research Project—"Can we build a quantum computer using magnets?"—has now become a scientific reality. A KAIST-led international research team has successfully demonstrated a core quantum computing technology using magnetic materials (ferromagnets) for the first time in the world.
KAIST (represented by President Kwang-Hyung Lee) announced on the 6th of May that a team led by Professor Kab-Jin Kim from the Department of Physics, in collaboration with the Argonne National Laboratory and the University of Illinois Urbana-Champaign (UIUC), has developed a “photon-magnon hybrid chip” and successfully implemented real-time, multi-pulse interference using magnetic materials—marking a global first.
< Photo 1. Dr. Moojune Song (left) and Professor Kab-Jin Kim (right) of KAIST Department of Physics >
In simple terms, the researchers developed a special chip that synchronizes light and internal magnetic vibrations (magnons), enabling the transmission of phase information between distant magnets. They succeeded in observing and controlling interference between multiple signals in real time. This marks the first experimental evidence that magnets can serve as key components in quantum computing, serving as a pivotal step toward magnet-based quantum platforms.
The N and S poles of a magnet stem from the spin of electrons inside atoms. When many atoms align, their collective spin vibrations create a quantum particle known as a “magnon.”
Magnons are especially promising because of their nonreciprocal nature—they can carry information in only one direction, which makes them suitable for quantum noise isolation in compact quantum chips. They can also couple with both light and microwaves, enabling the potential for long-distance quantum communication over tens of kilometers.
Moreover, using special materials like antiferromagnets could allow quantum computers to operate at terahertz (THz) frequencies, far surpassing today’s hardware limitations, and possibly enabling room-temperature quantum computing without the need for bulky cryogenic equipment.
To build such a system, however, one must be able to transmit, measure, and control the phase information of magnons—the starting point and propagation of their waveforms—in real time. This had not been achieved until now.
< Figure 1. Superconducting Circuit-Based Magnon-Photon Hybrid System. (a) Schematic diagram of the device. A NbN superconducting resonator circuit fabricated on a silicon substrate is coupled with spherical YIG magnets (250 μm diameter), and magnons are generated and measured in real-time via a vertical antenna. (b) Photograph of the actual device. The distance between the two YIG spheres is 12 mm, a distance at which they cannot influence each other without the superconducting circuit. >
Professor Kim’s team used two tiny magnetic spheres made of Yttrium Iron Garnet (YIG) placed 12 mm apart with a superconducting resonator in between—similar to those used in quantum processors by Google and IBM. They input pulses into one magnet and successfully observed lossless transmission of magnon vibrations to the second magnet via the superconducting circuit.
They confirmed that from single nanosecond pulses to four microwave pulses, the magnon vibrations maintained their phase information and demonstrated predictable constructive or destructive interference in real time—known as coherent interference.
By adjusting the pulse frequencies and their intervals, the researchers could also freely control the interference patterns of magnons, effectively showing for the first time that electrical signals can be used to manipulate magnonic quantum states.
This work demonstrated that quantum gate operations using multiple pulses—a fundamental technique in quantum information processing—can be implemented using a hybrid system of magnetic materials and superconducting circuits. This opens the door for the practical use of magnet-based quantum devices.
< Figure 2. Experimental Data. (a) Measurement results of magnon-magnon band anticrossing via continuous wave measurement, showing the formation of a strong coupling hybrid system. (b) Magnon pulse exchange oscillation phenomenon between YIG spheres upon single pulse application. It can be seen that magnon information is coherently transmitted at regular time intervals through the superconducting circuit. (c,d) Magnon interference phenomenon upon dual pulse application. The magnon information state can be arbitrarily controlled by adjusting the time interval and carrier frequency between pulses. >
Professor Kab-Jin Kim stated, “This project began with a bold, even unconventional idea proposed to the Global Singularity Research Program: ‘What if we could build a quantum computer with magnets?’ The journey has been fascinating, and this study not only opens a new field of quantum spintronics, but also marks a turning point in developing high-efficiency quantum information processing devices.”
The research was co-led by postdoctoral researcher Moojune Song (KAIST), Dr. Yi Li and Dr. Valentine Novosad from Argonne National Lab, and Prof. Axel Hoffmann’s team at UIUC. The results were published in Nature Communications on April 17 and npj Spintronics on April 1, 2025.
Paper 1: Single-shot magnon interference in a magnon-superconducting-resonator hybrid circuit, Nat. Commun. 16, 3649 (2025)
DOI: https://doi.org/10.1038/s41467-025-58482-2
Paper 2: Single-shot electrical detection of short-wavelength magnon pulse transmission in a magnonic ultra-thin-film waveguide, npj Spintronics 3, 12 (2025)
DOI: https://doi.org/10.1038/s44306-025-00072-5
The research was supported by KAIST’s Global Singularity Research Initiative, the National Research Foundation of Korea (including the Mid-Career Researcher, Leading Research Center, and Quantum Information Science Human Resource Development programs), and the U.S. Department of Energy.
2025.06.12 View 202 -
KAIST Research Team Breaks Down Musical Instincts with AI
Music, often referred to as the universal language, is known to be a common component in all cultures. Then, could ‘musical instinct’ be something that is shared to some degree despite the extensive environmental differences amongst cultures?
On January 16, a KAIST research team led by Professor Hawoong Jung from the Department of Physics announced to have identified the principle by which musical instincts emerge from the human brain without special learning using an artificial neural network model.
Previously, many researchers have attempted to identify the similarities and differences between the music that exist in various different cultures, and tried to understand the origin of the universality. A paper published in Science in 2019 had revealed that music is produced in all ethnographically distinct cultures, and that similar forms of beats and tunes are used. Neuroscientist have also previously found out that a specific part of the human brain, namely the auditory cortex, is responsible for processing musical information.
Professor Jung’s team used an artificial neural network model to show that cognitive functions for music forms spontaneously as a result of processing auditory information received from nature, without being taught music. The research team utilized AudioSet, a large-scale collection of sound data provided by Google, and taught the artificial neural network to learn the various sounds. Interestingly, the research team discovered that certain neurons within the network model would respond selectively to music. In other words, they observed the spontaneous generation of neurons that reacted minimally to various other sounds like those of animals, nature, or machines, but showed high levels of response to various forms of music including both instrumental and vocal.
The neurons in the artificial neural network model showed similar reactive behaviours to those in the auditory cortex of a real brain. For example, artificial neurons responded less to the sound of music that was cropped into short intervals and were rearranged. This indicates that the spontaneously-generated music-selective neurons encode the temporal structure of music. This property was not limited to a specific genre of music, but emerged across 25 different genres including classic, pop, rock, jazz, and electronic.
< Figure 1. Illustration of the musicality of the brain and artificial neural network (created with DALL·E3 AI based on the paper content) >
Furthermore, suppressing the activity of the music-selective neurons was found to greatly impede the cognitive accuracy for other natural sounds. That is to say, the neural function that processes musical information helps process other sounds, and that ‘musical ability’ may be an instinct formed as a result of an evolutionary adaptation acquired to better process sounds from nature.
Professor Hawoong Jung, who advised the research, said, “The results of our study imply that evolutionary pressure has contributed to forming the universal basis for processing musical information in various cultures.” As for the significance of the research, he explained, “We look forward for this artificially built model with human-like musicality to become an original model for various applications including AI music generation, musical therapy, and for research in musical cognition.” He also commented on its limitations, adding, “This research however does not take into consideration the developmental process that follows the learning of music, and it must be noted that this is a study on the foundation of processing musical information in early development.”
< Figure 2. The artificial neural network that learned to recognize non-musical natural sounds in the cyber space distinguishes between music and non-music. >
This research, conducted by first author Dr. Gwangsu Kim of the KAIST Department of Physics (current affiliation: MIT Department of Brain and Cognitive Sciences) and Dr. Dong-Kyum Kim (current affiliation: IBS) was published in Nature Communications under the title, “Spontaneous emergence of rudimentary music detectors in deep neural networks”.
This research was supported by the National Research Foundation of Korea.
2024.01.23 View 6880 -
North Korea and Beyond: AI-Powered Satellite Analysis Reveals the Unseen Economic Landscape of Underdeveloped Nations
- A joint research team in computer science, economics, and geography has developed an artificial intelligence (AI) technology to measure grid-level economic development within six-square-kilometer regions.
- This AI technology is applicable in regions with limited statistical data (e.g., North Korea), supporting international efforts to propose policies for economic growth and poverty reduction in underdeveloped countries.
- The research team plans to make this technology freely available for use to contribute to the United Nations' Sustainable Development Goals (SDGs).
The United Nations reports that more than 700 million people are in extreme poverty, earning less than two dollars a day. However, an accurate assessment of poverty remains a global challenge. For example, 53 countries have not conducted agricultural surveys in the past 15 years, and 17 countries have not published a population census. To fill this data gap, new technologies are being explored to estimate poverty using alternative sources such as street views, aerial photos, and satellite images.
The paper published in Nature Communications demonstrates how artificial intelligence (AI) can help analyze economic conditions from daytime satellite imagery. This new technology can even apply to the least developed countries - such as North Korea - that do not have reliable statistical data for typical machine learning training.
The researchers used Sentinel-2 satellite images from the European Space Agency (ESA) that are publicly available. They split these images into small six-square-kilometer grids. At this zoom level, visual information such as buildings, roads, and greenery can be used to quantify economic indicators. As a result, the team obtained the first ever fine-grained economic map of regions like North Korea. The same algorithm was applied to other underdeveloped countries in Asia: North Korea, Nepal, Laos, Myanmar, Bangladesh, and Cambodia (see Image 1).
The key feature of their research model is the "human-machine collaborative approach," which lets researchers combine human input with AI predictions for areas with scarce data. In this research, ten human experts compared satellite images and judged the economic conditions in the area, with the AI learning from this human data and giving economic scores to each image. The results showed that the Human-AI collaborative approach outperformed machine-only learning algorithms.
< Image 1. Nightlight satellite images of North Korea (Top-left: Background photo provided by NASA's Earth Observatory). South Korea appears brightly lit compared to North Korea, which is mostly dark except for Pyongyang. In contrast, the model developed by the research team uses daytime satellite imagery to predict more detailed economic predictions for North Korea (top-right) and five Asian countries (Bottom: Background photo from Google Earth). >
The research was led by an interdisciplinary team of computer scientists, economists, and a geographer from KAIST & IBS (Donghyun Ahn, Meeyoung Cha, Jihee Kim), Sogang University (Hyunjoo Yang), HKUST (Sangyoon Park), and NUS (Jeasurk Yang). Dr Charles Axelsson, Associate Editor at Nature Communications, handled this paper during the peer review process at the journal.
The research team found that the scores showed a strong correlation with traditional socio-economic metrics such as population density, employment, and number of businesses. This demonstrates the wide applicability and scalability of the approach, particularly in data-scarce countries. Furthermore, the model's strength lies in its ability to detect annual changes in economic conditions at a more detailed geospatial level without using any survey data (see Image 2).
< Image 2. Differences in satellite imagery and economic scores in North Korea between 2016 and 2019. Significant development was found in the Wonsan Kalma area (top), one of the tourist development zones, but no changes were observed in the Wiwon Industrial Development Zone (bottom). (Background photo: Sentinel-2 satellite imagery provided by the European Space Agency (ESA)). >
This model would be especially valuable for rapidly monitoring the progress of Sustainable Development Goals such as reducing poverty and promoting more equitable and sustainable growth on an international scale. The model can also be adapted to measure various social and environmental indicators. For example, it can be trained to identify regions with high vulnerability to climate change and disasters to provide timely guidance on disaster relief efforts.
As an example, the researchers explored how North Korea changed before and after the United Nations sanctions against the country. By applying the model to satellite images of North Korea both in 2016 and in 2019, the researchers discovered three key trends in the country's economic development between 2016 and 2019. First, economic growth in North Korea became more concentrated in Pyongyang and major cities, exacerbating the urban-rural divide. Second, satellite imagery revealed significant changes in areas designated for tourism and economic development, such as new building construction and other meaningful alterations. Third, traditional industrial and export development zones showed relatively minor changes.
Meeyoung Cha, a data scientist in the team explained, "This is an important interdisciplinary effort to address global challenges like poverty. We plan to apply our AI algorithm to other international issues, such as monitoring carbon emissions, disaster damage detection, and the impact of climate change."
An economist on the research team, Jihee Kim, commented that this approach would enable detailed examinations of economic conditions in the developing world at a low cost, reducing data disparities between developed and developing nations. She further emphasized that this is most essential because many public policies require economic measurements to achieve their goals, whether they are for growth, equality, or sustainability.
The research team has made the source code publicly available via GitHub and plans to continue improving the technology, applying it to new satellite images updated annually. The results of this study, with Ph.D. candidate Donghyun Ahn at KAIST and Ph.D. candidate Jeasurk Yang at NUS as joint first authors, were published in Nature Communications under the title "A human-machine collaborative approach measures economic development using satellite imagery."
< Photos of the main authors. 1. Donghyun Ahn, PhD candidate at KAIST School of Computing 2. Jeasurk Yang, PhD candidate at the Department of Geography of National University of Singapore 3. Meeyoung Cha, Professor of KAIST School of Computing and CI at IBS 4. Jihee Kim, Professor of KAIST School of Business and Technology Management 5. Sangyoon Park, Professor of the Division of Social Science at Hong Kong University of Science and Technology 6. Hyunjoo Yang, Professor of the Department of Economics at Sogang University >
2023.12.07 View 8122 -
Shaping the AI Semiconductor Ecosystem
- As the marriage of AI and semiconductor being highlighted as the strategic technology of national enthusiasm, KAIST's achievements in the related fields accumulated through top-class education and research capabilities that surpass that of peer universities around the world are standing far apart from the rest of the pack.
As Artificial Intelligence Semiconductor, or a system of semiconductors designed for specifically for highly complicated computation need for AI to conduct its learning and deducing calculations, (hereafter AI semiconductors) stand out as a national strategic technology, the related achievements of KAIST, headed by President Kwang Hyung Lee, are also attracting attention. The Ministry of Science, ICT and Future Planning (MSIT) of Korea initiated a program to support the advancement of AI semiconductor last year with the goal of occupying 20% of the global AI semiconductor market by 2030. This year, through industry-university-research discussions, the Ministry expanded to the program with the addition of 1.2 trillion won of investment over five years through 'Support Plan for AI Semiconductor Industry Promotion'. Accordingly, major universities began putting together programs devised to train students to develop expertise in AI semiconductors.
KAIST has accumulated top-notch educational and research capabilities in the two core fields of AI semiconductor - Semiconductor and Artificial Intelligence. Notably, in the field of semiconductors, the International Solid-State Circuit Conference (ISSCC) is the world's most prestigious conference about designing of semiconductor integrated circuit. Established in 1954, with more than 60% of the participants coming from companies including Samsung, Qualcomm, TSMC, and Intel, the conference naturally focuses on practical value of the studies from the industrial point-of-view, earning the nickname the ‘Semiconductor Design Olympics’. At such conference of legacy and influence, KAIST kept its presence widely visible over other participating universities, leading in terms of the number of accepted papers over world-class schools such as Massachusetts Institute of Technology (MIT) and Stanford for the past 17 years.
Number of papers published at the InternationalSolid-State Circuit Conference (ISSCC) in 2022 sorted by nations and by institutions
Number of papers by universities presented at the International Solid-State Circuit Conference (ISCCC) in 2006~2022
In terms of the number of papers accepted at the ISSCC, KAIST ranked among top two universities each year since 2006. Looking at the average number of accepted papers over the past 17 years, KAIST stands out as an unparalleled leader. The average number of KAIST papers adopted during the period of 17 years from 2006 through 2022, was 8.4, which is almost double of that of competitors like MIT (4.6) and UCLA (3.6). In Korea, it maintains the second place overall after Samsung, the undisputed number one in the semiconductor design field. Also, this year, KAIST was ranked first among universities participating at the Symposium on VLSI Technology and Circuits, an academic conference in the field of integrated circuits that rivals the ISSCC.
Number of papers adopted by the Symposium on VLSI Technology and Circuits in 2022 submitted from the universities
With KAIST researchers working and presenting new technologies at the frontiers of all key areas of the semiconductor industry, the quality of KAIST research is also maintained at the highest level. Professor Myoungsoo Jung's research team in the School of Electrical Engineering is actively working to develop heterogeneous computing environment with high energy efficiency in response to the industry's demand for high performance at low power. In the field of materials, a research team led by Professor Byong-Guk Park of the Department of Materials Science and Engineering developed the Spin Orbit Torque (SOT)-based Magnetic RAM (MRAM) memory that operates at least 10 times faster than conventional memories to suggest a way to overcome the limitations of the existing 'von Neumann structure'.
As such, while providing solutions to major challenges in the current semiconductor industry, the development of new technologies necessary to preoccupy new fields in the semiconductor industry are also very actively pursued. In the field of Quantum Computing, which is attracting attention as next-generation computing technology needed in order to take the lead in the fields of cryptography and nonlinear computation, Professor Sanghyeon Kim's research team in the School of Electrical Engineering presented the world's first 3D integrated quantum computing system at 2021 VLSI Symposium. In Neuromorphic Computing, which is expected to bring remarkable advancements in the field of artificial intelligence by utilizing the principles of the neurology, the research team of Professor Shinhyun Choi of School of Electrical Engineering is developing a next-generation memristor that mimics neurons.
The number of papers by the International Conference on Machine Learning (ICML) and the Conference on Neural Information Processing Systems (NeurIPS), two of the world’s most prestigious academic societies in the field of artificial intelligence (KAIST 6th in the world, 1st in Asia, in 2020)
The field of artificial intelligence has also grown rapidly. Based on the number of papers from the International Conference on Machine Learning (ICML) and the Conference on Neural Information Processing Systems (NeurIPS), two of the world's most prestigious conferences in the field of artificial intelligence, KAIST ranked 6th in the world in 2020 and 1st in Asia. Since 2012, KAIST's ranking steadily inclined from 37th to 6th, climbing 31 steps over the period of eight years. In 2021, 129 papers, or about 40%, of Korean papers published at 11 top artificial intelligence conferences were presented by KAIST. Thanks to KAIST's efforts, in 2021, Korea ranked sixth after the United States, China, United Kingdom, Canada, and Germany in terms of the number of papers published by global AI academic societies.
Number of papers from Korea (and by KAIST) published at 11 top conferences in the field of artificial intelligence in 2021
In terms of content, KAIST's AI research is also at the forefront. Professor Hoi-Jun Yoo's research team in the School of Electrical Engineering compensated for the shortcomings of the “edge networks” by implementing artificial intelligence real-time learning networks on mobile devices. In order to materialize artificial intelligence, data accumulation and a huge amount of computation is required. For this, a high-performance server takes care of massive computation, and for the user terminals, the “edge network” that collects data and performs simple computations are used. Professor Yoo's research greatly increased AI’s processing speed and performance by allotting the learning task to the user terminal as well.
In June, a research team led by Professor Min-Soo Kim of the School of Computing presented a solution that is essential for processing super-scale artificial intelligence models. The super-scale machine learning system developed by the research team is expected to achieve speeds up to 8.8 times faster than Google's Tensorflow or IBM's System DS, which are mainly used in the industry.
KAIST is also making remarkable achievements in the field of AI semiconductors. In 2020, Professor Minsoo Rhu's research team in the School of Electrical Engineering succeeded in developing the world's first AI semiconductor optimized for AI recommendation systems. Due to the nature of the AI recommendation system having to handle vast amounts of contents and user information, it quickly meets its limitation because of the information bottleneck when the process is operated through a general-purpose artificial intelligence system. Professor Minsoo Rhu's team developed a semiconductor that can achieve a speed that is 21 times faster than existing systems using the 'Processing-In-Memory (PIM)' technology. PIM is a technology that improves efficiency by performing the calculations in 'RAM', or random-access memory, which is usually only used to store data temporarily just before they are processed. When PIM technology is put out on the market, it is expected that fortify competitiveness of Korean companies in the AI semiconductor market drastically, as they already hold great strength in the memory area.
KAIST does not plan to be complacent with its achievements, but is making various plans to further the distance from the competitors catching on in the fields of artificial intelligence, semiconductors, and AI semiconductors. Following the establishment of the first artificial intelligence research center in Korea in 1990, the Kim Jaechul AI Graduate School was opened in 2019 to sustain the supply chain of the experts in the field. In 2020, Artificial Intelligence Semiconductor System Research Center was launched to conduct convergent research on AI and semiconductors, which was followed by the establishment of the AI Institutes to promote “AI+X” research efforts.
Based on the internal capabilities accumulated through these efforts, KAIST is also making efforts to train human resources needed in these areas. KAIST established joint research centers with companies such as Naver, while collaborating with local governments such as Hwaseong City to simultaneously nurture professional manpower. Back in 2021, KAIST signed an agreement to establish the Semiconductor System Engineering Department with Samsung Electronics and are preparing a new semiconductor specialist training program. The newly established Department of Semiconductor System Engineering will select around 100 new students every year from 2023 and provide special scholarships to all students so that they can develop their professional skills. In addition, through close cooperation with the industry, they will receive special support which includes field trips and internships at Samsung Electronics, and joint workshops and on-site training.
KAIST has made a significant contribution to the growth of the Korean semiconductor industry ecosystem, producing 25% of doctoral workers in the domestic semiconductor field and 20% of CEOs of mid-sized and venture companies with doctoral degrees. With the dawn coming up on the AI semiconductor ecosystem, whether KAIST will reprise the pivotal role seems to be the crucial point of business.
2022.08.05 View 12735 -
KAIST Research Team Proves How a Neurotransmitter may be the Key in Controlling Alzheimer’s Toxicity
With nearly 50 million dementia patients worldwide, and Alzheimers’s disease is the most common neurodegenerative disease. Its main symptom is the impairment of general cognitive abilities, including the ability to speak or to remember. The importance of finding a cure is widely understood with increasingly aging population and the life expectancy being ever-extended. However, even the cause of the grim disease is yet to be given a clear definition.
A KAIST research team in the Department of Chemistry led by professor Mi Hee Lim took on a lead to discovered a new role for somatostatin, a protein-based neurotransmitter, in reducing the toxicity caused in the pathogenic mechanism taken towards development of Alzheimer’s disease. The study was published in the July issue of Nature Chemistry under the title, “Conformational and functional changes of the native neuropeptide somatostatin occur in the presence of copper and amyloid-β”.
According to the amyloid hypothesis, the abnormal deposition of Aβ proteins causes death of neuronal cells. While Aβ agglomerations make up most of the aged plaques through fibrosis, in recent studies, high concentrations of transitional metal were found in the plaques from Alzheimer’s patients.
This suggests a close interaction between metallic ions and Aβ, which accelerates the fibrosis of proteins. Copper in particular is a redox-activating transition metal that can produce large amounts of oxygen and cause serious oxidative stress on cell organelles. Aβ proteins and transition metals can closely interact with neurotransmitters at synapses, but the direct effects of such abnormalities on the structure and function of neurotransmitters are yet to be understood.
Figure 1. Functional shift of somatostatin (SST) by factors in the pathogenesis of Alzheimer's disease.
Figure 2. Somatostatin’s loss-of-function as neurotransmitter. a. Schematic diagram of SST auto-aggregation due to Alzheimer's pathological factors. b. SST’s aggregation by copper ions. c. Coordination-prediction structure and N-terminal folding of copper-SST. d. Inhibition of SST receptor binding specificity by metals.
In their research, Professor Lim’s team discovered that when somatostatin, the protein-based neurotransmitter, is met with copper, Aβ, and metal-Aβ complexes, self-aggregates and ceases to perform its innate function of transmitting neural signals, but begins to attenuate the toxicity and agglomeration of metal-Aβ complexes.
Figure 3. Gain-of-function of somatostatin (SST) in the dementia setting. a. Prediction of docking of SST and amyloid beta. b. SST making metal-amyloid beta aggregates into an amorphous form. c. Cytotoxic mitigation effect of SST. d. SST mitigating the interaction between amyloid beta protein with the cell membrane.
This research, by Dr. Jiyeon Han et al. from the KAIST Department of Chemistry, revealed the coordination structure between copper and somatostatin at a molecular level through which it suggested the agglomeration mechanism, and discovered the effects of somatostatin on Aβ agglomeration path depending on the presence or absence of metals. The team has further confirmed somatostatin’s receptor binding, interactions with cell membranes, and effects on cell toxicity for the first time to receive international attention.
Professor Mi Hee Lim said, “This research has great significance in having discovered a new role of neurotransmitters in the pathogenesis of Alzheimer’s disease.” “We expect this research to contribute to defining the pathogenic network of neurodegenerative diseases caused by aging, and to the development of future biomarkers and medicine,” she added.
This research was conducted jointly by Professor Seung-Hee Lee’s team of KAIST Department of Biological Sciences, Professor Kiyoung Park’s Team of KAIST Department of Chemistry, and Professor Yulong Li’s team of Peking University.
The research was funded by Basic Science Research Program of the National Research Foundation of Korea and KAIST.
For more information about the research team, visit the website: https://sites.google.com/site/miheelimlab/1-professor-mi-hee-lim.
2022.07.29 View 14732 -
An AI-based, Indoor/Outdoor-Integrated (IOI) GPS System to Bring Seismic Waves in the Terrains of Positioning Technology
KAIST breaks new grounds in positioning technology with an AI-integrated GPS board that works both indoors and out
KAIST (President Kwang Hyung Lee) announced on the 8th that Professor Dong-Soo Han's research team (Intelligent Service Integration Lab) from the School of Computing has developed a GPS system that works both indoors and outdoors with quality precision regardless of the environment.
This Indoor/Outdoor-Integrated GPS System, or IOI GPS System, for short, uses the GPS signals outdoors and estimates locations indoors using signals from multiple sources like an inertial sensor, pressure sensors, geomagnetic sensors, and light sensors. To this end, the research team developed techniques to detect environmental changes such as entering a building, and methods to detect entrances, ground floors, stairs, elevators and levels of buildings by utilizing artificial intelligence techniques. Various landmark detecting techniques were also incorporated with pedestrian dead reckoning (PDR), a navigation tool for pedestrians, to devise the so-called “Sensor-Fusion Positioning Algorithm”.
To date, it was common to estimate locations based on wireless LAN signals or base station signals in a space where the GPS signal could not reach. However, the IOI GPS enables positioning even in buildings without signals nor indoor maps.
The algorithm developed by the research team can provide accurate floor information within a building where even big tech companies like Google and Apple's positioning services do not provide. Unlike other positioning methods that rely on visual data, geomagnetic positioning techniques, or wireless LAN, this system also has the advantage of not requiring any prior preparation. In other words, the foundation to enable the usage of a universal GPS system that works both indoors and outdoors anywhere in the world is now ready.
The research team also produced a circuit board for the purpose of operating the IOI GPS System, mounted with chips to receive and process GPS, Wi-Fi, and Bluetooth signals, along with an inertial sensor, a barometer, a magnetometer, and a light sensor. The sensor-fusion positioning algorithm the lab has developed is also incorporated in the board.
When the accuracy of the IOI GPS board was tested in the N1 building of KAIST’s main campus in Daejeon, it achieved an accuracy of about 95% in floor estimation and an accuracy of about 3 to 6 meters in distance estimation. As for the indoor/outdoor transition, the navigational mode change was completed in about 0.3 seconds. When it was combined with the PDR technique, the estimation accuracy improved further down to a scope of one meter.
The research team is now working on assembling a tag with a built-in positioning board and applying it to location-based docent services for visitors at museums, science centers, and art galleries. The IOI GPS tag can be used for the purpose of tracking children and/or the elderly, and it can also be used to locate people or rescue workers lost in disaster-ridden or hazardous sites. On a different note, the sensor-fusion positioning algorithm and positioning board for vehicles are also under development for the tracking of vehicles entering indoor areas like underground parking lots.
When the IOI GPS board for vehicles is manufactured, the research team will work to collaborate with car manufacturers and car rental companies, and will also develop a sensor-fusion positioning algorithm for smartphones. Telecommunication companies seeking to diversify their programs in the field of location-based services will also be interested in the use the IOI GPS.
Professor Dong-Soo Han of the School of Computing, who leads the research team, said, “This is the first time to develop an indoor/outdoor integrated GPS system that can pinpoint locations in a building where there is no wireless signal or an indoor map, and there are an infinite number of areas it can be applied to. When the integration with the Korea Augmentation Satellite System (KASS) and the Korean GPS (KPS) System that began this year, is finally completed, Korea can become the leader in the field of GPS both indoors and outdoors, and we also have plans to manufacture semi-conductor chips for the IOI GPS System to keep the tech-gap between Korea and the followers.”
He added, "The guidance services at science centers, museums, and art galleries that uses IOI GPS tags can provide a set of data that would be very helpful for analyzing the visitors’ viewing traces. It is an essential piece of information required when the time comes to decide when to organize the next exhibit. We will be working on having it applied to the National Science Museum, first.”
The projects to develop the IOI GPS system and the trace analysis system for science centers were supported through Science, Culture, Exhibits and Services Capability Enhancement Program of the Ministry of Science and ICT.
Profile: Dong-Soo Han, Ph.D.Professorddsshhan@kaist.ac.krhttp://isilab.kaist.ac.kr
Intelligent Service Integration Lab.School of Computing
http://kaist.ac.kr/en/
Korea Advanced Institute of Science and Technology (KAIST)Daejeon, Republic of Korea
2022.07.13 View 11832 -
Professor Juho Kim’s Team Wins Best Paper Award at ACM CHI 2022
The research team led by Professor Juho Kim from the KAIST School of Computing won a Best Paper Award and an Honorable Mention Award at the Association for Computing Machinery Conference on Human Factors in Computing Systems (ACM CHI) held between April 30 and May 6.
ACM CHI is the world’s most recognized conference in the field of human computer interactions (HCI), and is ranked number one out of all HCI-related journals and conferences based on Google Scholar’s h-5 index. Best paper awards are given to works that rank in the top one percent, and honorable mention awards are given to the top five percent of the papers accepted by the conference.
Professor Juho Kim presented a total of seven papers at ACM CHI 2022, and tied for the largest number of papers. A total of 19 papers were affiliated with KAIST, putting it fifth out of all participating institutes and thereby proving KAIST’s competence in research.
One of Professor Kim’s research teams composed of Jeongyeon Kim (first author, MS graduate) from the School of Computing, MS candidate Yubin Choi from the School of Electrical Engineering, and Dr. Meng Xia (post-doctoral associate in the School of Computing, currently a post-doctoral associate at Carnegie Mellon University) received a best paper award for their paper, “Mobile-Friendly Content Design for MOOCs: Challenges, Requirements, and Design Opportunities”.
The study analyzed the difficulties experienced by learners watching video-based educational content in a mobile environment and suggests guidelines for solutions. The research team analyzed 134 survey responses and 21 interviews, and revealed that texts that are too small or overcrowded are what mainly brings down the legibility of video contents. Additionally, lighting, noise, and surrounding environments that change frequently are also important factors that may disturb a learning experience.
Based on these findings, the team analyzed the aptness of 41,722 frames from 101 video lectures for mobile environments, and confirmed that they generally show low levels of adequacy. For instance, in the case of text sizes, only 24.5% of the frames were shown to be adequate for learning in mobile environments. To overcome this issue, the research team suggested a guideline that may improve the legibility of video contents and help overcome the difficulties arising from mobile learning environments.
The importance of and dependency on video-based learning continue to rise, especially in the wake of the pandemic, and it is meaningful that this research suggested a means to analyze and tackle the difficulties of users that learn from the small screens of mobile devices. Furthermore, the paper also suggested technology that can solve problems related to video-based learning through human-AI collaborations, enhancing existing video lectures and improving learning experiences. This technology can be applied to various video-based platforms and content creation.
Meanwhile, a research team composed of Ph.D. candidate Tae Soo Kim (first author), MS candidate DaEun Choi, and Ph.D. candidate Yoonseo Choi from the School of Computing received an honorable mention award for their paper, “Stylette: styling the Web with Natural Language”.
The research team developed a novel interface technology that allows nonexperts who are unfamiliar with technical jargon to edit website features through speech. People often find it difficult to use or find the information they need from various websites due to accessibility issues, device-related constraints, inconvenient design, style preferences, etc. However, it is not easy for laymen to edit website features without expertise in programming or design, and most end up just putting up with the inconveniences. But what if the system could read the intentions of its users from their everyday language like “emphasize this part a little more”, or “I want a more modern design”, and edit the features automatically?
Based on this question, Professor Kim’s research team developed ‘Stylette’, a system in which AI analyses its users’ speech expressed in their natural language and automatically recommends a new style that best fits their intentions. The research team created a new system by putting together language AI, visual AI, and user interface technologies. On the linguistic side, a large-scale language model AI converts the intentions of the users expressed through their everyday language into adequate style elements. On the visual side, computer vision AI compares 1.7 million existing web design features and recommends a style adequate for the current website. In an experiment where 40 nonexperts were asked to edit a website design, the subjects that used this system showed double the success rate in a time span that was 35% shorter compared to the control group.
It is meaningful that this research proposed a practical case in which AI technology constructs intuitive interactions with users. The developed technology can be applied to existing design applications and web browsers in a plug-in format, and can be utilized to improve websites or for advertisements by collecting the natural intention data of users on a large scale.
2022.06.13 View 9025 -
Now You Can See Floral Scents!
Optical interferometry visualizes how often lilies emit volatile organic compounds
Have you ever thought about when flowers emit their scents?
KAIST mechanical engineers and biological scientists directly visualized how often a lily releases a floral scent using a laser interferometry method. These measurement results can provide new insights for understanding and further exploring the biosynthesis and emission mechanisms of floral volatiles.
Why is it important to know this? It is well known that the fragrance of flowers affects their interactions with pollinators, microorganisms, and florivores. For instance, many flowering plants can tune their scent emission rates when pollinators are active for pollination. Petunias and the wild tobacco Nicotiana attenuata emit floral scents at night to attract night-active pollinators. Thus, visualizing scent emissions can help us understand the ecological evolution of plant-pollinator interactions.
Many groups have been trying to develop methods for scent analysis. Mass spectrometry has been one widely used method for investigating the fragrance of flowers. Although mass spectrometry reveals the quality and quantity of floral scents, it is impossible to directly measure the releasing frequency. A laser-based gas detection system and a smartphone-based detection system using chemo-responsive dyes have also been used to measure volatile organic compounds (VOCs) in real-time, but it is still hard to measure the time-dependent emission rate of floral scents.
However, the KAIST research team co-led by Professor Hyoungsoo Kim from the Department of Mechanical Engineering and Professor Sang-Gyu Kim from the Department of Biological Sciences measured a refractive index difference between the vapor of the VOCs of lilies and the air to measure the emission frequency. The floral scent vapor was detected and the refractive index of air was 1.0 while that of the major floral scent of a linalool lily was 1.46.
Professor Hyoungsoo Kim said, “We expect this technology to be further applicable to various industrial sectors such as developing it to detect hazardous substances in a space.” The research team also plans to identify the DNA mechanism that controls floral scent secretion.
The current work entitled “Real-time visualization of scent accumulation reveals the frequency of floral scent emissions” was published in ‘Frontiers in Plant Science’ on April 18, 2022. (https://doi.org/10.3389/fpls.2022.835305).
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2021R1A2C2007835), the Rural Development Administration (PJ016403), and the KAIST-funded Global Singularity Research PREP-Program.
-Publication:H. Kim, G. Lee, J. Song, and S.-G. Kim, "Real-time visualization of scent accumulation reveals the frequency of floral scent emissions," Frontiers in Plant Science 18, 835305 (2022) (https://doi.org/10.3389/fpls.2022.835305)
-Profile:Professor Hyoungsoo Kimhttp://fil.kaist.ac.kr
@MadeInH on TwitterDepartment of Mechanical EngineeringKAIST
Professor Sang-Gyu Kimhttps://sites.google.com/view/kimlab/home Department of Biological SciencesKAIST
2022.05.25 View 9982 -
Energy-Efficient AI Hardware Technology Via a Brain-Inspired Stashing System
Researchers demonstrate neuromodulation-inspired stashing system for the energy-efficient learning of a spiking neural network using a self-rectifying memristor array
Researchers have proposed a novel system inspired by the neuromodulation of the brain, referred to as a ‘stashing system,’ that requires less energy consumption. The research group led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a technology that can efficiently handle mathematical operations for artificial intelligence by imitating the continuous changes in the topology of the neural network according to the situation. The human brain changes its neural topology in real time, learning to store or recall memories as needed. The research group presented a new artificial intelligence learning method that directly implements these neural coordination circuit configurations.
Research on artificial intelligence is becoming very active, and the development of artificial intelligence-based electronic devices and product releases are accelerating, especially in the Fourth Industrial Revolution age. To implement artificial intelligence in electronic devices, customized hardware development should also be supported. However most electronic devices for artificial intelligence require high power consumption and highly integrated memory arrays for large-scale tasks. It has been challenging to solve these power consumption and integration limitations, and efforts have been made to find out how the human brain solves problems.
To prove the efficiency of the developed technology, the research group created artificial neural network hardware equipped with a self-rectifying synaptic array and algorithm called a ‘stashing system’ that was developed to conduct artificial intelligence learning. As a result, it was able to reduce energy by 37% within the stashing system without any accuracy degradation. This result proves that emulating the neuromodulation in humans is possible.
Professor Kim said, "In this study, we implemented the learning method of the human brain with only a simple circuit composition and through this we were able to reduce the energy needed by nearly 40 percent.”
This neuromodulation-inspired stashing system that mimics the brain’s neural activity is compatible with existing electronic devices and commercialized semiconductor hardware. It is expected to be used in the design of next-generation semiconductor chips for artificial intelligence.
This study was published in Advanced Functional Materials in March 2022 and supported by KAIST, the National Research Foundation of Korea, the National NanoFab Center, and SK Hynix.
-Publication:
Woon Hyung Cheong, Jae Bum Jeon†, Jae Hyun In, Geunyoung Kim, Hanchan Song, Janho An, Juseong Park, Young Seok Kim, Cheol Seong Hwang, and Kyung Min Kim (2022)
“Demonstration of Neuromodulation-inspired Stashing System for Energy-efficient Learning of Spiking Neural Network using a Self-Rectifying Memristor Array,” Advanced FunctionalMaterials March 31, 2022 (DOI: 10.1002/adfm.202200337)
-Profile:
Professor Kyung Min Kimhttp://semi.kaist.ac.kr https://scholar.google.com/citations?user=BGw8yDYAAAAJ&hl=ko
Department of Materials Science and EngineeringKAIST
2022.05.18 View 11605 -
Professor June-Koo Rhee’s Team Wins the QHack Open Hackathon Science Challenge
The research team consisting of three master students Ju-Young Ryu, Jeung-rak Lee, and Eyel Elala in Professor June-Koo Rhee’s group from the KAIST IRTC of Quantum Computing for AI has won the first place at the QHack 2022 Open Hackathon Science Challenge.
The QHack 2022 Open Hackathon is one of the world’s prestigious quantum software hackathon events held by US Xanadu, in which 250 people from 100 countries participate. Major sponsors such as IBM Quantum, AWS, CERN QTI, and Google Quantum AI proposed challenging problems, and a winning team is selected judged on team projects in each of the 13 challenges.
The KAIST team supervised by Professor Rhee received the First Place prize on the Science Challenge which was organized by the CERN QTI of the European Communities. The team will be awarded an opportunity to tour CERN’s research lab in Europe for one week along with an online internship.
The students on the team presented a method for “Leaning Based Error Mitigation for VQE,” in which they implemented an LBEM protocol to lower the error in quantum computing, and leveraged the protocol in the VQU algorithm which is used to calculate the ground state energy of a given molecule.
Their research successfully demonstrated the ability to effectively mitigate the error in IBM Quantum hardware and the virtual error model. In conjunction, Professor June-Koo (Kevin) Rhee founded a quantum computing venture start-up, Qunova Computing(https://qunovacomputing.com), with technology tranfer from the KAIST ITRC of Quantum Computing for AI. Qunova Computing is one of the frontier of the quantum software industry in Korea.
2022.04.08 View 7267 -
Label-Free Multiplexed Microtomography of Endogenous Subcellular Dynamics Using Deep Learning
AI-based holographic microscopy allows molecular imaging without introducing exogenous labeling agents
A research team upgraded the 3D microtomography observing dynamics of label-free live cells in multiplexed fluorescence imaging. The AI-powered 3D holotomographic microscopy extracts various molecular information from live unlabeled biological cells in real time without exogenous labeling or staining agents.
Professor YongKeum Park’s team and the startup Tomocube encoded 3D refractive index tomograms using the refractive index as a means of measurement. Then they decoded the information with a deep learning-based model that infers multiple 3D fluorescence tomograms from the refractive index measurements of the corresponding subcellular targets, thereby achieving multiplexed micro tomography. This study was reported in Nature Cell Biology online on December 7, 2021.
Fluorescence microscopy is the most widely used optical microscopy technique due to its high biochemical specificity. However, it needs to genetically manipulate or to stain cells with fluorescent labels in order to express fluorescent proteins. These labeling processes inevitably affect the intrinsic physiology of cells. It also has challenges in long-term measuring due to photobleaching and phototoxicity. The overlapped spectra of multiplexed fluorescence signals also hinder the viewing of various structures at the same time. More critically, it took several hours to observe the cells after preparing them.
3D holographic microscopy, also known as holotomography, is providing new ways to quantitatively image live cells without pretreatments such as staining. Holotomography can accurately and quickly measure the morphological and structural information of cells, but only provides limited biochemical and molecular information.
The 'AI microscope' created in this process takes advantage of the features of both holographic microscopy and fluorescence microscopy. That is, a specific image from a fluorescence microscope can be obtained without a fluorescent label. Therefore, the microscope can observe many types of cellular structures in their natural state in 3D and at the same time as fast as one millisecond, and long-term measurements over several days are also possible.
The Tomocube-KAIST team showed that fluorescence images can be directly and precisely predicted from holotomographic images in various cells and conditions. Using the quantitative relationship between the spatial distribution of the refractive index found by AI and the major structures in cells, it was possible to decipher the spatial distribution of the refractive index. And surprisingly, it confirmed that this relationship is constant regardless of cell type.
Professor Park said, “We were able to develop a new concept microscope that combines the advantages of several microscopes with the multidisciplinary research of AI, optics, and biology. It will be immediately applicable for new types of cells not included in the existing data and is expected to be widely applicable for various biological and medical research.”
When comparing the molecular image information extracted by AI with the molecular image information physically obtained by fluorescence staining in 3D space, it showed a 97% or more conformity, which is a level that is difficult to distinguish with the naked eye.
“Compared to the sub-60% accuracy of the fluorescence information extracted from the model developed by the Google AI team, it showed significantly higher performance,” Professor Park added.
This work was supported by the KAIST Up program, the BK21+ program, Tomocube, the National Research Foundation of Korea, and the Ministry of Science and ICT, and the Ministry of Health & Welfare.
-Publication
Hyun-seok Min, Won-Do Heo, YongKeun Park, et al. “Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning,” Nature Cell Biology (doi.org/10.1038/s41556-021-00802-x) published online December 07 2021.
-Profile
Professor YongKeun Park
Biomedical Optics Laboratory
Department of Physics
KAIST
2022.02.09 View 11607 -
3 KAIST PhD Candidates Selected as the 2021 Google PhD Fellows
PhD candidates Soo Ye Kim and Sanghyun Woo from the KAIST School of Electrical Engineering and Hae Beom Lee from the Kim Jaechul Graduate School of AI were selected as the 2021 Google PhD Fellows. The Google PhD Fellowship is a scholarship program that supports graduate school students from around the world that have produced excellent achievements from promising computer science-related fields. The 75 selected fellows will receive ten thousand dollars of funding with the opportunity to discuss research and receive one-on-one feedback from experts in related fields at Google.
Kim and Woo were named fellows in the field of "Machine Perception, Speech Technology and Computer Vision" with research of deep learning based super-resolution and computer vision respectively. Lee was named a fellow in the field of "Machine Learning" for his research in meta-learning.
Kim's research includes the formulation of novel methods for super-resolution and HDR video restoration and deep joint frame interpolation and super-resolution methods. Many of her works have been presented in leading conferences in computer vision and AI such as CVPR, ICCV, and AAAI. In addition, she has been collaborating as a research intern with the Vision Group Team at Adobe Research to study depth map refinement techniques.
(Kim's research on deep learning based joint super-resolution and inverse tone-mapping framework for HDR videos)
Woo’s research includes an effective deep learning model design based on the attention mechanism and learning methods based on self-learning and simulators. His works have been also presented in leading conferences such as CVPR, ECCV, and NeurIPS. In particular, his work on the Convolutional Block Attention Module (CBAM) which was presented at ECCV in 2018 has surpassed over 2700 citations on Google Scholar after being referenced in many computer vision applications. He was also a recipient of Microsoft Research PhD Fellowship in 2020.
(Woo's research on attention mechanism based deep learning models)
Lee’s research focuses effectively overcoming various limitations of the existing meta-learning framework. Specifically, he proposed to deal with a realistic task distribution with imbalances, improved the practicality of meta-knowledge, and made meta-learning possible even in large-scale task scenarios. These various studies have been accepted to numerous top-tier machine learning conferences such as NeurIPS, ICML, and ICLR. In particular, one of his papers has been selected as an oral presentation at ICLR 2020 and another as a spotlight presentation at NeurIPS 2020.
(Lee's research on learning to balance and continual trajectory shifting)
Due to the COVID-19 pandemic, the award ceremony was held virtually at the Google PhD Fellowship Summit from August 31st to September 1st. The list of fellowship recipients is displayed on the Google webpage.
2021.10.18 View 6476