Professor Jonghyun Choi’s research team in the Department of Electrical and Computer Engineering at Seoul National University has been selected for NVIDIA’s Academic Grant Program. The NVIDIA Academic Grant Program supports innovative artificial intelligence research at universities and accredited research institutions around the world. Selected through a review process, research teams receive access to the latest GPU infrastructure and research resources free of charge. As part of the program, Prof. Choi’s team will receive credits for approximately 34,000 hours of use on a Brev cloud platform node equipped with eight H100 80GB GPUs, as well as two RTX PRO 6000 Max-Q GPUs. Based on this support, the team plans to conduct research to improve the performance and generalization capabilities of Vision-Language-Action (VLA) models. VLA models are artificial intelligence models that understand visual information and language instructions and translate them into physical actions, effectively serving as the “brain” that enables robots to perform diverse tasks in various environments. Through this research, the team is expected to enhance the multi-task performance and generalization capabilities of VLA models and contribute to the development of core technologies in the field of Embodied AI. Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57808 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...

Professor Jaeyoung Do’s research team at the AIDAS Lab in the Department of Electrical and Computer Engineering at Seoul National University announced that its medical vision-language model (VLM), MEDIC-AD, has been accepted as an Oral Presentation and selected as an Award Candidate at CVPR 2026, one of the world’s most prestigious conferences in artificial intelligence and computer vision. MEDIC-AD was developed through clinical collaboration with Samsung Medical Center and joint research with the NVIDIA AI Technology Center (NVAITC), operated by NVIDIA, a global leader in semiconductors and AI. This research was designed to address a key limitation of existing medical AI models in that they possess broad medical knowledge but often lack the capabilities essential for real-world clinical practice, namely lesion detection, longitudinal symptom tracking, and visually explainable reasoning. Addressing Real Challenges in Clinical Practice MEDIC-AD focuses on solving three core tasks required in actual clinical settings: lesion detection, symptom tracking, and explainability. While most existing medical AI models have focused on acquiring vast amounts of medical knowledge, clinical practice requires more than knowledge alone. AI must be able to accurately identify abnormal lesions in medical images, determine whether a disease has improved or worsened compared with previous scans, and provide visual evidence that clinicians can verify. MEDIC-AD distinguishes itself from prior research by integrating all three capabilities into a single model. In particular, if AI can accurately classify patient progress as “no change,” “improved,” or “worsened” during follow-up, it can reduce the diagnostic burden on clinicians and help detect subtle changes at an early stage. The model is expected to have significant clinical impact, including the detection of early-stage lesions that may otherwise be missed and the rapid assessment of treatment response. Core AI Technology: Clinical Intelligence Built in Three Stages The key technical feature of MEDIC-AD is its stage-wise framework, a sequential learning structure composed of three stages. The first stage is anomaly detection. The research team inserted a new learning component, called an anomaly-aware token, into the transformer layers of the vision-language model. This token generates an Anomaly Attention Map, a probability map that distinguishes normal patches from abnormal patches, enabling the model to focus more effectively on lesion regions. Because this structure was trained across various imaging modalities, including brain MRI, head CT, and chest X-ray, the model can detect new diseases not included in the training data in a zero-shot setting. Figure 1. Architecture of the MEDIC-AD model The second stage is difference reasoning. Existing models typically process two images by simply concatenating them, which limits their ability to capture clinically meaningful changes over time. MEDIC-AD introduces a difference token that explicitly compares and separates anomaly features extracted from previous and current images of the same patient. This allows the model to precisely infer disease progression by identifying actual changes in lesions, without being misled by non-clinical variations such as overall brightness changes or differences in imaging angle. Figure 2. Example of MEDIC-AD evaluated on the MMXU benchmark. The model detects changes in findings between two X-ray images and visualizes the evidence behind its assessment. The third stage is visual explainability. To improve the reliability of AI-assisted diagnosis, the model must be able to show clinicians why it reached a particular conclusion. MEDIC-AD combines the tokens learned in the first stage with a ConvNeXt-based segmentation head to visualize, as a heatmap, the specific image regions that served as the basis for the model’s judgment. This is a key function that aligns the AI model’s conclusions with visual evidence, thereby improving clinical trust. Global Validation Through Joint Research with NVAITC In this research, collaboration with the NVIDIA AI Technology Center (NVAITC) went beyond computing support. It involved joint research across large-scale model optimization and overall research direction. Collaboration with NVIDIA, which possesses world-class AI infrastructure and expertise, contributed to improving the model’s robustness and global competitiveness. On the clinical data side, the team obtained long-term follow-up chest X-ray data from 300 real patients through collaboration with Professor Pa Hong’s research team at Samsung Changwon Hospital. Unlike typical AI studies that train and evaluate models on public benchmark datasets, this study validated model performance using data collected from real hospital workflows, further strengthening its potential for clinical application. Superior Performance Compared with Global Models Including GPT-4o and Claude The results showed that MEDIC-AD outperformed existing medical AI models as well as leading global large language models, including OpenAI’s GPT-4o and Anthropic’s Claude 3.5, across all three tasks: lesion detection, symptom tracking, and visual explainability. In particular, on MMXU, a benchmark for disease-change analysis based on long-term clinical data, MEDIC-AD achieved an overall accuracy of 65.5%, substantially outperforming next-generation foundation models such as Lingshu at 62.0% and Citrus-V at 57.1%. In the visual explainability metric mIoU, which evaluates heatmap quality, MEDIC-AD achieved a score of up to 87.6, far exceeding competing models such as Citrus-V, which recorded an mIoU of 32.6. This research was conducted by Woohyeon Park, Jaeik Kim, and Sunghwan Cho of the ECE Department at SNU, with Professor Jaeyoung Do serving as the corresponding author. The study was also selected as an exemplary case of the government’s Supplementary Budget High-Performance Computing Support Program. The research team plans to expand this work into next-generation multimodal medical foundation models that integrate medical imaging, clinical text, and patient data. Professor Do stated, “This research is meaningful because it goes beyond simply improving the performance of medical AI. It implements the actual clinical diagnostic process—detection, comparison, and explanation—inside the AI model itself. Through continued collaboration with hospitals and industry, we will work to develop trustworthy AI technologies that make tangible contributions to patient diagnosis and treatment.” Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57783 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...

The Applied Superconductivity Center at Seoul National University, led by Professor Seungyong Hahn of the Department of Electrical and Computer Engineering and the Electric Power Research Institute, announced that it has signed a three-phase joint research agreement worth £10.17 million (approximately KRW 20 billion) with UK Industrial Fusion Solutions (UKIFS), a wholly owned subsidiary of the United Kingdom Atomic Energy Authority (UKAEA) that leads the Spherical Tokamak for Energy Production (STEP) program. STEP is a major strategic national infrastructure project led by UKAEA. It aims to build a 100 MW-class commercial fusion power plant by the early 2040s, capable of supplying electricity to more than 200,000 four-person households. In June 2025, the UK government confirmed West Burton in Nottinghamshire as the construction site and announced an investment of £2.5 billion (approximately KRW 4.9 trillion) over five years to advance fusion energy development.* * Source: Major funding milestone for world-first prototype fusion plant - STEP Fusion The Applied Superconductivity Center and UKAEA laid the groundwork for this project through Phase 1 and Phase 2 joint research conducted over approximately two years, beginning in June 2024. In Phase 1, the team designed and fabricated a 3.6-meter-class, high-current, high-temperature superconducting cable prototype, achieving world-class performance and reliability. In Phase 2, the team developed manufacturing equipment for long-length cable production applicable to actual fusion magnets. In particular, the fabricated cable prototype underwent performance testing in July 2025 at the SULTAN test facility under the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. As a result, the prototype achieved the facility’s operational limits of an external magnetic field of 10.9 T and an operating current of 91 kA, corresponding to an electromagnetic force* of 100 tons per meter. The cable also demonstrated high reliability, with no performance degradation observed after more than 1,400 repeated charge-discharge cycles and intentional quench accident tests. This represents an unprecedented achievement in the field of high-temperature superconducting cables since the SULTAN ((German) SUpraLeiter Test ANlage) facility began operation in 1992. Furthermore, key performance indicators, including temperature-dependent critical current predictions, matched the values predicted in advance by analysis software independently developed by the SNU research team, demonstrating the precision of the team’s design technology. * Electromagnetic force: the product of current, perpendicular magnetic field, and length In Phase 2 of the research collaboration, which began in July 2025, the team moved beyond the conventional manual fabrication method for high-temperature superconducting cables and developed dedicated manufacturing equipment for long-length cable production required for future fusion magnets. In the newly agreed Phase 3 collaboration, the goal is to fabricate a 3-meter-scale prototype of a Toroidal Field Model Coil (TFMC) for fusion applications. This Phase 3 collaboration is expected to serve as an important turning point in raising the technology readiness level (TRL) of STEP’s high-temperature superconducting magnet technology, as the scope of cooperation has significantly expanded beyond the laboratory scale to long-length cable production using the team’s self-developed specialized manufacturing equipment and ultimately to the fabrication of a TFMC prototype. Behind these achievements is the work of the Project for Research and Innovation in Superconducting Magnet (PRISM), also known as the High-Temperature Superconducting Magnet Core Technology Research Group. PRISM is supported by the National Research Foundation of Korea under the Ministry of Science and ICT and led by the Applied Superconductivity Center at Seoul National University. The group is headed by Sangjin Lee, visiting professor in the ECE Department at SNU. Launched in 2022, PRISM carries out the High-Temperature Superconducting Magnet Technology Development Project from April 2022 to December 2026, under the vision of “the nation as one research institute and one university.” With a total budget of KRW 46.4 billion over five years, the project brings together 27 industry, university, and research institutions and more than 220 researchers. The group has systematized high-temperature superconducting magnets, which can be applied across a wide range of manufacturing industries, into four major configurations and seven key technologies for the first time in the world, and is developing core original technologies for mass production and high-end commercialization. Based on the results of the ongoing prototype development, the research team is currently discussing plans to expand the number of fusion model magnet prototypes produced and to participate in the fabrication of the final STEP model magnet. This marks the first case in which Korean technology could be applied to a core system of an actual fusion reactor, going beyond the simple supply of components. The collaboration is expected to open opportunities for Korean researchers and deep-tech companies to enter various advanced industrial fields, including future high-temperature-superconductivity-based fusion reactor construction projects, as well as biotechnology and materials, medicine, national defense, advanced science, and future mobility. Ultimately, the results of Korea’s original high-temperature superconducting technology development are expected to become a key foundation not only for the achievements of individual research institutions, but also for the expansion of the broader domestic industrial ecosystem into the global market and for securing national strategic technological competitiveness. For this joint research with UKIFS, SNU formed a response team together with PRISM participating companies PowerNix Co., Ltd. (CEO Kwanghee Yun) and Standard Magnet Inc. (CEO Jaemin Kim). The team worked closely together throughout the entire process of designing, fabricating, and evaluating the cable prototype, producing excellent outcomes. These achievements are linked to the Ministry of Science and ICT’s “Deep Science Startup Activation Support Program” and are now leading to the establishment of a domestic company specializing in high-temperature superconducting systems for fusion energy, centered on the response team. This collaboration is also being carried out as part of the Seoul National University Energy Initiative (SNU-EI), led by Professor Sung Jae Kim of the ECE Department. Accordingly, beyond the development of high-temperature superconducting magnet technology for fusion power, which is expected to become one pillar of future electricity production, the project is expected to lay the groundwork for collaboration with energy experts in the production division under SNU-EI. Through this collaboration, the team aims to examine key enabling technologies and technological limitations that can accelerate the practical commercialization of fusion energy, while expanding into various forms of technical cooperation and derivative projects. ▲ Figure 1. (Left) Schematic of the STEP fusion reactor being developed by the UKAEA (Source: https://step.ukaea.uk/) (Right) Configuration of an HTS magnet system for fusion applications: (1) wire; (2) cable; (3) magnet; (4) system. The UKAEA-SNU joint research agreement is expected to expand from cables to magnets and systems. (Source: Wire - https://sunam2004.tradekorea.com/main.do; Cable - provided by SNU; Magnet - K. J. Chung et al., Design and Fabrication of VEST at SNU, presented at 16th International Workshop on Spherical Torus, Sep. 27-30, 2011.; System - https://actu.epfl.ch/news/welcome-mast-upgrade-a-new-fusion-device/) ▲ Figure 2. (Left) Prototype of dedicated manufacturing equipment for long-length cable production developed at SNU (Right) Installation of a 12-meter-long facility based on the prototype ▲ Figure 3. (Left) The SULTAN test facility managed by the Swiss Plasma Center (SPC) under EPFL in Switzerland. The facility outlined by the light-green frame on the right is SULTAN. (Source: https://www.epfl.ch/research/domains/swiss-plasma-center/research/superconductivity/page-97675-en-html/) (Center) Photo of the HTS cable tested at the SULTAN facility. The cable achieved the facility’s operational limits of an external magnetic field of 10.9 T and an operating current of 91 kA, corresponding to an electromagnetic force of 100 tons per meter. (Right) Data recorded when the cable reached its maximum current, showing representative voltage (navy) and current (blue) in the high-field region of 10.9 T. The cable reached 91 kA at around 20 K. ▲ Figure 4. (Left) Equipment for fabricating a HTS cable former (Right) Photo of the fabricated 10-meter-class long-length cable former [Contact] - Professor Seungyong Hahn / 02-880-1495 / hahnsy@snu.ac.kr - Integrated M.S.-Ph.D. Candidate Dongwoo Lee / imdwl0830@snu.ac.kr [Reference] - STEP Website: https://stepfusion.com/uk-fusion-energy-strengthens-korea-partnership/ - College of Engineering Notice Board: https://eng.snu.ac.kr/communication/promotion/news?md=v&bbsidx=8075 - Maeil Business Newspaper Article: https://n.news.naver.com/article/009/0005682928?sid=105 Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57756 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...

A paper by researchers Jong-Won Back and Sungho Park from Professor Jong-Ho Lee’s research team in Seoul National University’s Department of Electrical and Computer Engineering was selected as the cover article for the June issue of Nano Energy (Impact Factor: 17.1), one of the world’s leading journals in the field of energy research. This research introduced a novel binary neural network architecture utilizing three-dimensional NAND flash memory, reducing energy consumption by approximately 98% compared to conventional architectures. The proposed approach also addressed limitations of previous designs that were susceptible to interference from neighboring data, resulting in improved computational accuracy. Given that 3D NAND flash memory is currently the most widely commercialized non-volatile memory technology, this work is expected to have significant implications for the development of low-power, high-performance neuromorphic computing systems based on commercially scalable memory platforms. Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57782 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...

▲ Professor Jongho Lee of Seoul National University’s ECE Department selected as an ISMRM Fellow Seoul National University’s College of Engineering announced that Professor Jongho Lee was selected as a Fellow of the International Society for Magnetic Resonance in Medicine (ISMRM) at the society’s 2026 annual meeting held in Cape Town, South Africa, during the third week of May. ISMRM is the world’s leading academic society in the field of magnetic resonance imaging (MRI), with more than 8,500 experts worldwide. Each year, only approximately the top 0.2% of members with outstanding research achievements are selected as Fellows. With this appointment, Prof. Lee became the only currently active Korean biomedical engineer to hold ISMRM Fellow status, further elevating the global standing of Korean biomedical engineering. After graduating from the Department of Electrical and Computer Engineering at Seoul National University, Prof. Lee earned his Ph.D. in electrical engineering from Standford University. He subsequently worked as a researcher at the National Institutes of Health (NIH) and as a professor in the Department of Radiology at the University of Pennsylvania before joining Seoul National University in 2014. Since then, he has established himself as a globally recognized scholar through pioneering research in the field of brain microstructure imaging. His research team is currently collaborating with leading international institutions, including Harvard Medical School, Max Planck Society, and Jülich Research Centre, to develop next-generation imaging technologies. Prof. Lee’s work has also gained recognition for its real-world impact in clinical medicine and industry. A Parkinson’s disease diagnostic solution developed by his team is currently being used in major Korean tertiary hospitals, including Seoul National University Hospital, Samsung Medical Center, and Severance Hospital. In addition, AIRS Medical, a medical AI startup founded by his former students, has expanded its MRI image reconstruction technology SwiftMR to more than 1,500 hospitals across over 40 countries. The company’s valuation has recently risen rapidly, bringing it close to unicorn status. Prof. Lee commented, “I would like to express my deepest gratitude to my students, who have devoted themselves tirelessly to research over the years, and to my family, who has always been my strongest support. I will continue to dedicate myself to advancing medical imaging technologies that can provide meaningful benefits to patients and to mentoring the next generation of researchers.” [Contact] Professor Jongho Lee, Laboratory for Imaging Science and Technology (LIST), Department of Electrical Engineering, Seoul National University / 02-880-7310 / jonghoyi@snu.ac.kr Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57768 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...

A research team from the Advanced TFTs and Circuits for Smart Electronics Laboratory (ACELAB), led by Professor Soo-Yeon Lee and including Ph.D. candidate Sunyeol Bae, received the Distinguished Student Paper Award at Display Week 2026, organized by the Society for Information Display (SID) and held in Los Angeles, USA, from May 3 to 8. Ph.D. candidate Sunyeol Bae delivered an oral presentation in the Novel TFT Structrues session under the title “Subthreshold Swing Control in IGZO TFTs Using Floating-Gate Engineering for AMOLED Displays.” The paper was selected as a Distinguished Paper, an honor awarded to only 36 papers among the 673 presented at the conference. Founded in 1962, SID is one of the world’s most prestigious international organizations in the field of display technology and celebrated its 64th anniversary this year. Display Week is the largest annual academic conference in the display field, attracting approximately 8,000 researchers and industry professionals each year. Meanwhile, the research was also published in the May 2026 issue of the Journal of the Society for Information Display (JSID). Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57767 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...

▲ Professor Jong-Ho Lee, Dr. Ryun-Han Koo, and Ph.D. candidate Jonghyun Ko of the ECE Department at Seoul National University Seoul National University’s College of Engineering announced that a research team led by Professor Jong-Ho Lee from the Department of Electrical and Computer Engineering has proposed the world’s first AI semiconductor technology that integrates the core functions of generative AI into a single ferroelectric memory–based device platform. This technology is particularly significant as it represents the world’s first demonstration of integrating the two key functions required for generative AI implementation—random sampling and stable computation—within a single memory array. By leveraging the voltage-dependent characteristics of ferroelectric memory, the research team implemented both probabilistic sampling using random telegraph noise (RTN) and deterministic computation using nonvolatile multilevel conductance states on a unified platform. The research findings were published in Nature Communications, one of the world’s leading international scientific journals. Generative AI has recently expanded rapidly into diverse applications, including image generation, video synthesis, autonomous systems, and personalized content creation. However, implementing generative AI directly on semiconductor chips remains a major challenge. Conventional AI semiconductors are primarily optimized for stable deterministic operations such as classification and inference, whereas generative models additionally require probabilistic functions capable of drawing random samples from latent spaces. As such, previous approaches have often separated probabilistic sampling and decoding across different devices or external software modules, leading to limitations such as increased chip area, wiring complexity, power consumption, and latency. In particular, integrating both functions within a single memory-based hardware platform while maintaining compatibility with conventional CMOS processes and scalability has remained a difficult challenge. To overcome these limitations, the research team focused on the voltage-dependent characteristics of hafnium oxide–based ferroelectric memory. In high-voltage regions, strong RTN emerges, enabling probabilistic sampling behavior, whereas in low-voltage regions, RTN is suppressed, allowing stable vector–matrix multiplication (VMM) operations using nonvolatile multilevel conductance states. Based on this mechanism, the team proposed a strategy for simultaneously realizing both randomness and computational stability required for generative AI within a single memory array. This technology is particularly significant in that it integrates the sampling and decoding functions—previously separated in generative AI hardware—into a single ferroelectric memory–based platform. Without requiring a separate external random number generation module, the same device can perform different functions depending on its operating regime, demonstrating the potential to simultaneously improve integration density and power efficiency in future generative AI semiconductors. The research team experimentally validated the concept using a NOR-type ferroelectric memory array fabricated on a 6-inch wafer. After optimizing the latent vector distribution by adjusting voltage and sampling time, the team applied the system to a variational autoencoder (VAE) and conducted image-generation experiments using the CelebA facial image dataset. The results confirmed the ability to generate images reflecting diverse facial attributes, while circuit-level verification demonstrated stable generative performance even after approximately 100,000 repeated operations. This study is meaningful in that it demonstrated that two functions long treated separately in generative AI hardware can be integrated within a single CMOS-compatible ferroelectric memory–based device platform. The technology presents promising opportunities for simultaneously improving area efficiency and power efficiency in future on-chip generative AI accelerators, neuromorphic systems, and low-power edge AI semiconductors. Ferroelectric memory, in particular, offers strong potential for future scalability toward large-scale generative AI hardware systems thanks to its high compatibility with existing semiconductor fabrication processes. Moving forward, the research team plans to further advance the technology toward real-time generative AI hardware through improvements in sampling speed, parallelism, array scale, and peripheral circuit optimization. Prof. Jong-Ho Lee, who led the study, stated, “One of the key challenges in generative AI hardware is simultaneously achieving random sampling and deterministic computation. This study is meaningful in that it showed how the voltage-dependent characteristics of ferroelectric memory can be utilized to realize both functions within a single device platform.” The study’s co-first authors, Ryun-Han Koo and Jonghyun Ko, are currently conducting research in Prof. Jongho Lee’s group at Seoul National University’s Department of Electrical and Computer Engineering, focusing on memory semiconductors, hardware AI, and low-power neuromorphic systems. ▲ Figure 1. Overview of the proposed ferroelectric memory–based hardware VAE system. The ferroelectric memory array simultaneously performs probabilistic latent-variable sampling using RTN and deterministic decoding based on VMM. ▲ Figure 2. Verification of noise control and image-generation performance in ferroelectric memory. The RTN-based randomness of the memory device varies depending on the applied voltage and sampling time conditions, resulting in changes to the generated latent vector distribution and image quality. Under optimized conditions, the research team confirmed balanced image generation on the CelebA dataset. [Reference] Paper/Journal : “CMOS compatible ferroelectric tunnel junctions integrate stochastic sampling and deterministic computing for image generation”, Nature Communications DOI: 10.1038/s41467-026-72969-6 Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57729 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...

A research team led by Prof. Jae-Hyeung Park in the Three-Dimensional Optical Engineering Laboratory (3DOEL), consisting of researchers Minseong Kim and Myeong-Ho Choi, received the Distinguished Paper Award at Display Week 2026, held in Los Angeles, USA, from May 3 to 8. The team delivered an oral presentation in the Holographic and XR Displays session under the title “Thin Maxwellian Virtual Reality Near-Eye Display Using Holographic Optical Element and Micro Lens Array.” Approximately 1,000 papers were submitted to Display Week 2026, of which 673 were accepted for presentation. Among them, only 36 outstanding papers were selected for the Distinguished Paper honor. Founded in 1962 and celebrating its 64th anniversary this year, the Society for Information Display is one of the world’s most prestigious international organizations in the field of display technology. Each year, approximately 8,000 researchers and industry professionals attend the conference to share the latest advances and research achievements in display technologies. Meanwhile, the research was also selected as the front-cover article of the May 2026 issue of the Journal of the Society for Information Display. ▲ Front cover of the May 2026 issue of the Journal of the Society for Information Display Source: https://ece.snu.ac.kr/ece/news?md=v&bbsidx=57744 Translated by: Changhoon Kang, English Editor of the Department of Electrical and Computer Engineering, changhoon27@snu.ac.kr...