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Professor Kuk-Jin Yoon’s Research Team at the Department of Mechanical Engineering Achieves Landmark Success with 10 Papers Accepted at CVPR 2026
<Professor Kuk-Jin Joon from Department of Mechanical Engineering>
Professor Kuk-Jin Yoon’s research team from our university’s Department of Mechanical Engineering has once again demonstrated its overwhelming academic prowess by having a total of 10 papers accepted as lead authors at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026).
CVPR is the most influential international conference in the fields of artificial intelligence and visual intelligence. Since its inception in 1983, it has selected outstanding research through a rigorous peer-review process every year. For CVPR 2026, a total of 16,092 papers were submitted worldwide, with 4,090 accepted, resulting in a competitive acceptance rate of approximately 25.42%. Achieving 10 accepted papers as lead or corresponding authors from a single laboratory is regarded as an exceptionally rare and world-class feat.
Professor Kuk-Jin Yoon’s team conducts extensive research with the ultimate goal of achieving human-level visual intelligence. The papers accepted this year cover cutting-edge topics in computer vision, including:
Event camera-based technologies
Perception technologies for autonomous driving
AI optimization and adaptation techniques
This achievement follows the team's remarkable success at ICCV 2025 last year, where they published 12 papers as lead/corresponding authors. The results at CVPR 2026 further solidify the laboratory's position as a global hub for pioneering computer vision research. The research team plans to continue contributing to the advancement of future AI technologies by tackling challenging research that transcends the limitations of existing methods.
Meanwhile, CVPR 2026 is scheduled to be held in Denver, Colorado, USA, from June 3 to June 7.
<CVPR 2026 (Denver, USA)>
2026.03.06 View 2425 -
KAIST Researchers First in the World to Identify Security Threat Exploiting Google Gemini’s "Malicious Expert AI" Structure
<Photo 1. (From left) Ph.D. candidates Mingyoo Song and Jaehan Kim, Professor Sooel Son, (Top right) Professor Seungwon Shin, Lead Researcher Seung Ho Na>
Most major commercial Large Language Models (LLMs), such as Google’s Gemini, utilize a Mixture-of-Experts (MoE) structure. This architecture enhances efficiency by dynamically selecting and using multiple "small AI models (Expert AIs)" depending on input queries . However, KAIST research team has revealed for the first time in the world that this very structure can actually become a new security threat.
A joint research team led by Professor Seungwon Shin (School of Electrical Engineering) and Professor Sooel Son (School of Computing) announced on December 26th that they have identified an attack technique that can seriously compromise the safety of LLMs by exploiting the MoE structure. For this research, they received the Distinguished Paper Award at ACSAC 2025, one of the most prestigious international conferences in the field of information security.
ACSAC (Annual Computer Security Applications Conference) is among the most influential international academic conferences in security. This year, only two papers out of all submissions were selected as Distinguished Papers. It is highly unusual for a domestic Korean research team to achieve such a feat in the field of AI security.
In this study, the team systematically analyzed the fundamental security vulnerabilities of the MoE structure. In particular, they demonstrated that even if an attacker does not have direct access to the internal structure of a commercial LLM, the entire model can be induced to generate dangerous responses if just one maliciously manipulated "Expert Model" is distributed through open-source channels and integrated into the system.
<Figure 1. Conceptual diagram of the attack technology proposed by the research team.>
To put it simply: even if there is only one "malicious expert" mixed among normal AI experts, that specific expert may be repeatedly selected for processing harmful queries, causing the overall safety of the AI to collapse. A particularly dangerous factor highlighted was that this process causes almost no degradation in model performance, making the problem extremely difficult to detect in advance.
Experimental results showed that the attack technique proposed by the research team could increase the harmful response rate from 0% to up to 80%. They confirmed that the safety of the entire model significantly deteriorates even if only one out of many experts is "infected."
This research is highly significant as it presents the first new security threat that can occur in the rapidly expanding global open-source-based LLM development environment. Simultaneously, it suggests that verifying the "source and safety of individual expert models" is now essential—not just performance—during the AI model development process.
Professors Seungwon Shin and Sooel Son stated, "Through this study, we have empirically confirmed that the MoE structure, which is spreading rapidly for the sake of efficiency, can become a new security threat. This award is a meaningful achievement that recognizes the importance of AI security on an international level."
The study involved Ph.D. candidates Jaehan Kim and Mingyoo Song, Dr. Seung Ho Na (currently at Samsung Electronics), Professor Seungwon Shin, and Professor Sooel Son. The results were presented at ACSAC in Hawaii, USA, on December 12, 2025.
<Figure 2. Photo of the Distinguished Paper Award certificate>
Paper Title: MoEvil: Poisoning Experts to Compromise the Safety of Mixture-of-Experts LLMs
Paper File: https://jaehanwork.github.io/files/moevil.pdf
GitHub (Open Source): https://github.com/jaehanwork/MoEvil
This research was supported by the Korea Internet & Security Agency (KISA) and the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Ministry of Science and ICT.
2025.12.26 View 2796 -
KAIST & CMU Unveils Amuse, a Songwriting AI-Collaborator to Help Create Music
Wouldn't it be great if music creators had someone to brainstorm with, help them when they're stuck, and explore different musical directions together? Researchers of KAIST and Carnegie Mellon University (CMU) have developed AI technology similar to a fellow songwriter who helps create music.
KAIST (President Kwang-Hyung Lee) has developed an AI-based music creation support system, Amuse, by a research team led by Professor Sung-Ju Lee of the School of Electrical Engineering in collaboration with CMU. The research was presented at the ACM Conference on Human Factors in Computing Systems (CHI), one of the world’s top conferences in human-computer interaction, held in Yokohama, Japan from April 26 to May 1. It received the Best Paper Award, given to only the top 1% of all submissions.
< (From left) Professor Chris Donahue of Carnegie Mellon University, Ph.D. Student Yewon Kim and Professor Sung-Ju Lee of the School of Electrical Engineering >
The system developed by Professor Sung-Ju Lee’s research team, Amuse, is an AI-based system that converts various forms of inspiration such as text, images, and audio into harmonic structures (chord progressions) to support composition.
For example, if a user inputs a phrase, image, or sound clip such as “memories of a warm summer beach”, Amuse automatically generates and suggests chord progressions that match the inspiration.
Unlike existing generative AI, Amuse is differentiated in that it respects the user's creative flow and naturally induces creative exploration through an interactive method that allows flexible integration and modification of AI suggestions.
The core technology of the Amuse system is a generation method that blends two approaches: a large language model creates music code based on the user's prompt and inspiration, while another AI model, trained on real music data, filters out awkward or unnatural results using rejection sampling.
< Figure 1. Amuse system configuration. After extracting music keywords from user input, a large language model-based code progression is generated and refined through rejection sampling (left). Code extraction from audio input is also possible (right). The bottom is an example visualizing the chord structure of the generated code. >
The research team conducted a user study targeting actual musicians and evaluated that Amuse has high potential as a creative companion, or a Co-Creative AI, a concept in which people and AI collaborate, rather than having a generative AI simply put together a song.
The paper, in which a Ph.D. student Yewon Kim and Professor Sung-Ju Lee of KAIST School of Electrical and Electronic Engineering and Carnegie Mellon University Professor Chris Donahue participated, demonstrated the potential of creative AI system design in both academia and industry. ※ Paper title: Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations DOI: https://doi.org/10.1145/3706598.3713818
※ Research demo video: https://youtu.be/udilkRSnftI?si=FNXccC9EjxHOCrm1
※ Research homepage: https://nmsl.kaist.ac.kr/projects/amuse/
Professor Sung-Ju Lee said, “Recent generative AI technology has raised concerns in that it directly imitates copyrighted content, thereby violating the copyright of the creator, or generating results one-way regardless of the creator’s intention. Accordingly, the research team was aware of this trend, paid attention to what the creator actually needs, and focused on designing an AI system centered on the creator.”
He continued, “Amuse is an attempt to explore the possibility of collaboration with AI while maintaining the initiative of the creator, and is expected to be a starting point for suggesting a more creator-friendly direction in the development of music creation tools and generative AI systems in the future.”
This research was conducted with the support of the National Research Foundation of Korea with funding from the government (Ministry of Science and ICT). (RS-2024-00337007)
2025.05.07 View 15429 -
KAIST achieves quantum entanglement essential for quantum error correction
Quantum computing is a technology capable of solving complex problems that classical computers struggle with. To perform accurate computations, quantum computers must correct errors that arise during operations. However, generating the quantum entanglement necessary for quantum error correction has long been considered a major challenge.
< Photo 1. (From left) Students Young-Do Yoon and Chan Roh of the Master's and Doctoral Integrated Program of the Department of Physics poses with Professor Young-Sik Ra and Student Geunhee Gwak of the same program >
KAIST (represented by President Kwang Hyung Lee) announced on the 25th of February that a research team led by Professor Young-Sik Ra from the Department of Physics has successfully implemented a three-dimensional cluster quantum entangled state, a key component for quantum error correction, through experimental demonstration.
Measurement-based quantum computing is an emerging paradigm that implements quantum computations by measuring specially entangled cluster states. The core of this approach lies in the generation of these cluster quantum entangled states, with two-dimensional cluster states commonly used for universal quantum computing.
However, to advance towards fault-tolerant quantum computing, which can correct quantum errors occurring during computations, a more complex three-dimensional cluster state is required. While previous studies have reported the generation of two-dimensional cluster states, experimental implementation of the three-dimensional cluster states necessary for fault-tolerant quantum computing had remained elusive due to the extreme complexity of their entanglement structure.
< Figure 1. (a) Experimental schematic. A pulse laser with a wavelength of 800 nm is converted into a pulse laser with a wavelength of 400 nm through second harmonic generation, and this is incident on a nonlinear crystal (PPKTP) to generate multiple quantum entanglement sources. (b) Generation of a 3D cluster state through optical mode basis change >
The research team overcame this challenge by developing a technique to control femtosecond time-frequency modes, successfully generating a three-dimensional cluster quantum entangled state for the first time.
The team directed a femtosecond laser into a nonlinear crystal, simultaneously generating quantum light sources across multiple frequency modes. (A femtosecond laser is a device that emits ultrashort, high-intensity light pulses.) Using this approach, they successfully created a three-dimensional cluster quantum entangled state.
Professor Young-Sik Ra noted, “This study marks the first successful demonstration of a three-dimensional cluster quantum entangled state, which was previously difficult to achieve with existing technology. This breakthrough is expected to serve as a crucial stepping stone for future research in measurement-based and fault-tolerant quantum computing.”
< Figure 2. Results of 3D cluster state generation. (a) Nullifier measurement of the cluster state. (b) 3D cluster state reconstructed using quantum state tomography. (c) Confirmation of quantum entanglement characteristics of the 3D cluster state >
The study was published online in Nature Photonics on February 24, 2025. The first author is Chan Roh, a Ph.D. candidate in KAIST’s integrated master’s and doctoral program, with Geunhee Gwak and Youngdo Yoon contributing as co-authors. (Paper title: “Generation of Three-Dimensional Cluster Entangled State”, DOI: 10.1038/s41566-025-01631-2)
This research was supported by the National Research Foundation of Korea (Quantum Computing Technology Development Program, Mid-Career Researcher Support Program, and Quantum Simulator for Materials Innovation Program), the Institute for Information & Communications Technology Planning & Evaluation (Quantum Internet Core Technology Program, University ICT Research Center Support Program), and the U.S. Air Force Research Laboratory.
2025.02.25 View 9134 -
KAIST Awarded Presidential Commendation for Contributions in Software Industry
- At the “25th Software Industry Day” celebration held in the afternoon on Monday, December 2nd, 2024 at Yangjae L Tower in Seoul
- KAIST was awarded the “Presidential Commendation” for its contributions for the advancement of the Software Industry in the Group Category
- Korea’s first AI master’s and doctoral degree program opened at KAIST Kim Jaechul Graduate School of AI
- Focus on training non-major developers through SW Officer Training Academy "Jungle", Machine Learning Engineer Bootcamp, etc., talents who can integrate development and collaboration, and advanced talents in the latest AI technologies.
- Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI received Prime Minister’s Commendation for his contributions for the advancement of the software industry.
< Photo 1. Professor Kyung-soo Kim, the Senior Vice President for Planning and Budget (second from the left) and the Manager of Planning Team, Mr. Sunghoon Jung, stand at the stage after receiving the Presidential Commendation as KAIST was selected as one of the groups that contributed to the advancement of the software industry at the "25th Software Industry Day" celebration. >
“KAIST has been leading the way in achieving the grand goal of fostering 1 million AI talents in Korea by services that pan from providing various educational opportunities, from developing the capabilities of experts with no computer science specialty to fostering advanced professionals. I would like to thank all members of KAIST community who worked hard to achieve the great feat of receiving the Presidential Commendations.” (KAIST President Kwang Hyung Lee)
KAIST (President Kwang Hyung Lee) announced on December 3rd that it was selected as a group that contributed to the advancement of the software industry at the “2024 Software Industry Day” celebration held at the Yangjae El Tower in Seoul on the 2nd of December and received a presidential commendation.
The “Software Industry Day”, hosted by the Ministry of Science and ICT and organized by the National IT Industry Promotion Agency and the Korea Software Industry Association, is an event designed to promote the status of software industry workers in Korea and to honor their achievements.
Every year, those who have made significant contributions to policy development, human resource development, and export growth for industry revitalization are selected and awarded the ‘Software Industry Development Contribution Award.’
KAIST was recognized for its contribution to developing a demand-based, industrial field-centric curriculum and fostering non-major developers and convergence talents with the goal of expanding software value and fostering excellent human resources.
< Photo 2. Senior Vice President for Planning and Budget Kyung-soo Kim receiving the commendation as the representative of KAIST >
Specifically, it first opened the SW Officer Training Academy "Jungle" to foster convergent program developers equipped with the abilities to handle both the computer coding and human interactions for collaborations. This is a non-degree program that provides intensive study and assignments for 5 months for graduates and intellectuals without prior knowledge of computer science.
KAIST Kim Jaechul Graduate School of AI opened and operated Korea’s first master's and doctoral degree program in the field of artificial intelligence. In addition, it planned a “Machine Learning Engineers’ Boot Camp” and conducted lectures and practical training for a total of 16 weeks on the latest AI technologies such as deep learning basics and large language models. It aims to strengthen the practical capabilities of start-up companies while lowering the threshold for companies to introduce AI technology.
Also, KAIST was selected to participate in the 1st and 2nd stages of the Software-centered University Project and has been taking part in the project since 2016. Through this, it was highly evaluated for promoting curriculum based on latest technology, an autonomous system where students directly select integrated education, and expansion of internships.
< Photo 3. Professor Minjoon Seo of Kim Jaechul Graduate School of AI, who received the Prime Minister's Commendation for his contribution to the advancement of the software industry on the same day >
At the awards ceremony that day, Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI also received the Prime Minister's Commendation for his contribution to the advancement of the software industry. Professor Seo was recognized for his leading research achievements in the fields of AI and natural language processing by publishing 28 papers in top international AI conferences over the past four years.
At the same time, he was noted for his contributions to enhancing the originality and innovation of language model research, such as △knowledge encoding, △knowledge access and utilization, and △high-dimensional inference performance, and for demonstrating leadership in the international academic community.
President Kwang Hyung Lee of KAIST stated, “Our university will continue to do its best to foster software talents with global competitiveness through continuous development of cutting-edge curriculum and innovative degree systems.”
2024.12.03 View 14345 -
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 15007 -
LightPC Presents a Resilient System Using Only Non-Volatile Memory
Lightweight Persistence Centric System (LightPC) ensures both data and execution persistence for energy-efficient full system persistence
A KAIST research team has developed hardware and software technology that ensures both data and execution persistence. The Lightweight Persistence Centric System (LightPC) makes the systems resilient against power failures by utilizing only non-volatile memory as the main memory.
“We mounted non-volatile memory on a system board prototype and created an operating system to verify the effectiveness of LightPC,” said Professor Myoungsoo Jung. The team confirmed that LightPC validated its execution while powering up and down in the middle of execution, showing up to eight times more memory, 4.3 times faster application execution, and 73% lower power consumption compared to traditional systems.
Professor Jung said that LightPC can be utilized in a variety of fields such as data centers and high-performance computing to provide large-capacity memory, high performance, low power consumption, and service reliability.
In general, power failures on legacy systems can lead to the loss of data stored in the DRAM-based main memory. Unlike volatile memory such as DRAM, non-volatile memory can retain its data without power. Although non-volatile memory has the characteristics of lower power consumption and larger capacity than DRAM, non-volatile memory is typically used for the task of secondary storage due to its lower write performance. For this reason, nonvolatile memory is often used with DRAM. However, modern systems employing non-volatile memory-based main memory experience unexpected performance degradation due to the complicated memory microarchitecture.
To enable both data and execution persistent in legacy systems, it is necessary to transfer the data from the volatile memory to the non-volatile memory. Checkpointing is one possible solution. It periodically transfers the data in preparation for a sudden power failure. While this technology is essential for ensuring high mobility and reliability for users, checkpointing also has fatal drawbacks. It takes additional time and power to move data and requires a data recovery process as well as restarting the system.
In order to address these issues, the research team developed a processor and memory controller to raise the performance of non-volatile memory-only memory. LightPC matches the performance of DRAM by minimizing the internal volatile memory components from non-volatile memory, exposing the non-volatile memory (PRAM) media to the host, and increasing parallelism to service on-the-fly requests as soon as possible.
The team also presented operating system technology that quickly makes execution states of running processes persistent without the need for a checkpointing process. The operating system prevents all modifications to execution states and data by keeping all program executions idle before transferring data in order to support consistency within a period much shorter than the standard power hold-up time of about 16 minutes. For consistency, when the power is recovered, the computer almost immediately revives itself and re-executes all the offline processes immediately without the need for a boot process.
The researchers will present their work (LightPC: Hardware and Software Co-Design for Energy-Efficient Full System Persistence) at the International Symposium on Computer Architecture (ISCA) 2022 in New York in June. More information is available at the CAMELab website (http://camelab.org).
-Profile: Professor Myoungsoo Jung Computer Architecture and Memory Systems Laboratory (CAMEL)http://camelab.org School of Electrical EngineeringKAIST
2022.04.25 View 28566 -
Professor Sung-Ju Lee’s Team Wins the Best Paper and the Methods Recognition Awards at the ACM CSCW
A research team led by Professor Sung-Ju Lee at the School of Electrical Engineering won the Best Paper Award and the Methods Recognition Award from ACM CSCW (International Conference on Computer-Supported Cooperative Work and Social Computing) 2021 for their paper “Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis”.
Founded in 1986, CSCW has been a premier conference on HCI (Human Computer Interaction) and Social Computing. This year, 340 full papers were presented and the best paper awards are given to the top 1% papers of the submitted. Methods Recognition, which is a new award, is given “for strong examples of work that includes well developed, explained, or implemented methods, and methodological innovation.”
Hyunsung Cho (KAIST alumus and currently a PhD candidate at Carnegie Mellon University), Daeun Choi (KAIST undergraduate researcher), Donghwi Kim (KAIST PhD Candidate), Wan Ju Kang (KAIST PhD Candidate), and Professor Eun Kyoung Choe (University of Maryland and KAIST alumna) collaborated on this research.
The authors developed a tool that tracks and analyzes which features of a mobile app (e.g., Instagram’s following post, following story, recommended post, post upload, direct messaging, etc.) are in use based on a smartphone’s User Interface (UI) layout. Utilizing this novel method, the authors revealed which feature usage patterns result in regretful smartphone use.
Professor Lee said, “Although many people enjoy the benefits of smartphones, issues have emerged from the overuse of smartphones. With this feature level analysis, users can reflect on their smartphone usage based on finer grained analysis and this could contribute to digital wellbeing.”
2021.11.22 View 13899 -
‘Game&Art: Auguries of Fantasy’ Features Future of the Metaverse
‘Game & Art: Auguries of Fantasy,’ a special exhibition combining art and technology will feature the new future of metaverse fantasy. The show will be hosted at the Daejeon Creative Center at the Daejeon Museum of Art through September 5.
This show exhibits a combination of science and technology with culture and arts, and introduces young artists whose creativity will lead to new opportunities in games and art. The Graduate School of Culture Technology was designated as a leading culture content academy in 2020 by the Ministry of Culture, Sports & Tourism and the Korea Creative Content Agency for fostering the R&D workforce in creative culture technology.
NCsoft sponsored the show and also participated as an artist. It combined its game-composing elements and technologies with other genres, including data for game construction, scenarios for forming a worldview, and game art and sound. All of the contents can be experienced online in a virtual space as well as offline, and can be easily accessed through personal devices.
Characterized by the themes ‘timeless’ and ‘spaceless’ which connect the past, present, and future, and space created in the digital world. The exhibition gives audience members an opportunity to experience freedom beyond the constraints of time and space under the theme of a fantasy reality created by games and art.
"Computer games, which began in the 1980s, have become cultural content that spans generations, and games are now the fusion field for leading-edge technologies including computer graphics, sound, human-computer interactions, big data, and AI. They are also the best platform for artistic creativity by adding human imagination to technology," said Professor Joo-Han Nam from the Graduate School of Culture Technology, who led the project.
"Our artists wanted to convey various messages to our society through works that connect the past, present, and future through games."
Ju-young Oh's "Unexpected Scenery V2" and "Hope for Rats V2" display game-type media work that raises issues surrounding technology, such as the lack of understanding behind various scientific achievements, the history of accidental achievements, and the side effects of new conveniences.
Tae-Wan Kim, in his work themed ‘healing’ combined the real-time movement of particles which follows the movements of people recorded as digital data. Metadata is collected by sensors in the exhibition space, and floating particle forms are evolved into abstract graphic designs according to audio-visual responses.
Meanwhile, ‘SOS’ is a collaboration work from six KAIST researchers (In-Hwa Yeom, Seung-Eon Lee, Seong-Jin Jeon, Jin-Seok Hong, Hyung-Seok Yoon, and Sang-Min Lee). SOS is based on diverse perspectives embracing phenomena surrounding contemporary natural resources. Audience members follow a gamified path between the various media-elements composing the art’s environment. Through this process, the audience can experience various emotions such as curiosity, suspicion, and recovery.
‘Diversity’ by Sung-Hyun Kim uses devices that recognize the movements of hands and fingers to provide experiences exploring the latent space of game play images learned by deep neural networks. Image volumes generated by neural networks are visualized through physics-based, three-dimensional, volume-rendering algorithms, and a series of processes were implemented based on the self-written code.
2021.06.21 View 15882 -
Taesik Gong Named Google PhD Fellow
PhD candidate Taesik Gong from the School of Computing was named a 2020 Google PhD Fellow in the field of machine learning.
The Google PhD Fellowship Program has recognized and supported outstanding graduate students in computer science and related fields since 2009. Gong is one of two Korean students chosen as the recipients of Google Fellowships this year. A total of 53 students across the world in 12 fields were awarded this fellowship.
Gong’s research on condition-independent mobile sensing powered by machine learning earned him this year’s fellowship. He has published and presented his work through many conferences including ACM SenSys and ACM UbiComp, and has worked at Microsoft Research Asia and Nokia Bell Labs as a research intern. Gong was also the winner of the NAVER PhD Fellowship Award in 2018.
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2020.10.15 View 19888 -
Professor Dongsu Han Named Program Chair for ACM CoNEXT 2020
Professor Dongsu Han from the School of Electrical Engineering has been appointed as the program chair for the 16th Association for Computing Machinery’s International Conference on emerging Networking EXperiments and Technologies (ACM CoNEXT 2020). Professor Han is the first program chair to be appointed from an Asian institution.
ACM CoNEXT is hosted by ACM SIGCOMM, ACM's Special Interest Group on Data Communications, which specializes in the field of communication and computer networks.
Professor Han will serve as program co-chair along with Professor Anja Feldmann from the Max Planck Institute for Informatics. Together, they have appointed 40 world-leading researchers as program committee members for this conference, including Professor Song Min Kim from KAIST School of Electrical Engineering.
Paper submissions for the conference can be made by the end of June, and the event itself is to take place from the 1st to 4th of December.
Conference Website: https://conferences2.sigcomm.org/co-next/2020/#!/home
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2020.06.02 View 17329 -
Professor Hojong Chang’s Research Team Wins ISIITA 2020 Best Paper Award
The paper written by Professor Hojong Chang’s research team from KAIST Institute for IT Convergence won the best paper award from the International Symposium on Innovation in Information Technology Application (ISIITA) 2020, held this month at Ton Duc Thang University in Vietnam.
ISIITA is a networking symposium where leading researchers from various fields including information and communications, biotechnology, and computer systems come together and share on the convergence of technology.
Professor Chang’s team won the best paper award at this year’s symposium with its paper, “A Study of Single Photon Counting System for Quantitative Analysis of Luminescence”. The awarded paper discusses the realization of a signal processing system for silicon photomultipliers.
The silicon photomultiplier is the core of a urinalysis technique that tests for sodium and potassium in the body using simple chemical reactions. If our bodily sodium and potassium levels exceed a certain amount, it can lead to high blood pressure, cardiovascular problems, and kidney damage.
Through this research, the team has developed a core technique that quantifies the sodium and potassium discharged in the urine. When the reagent is injected into the urine, a very small amount of light is emitted as a result of the chemical reaction. However, if there is a large amount of sodium and potassium, they interrupt the reaction and reduce the emission. The key to this measurement technique is digitizing the strength of this very fine emission of light. Professor Chang’s team developed a system that uses a photomultiplier to measure the chemiluminescence.
Professor Chang said, “I look forward for this signal processing system greatly helping to prevent diseases caused by the excessive consumption of sodium and potassium through quick and easy detection.”
Researcher Byunghun Han who carried out the central research for the system design added, “We are planning to focus on miniaturizing the developed technique, so that anyone can carry our device around like a cellphone.”
The research was supported by the Ministry of Science and ICT.
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2020.02.27 View 17218