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KAIST Develops AI That Reads Animal Behavior Like Language An artificial intelligence model capable of reading and interpreting animal behavior like language has been developed by researchers at KAIST. The team created BehaVERT, an AI model that learns behavioral data in a manner similar to natural language and was able to independently identify social behavioral deficits in an autism mouse model, opening a new avenue for interpretable neuroscience. KAIST (President Kwang-Hyung Lee) announced that a research team led by Professor Dae-Soo Kim from the Department of Brain and Cognitive Sciences has developed an AI model that interprets animal movements as a form of behavioral language. The researchers transformed skeletal movements of mice into tokens, analogous to words in natural language, and trained a transformer-based model to learn behavioral meaning. The resulting model, named BehaVERT, successfully identified core social behavioral abnormalities in an autism mouse model without being provided any prior biological knowledge. The study introduces a novel AI framework for analyzing animal behavior through language-based representations. Beyond simple behavior classification, the model demonstrates the ability to uncover biologically meaningful patterns and may serve as a foundation for next-generation behavioral foundation models applicable to drug discovery, psychiatric research, and behavioral genetics. Inspired by the idea that animal behavior may possess structures similar to language, the researchers represented the positions of a mouse's nose, ears, spine, limbs, and tail as behavioral tokens and trained a BERT-based transformer architecture. As a result, BehaVERT learned not only to classify behaviors but also to understand their contextual meaning over time, much like language models infer meaning from sequences of words. The model achieved state-of-the-art performance across five international benchmark datasets covering social interaction, multi-animal behavior, three-dimensional motion analysis, and autism-related behavioral assessment. Importantly, BehaVERT also provides interpretability, allowing researchers to visualize which behavioral cues influenced its decisions. In experiments distinguishing Shank3B knockout autism-model mice from healthy controls, the AI consistently focused on oral-oral contact behavior. This finding aligns with previous biological studies showing that autism-model mice exhibit deficits in social interaction despite maintaining normal approach behavior. In other words, the AI independently rediscovered a key biological characteristic solely from behavioral observations, without explicit biological instruction. The researchers further found that the model's internal representation space organized behavioral features such as mobility, attention, and social engagement into structured patterns. This suggests that animal behavior, much like language, may possess an underlying semantic structure. The study also highlights an unusual interdisciplinary achievement. The first author, Dr. Seungjae Shin, and other members of the research team were trained primarily in biology rather than artificial intelligence. By independently learning transformer architectures and deep learning techniques, they designed specialized models and training strategies tailored for behavioral analysis. Professor Kim's laboratory has long pursued AI-driven behavioral analysis and previously developed AVATAR, a technology that reconstructs rodent behavior in virtual environments, leading to the founding of Actnova Inc. "The project began with a simple question: Could animal movements contain a structure similar to language?" said Dr. Seungjae Shin, the first author of the study. The team also adopted a self-supervised learning framework that enables AI to learn directly from behavioral data without manual annotations. Furthermore, a model trained on rat behavior successfully transferred to mouse behavior analysis, demonstrating the feasibility of a behavioral foundation model applicable across species. "BehaVERT goes beyond behavior classification and enables the interpretation of behavioral meaning," said Professor Dae-Soo Kim. "We expect it to become a key research tool for discovering new insights in drug development, psychiatric disorders, behavioral genetics, and many other areas of life sciences." The study was published on March 24, 2026, in the International Journal of Computer Vision (IJCV), one of the world's leading journals in computer vision. Paper Information • Title: BehaVERT: A Transformer-Based Motion Language Model for Decoding Behavioral Semantics in Mice • Journal: International Journal of Computer Vision (IJCV) • DOI: 10.1007/s11263-026-02834-y Related Videos • BehaVERT — Social Behavior Analysis Visualization (Investigation & Mount), https://youtu.be/JshCr-ZBQR0 • BehaVERT — Social Behavior Analysis Visualization (Investigation & Attack), https://youtu.be/p9RPhZM__Js • BehaVERT — AI Discovers Core Social Behavioral Features in an Autism Mouse Model, https://youtu.be/D6zUyDu3t9I Funding This research was supported by the Mid-Career Researcher Program and the Brain Convergence Technology Development Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), Republic of Korea.
2026.07.01 View 1978
AI Enters the Experienced Hire Era... Teaching Learned Knowledge with Ease < (From left) KAIST Professor Hyunwoo J. Kim, Postdoctoral Researcher Sanghyeok Lee, M.S candidate Taehoon Song, Korea University Ph.D candidate Jihwan Park > How inconvenient would it be if you had to manually transfer every contact and photo from scratch every time you switched to a new smartphone? Current Artificial Intelligence (AI) models face a similar predicament. Whenever a superior new AI model—such as a new version of ChatGPT—emerges, it has to be retrained with massive amounts of data and at a high cost to acquire specialized knowledge in specific fields. A Korean research team has developed a "knowledge transplantation" technology between AI models that can resolve this inefficiency. KAIST announced on January 27th that a research team led by Professor Hyunwoo J. Kim from the School of Computing, in collaboration with a research team from Korea University, has developed a new technology capable of effectively "transplanting" learned knowledge between different AI models. Recently, Vision-Language Models (VLM), which understand both images and text simultaneously, have been evolving rapidly. These are easily understood as multimodal AIs, like ChatGPT, which can provide explanations when a user shows them a photo and asks a question. These models have the advantage of adapting relatively quickly to new fields using small amounts of data by pre-learning large-scale image and language data. However, the need to repeat this "adaptation process" from scratch every time a new AI model is released has been pointed out as a major inefficiency. Existing adaptation techniques also faced limitations: they were difficult to use if the model structure changed even slightly, or they significantly increased memory and computational costs because multiple models had to be used simultaneously. To solve these problems, the research team proposed "TransMiter," a transferable adaptation technique that allows learned knowledge to be reused regardless of the model's structure or size. The core of this technology is directly transferring the "adaptation experience" accumulated by one AI as it learns to another AI model. < TransMiter: A transferable adaptation technique reusable regardless of model structure, size, etc. > The researchers' technology does not overhaul the complex internal structure of the AI; instead, it adopts a method of passing on "know-how" learned by observing only the prediction results (output) to another AI. Even if the AI models have different architectures, if the know-how learned by one AI is organized based on the answers given to the same questions, another AI can utilize that knowledge immediately. Consequently, there is no need to undergo the complex and time-consuming retraining process, and there is almost no slowdown in speed. This study is highly significant as it is the first to prove that AI adaptation knowledge—previously considered almost impossible to reuse if model structures or sizes differed—can be precisely transplanted regardless of the model type. This is expected to not only reduce repetitive learning costs but also be utilized as a so-called "knowledge patch" technology that updates Large Language Models (LLMs) in real-time according to specific needs. Professor Hyunwoo J. Kim explained, "By extending this research, we can significantly reduce the cost of post-training that had to be performed repeatedly whenever a rapidly evolving hyper-scale language model appears. It will enable 'model patches' that easily add expertise in specific fields." The study involved Taehoon Song (Master's student, KAIST School of Computing), Sanghyeok Lee (Postdoctoral researcher), and Jihwan Park (Doctoral student, Korea University) as co-authors, with Professor Hyunwoo J. Kim serving as the corresponding author. The research results were accepted for oral presentation (4.6% acceptance rate as of 2025) at AAAI 2026 (Association for the Advancement of Artificial Intelligence), the most prestigious international conference in the field of AI, and were presented on January 25th. Paper Title: Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization DOI: https://doi.org/10.48550/arXiv.2508.08604 Meanwhile, Professor Hyunwoo J. Kim's laboratory presented a total of three papers at the conference, including this paper and "TabFlash," a technology developed in collaboration with Google Cloud AI to enhance the understanding of tables within documents.
2026.01.27 View 4128
“AI,” the New Language of Materials Science and Engineering Spoken at KAIST <(From Left) M.S candidate Chaeyul Kang, Professor Seumgbum Hong, Ph. D candidate Benediktus Madika, Ph.D candidate Batzorig Buyantogtokh, Ph.D candiate Aditi Saha, > Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee). Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization The era has arrived in which artificial intelligence (AI) autonomously imagines and predicts the structures and properties of new materials. Today, AI functions as a researcher’s “second brain,” actively participating in every stage of research, from idea generation to experimental validation. KAIST (President Kwang Hyung Lee) announced on October 26 that a comprehensive review paper analyzing the impact of AI, Machine Learning (ML), and Deep Learning (DL) technologies across materials science and engineering has been published in ACS Nano (Impact Factor = 18.7). The paper was co-authored by Professor Seungbum Hong and his team from the Department of Materials Science and Engineering at KAIST, in collaboration with researchers from Drexel University, Northwestern University, the University of St Andrews, and the University of Tennessee in the United States. The research team proposed a full-cycle utilization strategy for materials innovation through an AI-based catalyst search platform, which embodies the concept of a Self-Driving Lab—a system in which robots autonomously perform materials synthesis and optimization experiments. Professor Hong’s team categorized materials research into three major stages—Discovery, Development, and Optimization—and detailed the distinctive role of AI in each phase: In the Discovery Stage, AI designs new structures, predicts properties, and rapidly identifies the most promising materials among vast candidate pools. In the Development Stage, AI analyzes experimental data and autonomously adjusts experimental processes through Self-Driving Lab systems, significantly shortening research timelines. In the Optimization Stage, AI employs Reinforcement Learning, which identifies optimal conditions through Bayesian Optimization, which efficiently finds superior results with minimal experimentation, to fine-tune designs and process conditions for maximum performance. In essence, AI serves as a “smart assistant” that narrows down the most promising materials, reduces experimental trial and error, and autonomously optimizes experimental conditions to achieve the best-performing outcomes. The paper further highlights how cutting-edge technologies such as Generative AI, Graph Neural Networks (GNNs), and Transformer models are transforming AI from a computational tool into a “thinking researcher.” Nonetheless, the team cautions that AI’s predictions are not error-proof and that key challenges persist, such as imbalanced data quality, limited interpretability of AI predictions, and integration of heterogeneous datasets. To address these limitations, the authors emphasize the importance of developing AI systems capable of autonomously understanding physical principles and ensuring transparent, verifiable decision-making processes for researchers. The review also explores the concept of the Self-Driving Lab, where AI autonomously designs experimental plans, analyzes results, and determines the next experimental steps—without manual operation by researchers. The AI-Based Catalyst Search Platform exemplifies this concept, enabling robots to automatically design, execute, and optimize catalyst synthesis experiments. In particular, the study presents cases in which AI-driven experimentation has dramatically accelerated catalyst development, suggesting that similar approaches could revolutionize research in battery and energy materials. <AI Driving Innovation Across the Entire Cycle of New Material Discovery, Development, and Optimization> “This review demonstrates that artificial intelligence is emerging as the new language of materials science and engineering, transcending its role as a mere tool,” said Professor Seungbum Hong. “The roadmap presented by the KAIST team will serve as a valuable guide for researchers in Korea’s national core industries including batteries, semiconductors, and energy materials.” Benediktus Madika (Ph.D. candidate), Aditi Saha (Ph.D. candidate), Chaeyul Kang (M.S. candidate), and Batzorig Buyantogtokh (Ph.D. candidate) from KAIST’s Department of Materials Science and Engineering contributed as co-first authors. Collaborating authors include Professor Joshua Agar (Drexel University), Professors Chris Wolverton and Peter Voorhees (Northwestern University), Professor Peter Littlewood (University of St Andrews), and Professor Sergei Kalinin (University of Tennessee). Paper Title: Artificial Intelligence for Materials Discovery, Development, and Optimization DOI: 10.1021/acsnano.5c04200 This work was supported by the National Research Foundation of Korea (NRF) with funding from the Ministry of Science and ICT (RS-2023-00247245).
2025.10.27 View 4706
KAIST researcher Se Jin Park develops 'SpeechSSM,' opening up possibilities for a 24-hour AI voice assistant. <(From Left)Prof. Yong Man Ro and Ph.D. candidate Sejin Park> Se Jin Park, a researcher from Professor Yong Man Ro’s team at KAIST, has announced 'SpeechSSM', a spoken language model capable of generating long-duration speech that sounds natural and remains consistent. An efficient processing technique based on linear sequence modeling overcomes the limitations of existing spoken language models, enabling high-quality speech generation without time constraints. It is expected to be widely used in podcasts, audiobooks, and voice assistants due to its ability to generate natural, long-duration speech like humans. Recently, Spoken Language Models (SLMs) have been spotlighted as next-generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information. However, existing models showed significant limitations in generating long-duration content required for podcasts, audiobooks, and voice assistants. Now, KAIST researcher has succeeded in overcoming these limitations by developing 'SpeechSSM,' which enables consistent and natural speech generation without time constraints. KAIST(President Kwang Hyung Lee) announced on the 3rd of July that Ph.D. candidate Sejin Park from Professor Yong Man Ro's research team in the School of Electrical Engineering has developed 'SpeechSSM,' a spoken. a spoken language model capable of generating long-duration speech. This research is set to be presented as an oral paper at ICML (International Conference on Machine Learning) 2025, one of the top machine learning conferences, selected among approximately 1% of all submitted papers. This not only proves outstanding research ability but also serves as an opportunity to once again demonstrate KAIST's world-leading AI research capabilities. A major advantage of Spoken Language Models (SLMs) is their ability to directly process speech without intermediate text conversion, leveraging the unique acoustic characteristics of human speakers, allowing for the rapid generation of high-quality speech even in large-scale models. However, existing models faced difficulties in maintaining semantic and speaker consistency for long-duration speech due to increased 'speech token resolution' and memory consumption when capturing very detailed information by breaking down speech into fine fragments. To solve this problem, Se Jin Park developed 'SpeechSSM,' a spoken language model using a Hybrid State-Space Model, designed to efficiently process and generate long speech sequences. This model employs a 'hybrid structure' that alternately places 'attention layers' focusing on recent information and 'recurrent layers' that remember the overall narrative flow (long-term context). This allows the story to flow smoothly without losing coherence even when generating speech for a long time. Furthermore, memory usage and computational load do not increase sharply with input length, enabling stable and efficient learning and the generation of long-duration speech. SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech. Additionally, in the speech generation phase, it uses a 'Non-Autoregressive' audio synthesis model (SoundStorm), which rapidly generates multiple parts at once instead of slowly creating one character or one word at a time, enabling the fast generation of high-quality speech. While existing models typically evaluated short speech models of about 10 seconds, Se Jin Park created new evaluation tasks for speech generation based on their self-built benchmark dataset, 'LibriSpeech-Long,' capable of generating up to 16 minutes of speech. Compared to PPL (Perplexity), an existing speech model evaluation metric that only indicates grammatical correctness, she proposed new evaluation metrics such as 'SC-L (semantic coherence over time)' to assess content coherence over time, and 'N-MOS-T (naturalness mean opinion score over time)' to evaluate naturalness over time, enabling more effective and precise evaluation. Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation. This contrasts sharply with existing models, which tended to easily lose their topic and exhibit repetition during long-duration generation. PhD candidate Sejin Park explained, "Existing spoken language models had limitations in long-duration generation, so our goal was to develop a spoken language model capable of generating long-duration speech for actual human use." She added, "This research achievement is expected to greatly contribute to various types of voice content creation and voice AI fields like voice assistants, by maintaining consistent content in long contexts and responding more efficiently and quickly in real time than existing methods." This research, with Se Jin Park as the first author, was conducted in collaboration with Google DeepMind and is scheduled to be presented as an oral presentation at ICML (International Conference on Machine Learning) 2025 on July 16th. Paper Title: Long-Form Speech Generation with Spoken Language Models DOI: 10.48550/arXiv.2412.18603 Ph.D. candidate Se Jin Park has demonstrated outstanding research capabilities as a member of Professor Yong Man Ro's MLLM (multimodal large language model) research team, through her work integrating vision, speech, and language. Her achievements include a spotlight paper presentation at 2024 CVPR (Computer Vision and Pattern Recognition) and an Outstanding Paper Award at 2024 ACL (Association for Computational Linguistics). For more information, you can refer to the publication and accompanying demo: SpeechSSM Publications.
2025.07.04 View 6325
“For the First Time, We Shared a Meaningful Exchange”: KAIST Develops an AI App for Parents and Minimally Verbal Autistic Children Connect • KAIST team up with NAVER AI Lab and Dodakim Child Development Center Develop ‘AAcessTalk’, an AI-driven Communication Tool bridging the gap Between Children with Autism and their Parents • The project earned the prestigious Best Paper Award at the ACM CHI 2025, the Premier International Conference in Human-Computer Interaction • Families share heartwarming stories of breakthrough communication and newfound understanding. < Photo 1. (From left) Professor Hwajung Hong and Doctoral candidate Dasom Choi of the Department of Industrial Design with SoHyun Park and Young-Ho Kim of Naver Cloud AI Lab > For many families of minimally verbal autistic (MVA) children, communication often feels like an uphill battle. But now, thanks to a new AI-powered app developed by researchers at KAIST in collaboration with NAVER AI Lab and Dodakim Child Development Center, parents are finally experiencing moments of genuine connection with their children. On the 16th, the KAIST (President Kwang Hyung Lee) research team, led by Professor Hwajung Hong of the Department of Industrial Design, announced the development of ‘AAcessTalk,’ an artificial intelligence (AI)-based communication tool that enables genuine communication between children with autism and their parents. This research was recognized for its human-centered AI approach and received international attention, earning the Best Paper Award at the ACM CHI 2025*, an international conference held in Yokohama, Japan.*ACM CHI (ACM Conference on Human Factors in Computing Systems) 2025: One of the world's most prestigious academic conference in the field of Human-Computer Interaction (HCI). This year, approximately 1,200 papers were selected out of about 5,000 submissions, with the Best Paper Award given to only the top 1%. The conference, which drew over 5,000 researchers, was the largest in its history, reflecting the growing interest in ‘Human-AI Interaction.’ Called AACessTalk, the app offers personalized vocabulary cards tailored to each child’s interests and context, while guiding parents through conversations with customized prompts. This creates a space where children’s voices can finally be heard—and where parents and children can connect on a deeper level. Traditional augmentative and alternative communication (AAC) tools have relied heavily on fixed card systems that often fail to capture the subtle emotions and shifting interests of children with autism. AACessTalk breaks new ground by integrating AI technology that adapts in real time to the child’s mood and environment. < Figure. Schematics of AACessTalk system. It provides personalized vocabulary cards for children with autism and context-based conversation guides for parents to focus on practical communication. Large ‘Turn Pass Button’ is placed at the child’s side to allow the child to lead the conversation. > Among its standout features is a large ‘Turn Pass Button’ that gives children control over when to start or end conversations—allowing them to lead with agency. Another feature, the “What about Mom/Dad?” button, encourages children to ask about their parents’ thoughts, fostering mutual engagement in dialogue, something many children had never done before. One parent shared, “For the first time, we shared a meaningful exchange.” Such stories were common among the 11 families who participated in a two-week pilot study, where children used the app to take more initiative in conversations and parents discovered new layers of their children’s language abilities. Parents also reported moments of surprise and joy when their children used unexpected words or took the lead in conversations, breaking free from repetitive patterns. “I was amazed when my child used a word I hadn’t heard before. It helped me understand them in a whole new way,” recalled one caregiver. Professor Hwajung Hong, who led the research at KAIST’s Department of Industrial Design, emphasized the importance of empowering children to express their own voices. “This study shows that AI can be more than a communication aid—it can be a bridge to genuine connection and understanding within families,” she said. Looking ahead, the team plans to refine and expand human-centered AI technologies that honor neurodiversity, with a focus on bringing practical solutions to socially vulnerable groups and enriching user experiences. This research is the result of KAIST Department of Industrial Design doctoral student Dasom Choi's internship at NAVER AI Lab.* Thesis Title: AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card Recommendation* DOI: 10.1145/3706598.3713792* Main Author Information: Dasom Choi (KAIST, NAVER AI Lab, First Author), SoHyun Park (NAVER AI Lab) , Kyungah Lee (Dodakim Child Development Center), Hwajung Hong (KAIST), and Young-Ho Kim (NAVER AI Lab, Corresponding Author) This research was supported by the NAVER AI Lab internship program and grants from the National Research Foundation of Korea: the Doctoral Student Research Encouragement Grant (NRF-2024S1A5B5A19043580) and the Mid-Career Researcher Support Program for the Development of a Generative AI-Based Augmentative and Alternative Communication System for Autism Spectrum Disorder (RS-2024-00458557).
2025.05.19 View 14768
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 17719
Sungjoon Park Named Google PhD Fellow PhD candidate Sungjoon Park from the School of Computing was named a 2019 Google PhD Fellow in the field of natural language processing. The Google PhD fellowship program has recognized and supported outstanding graduate students in computer science and related fields since 2009. Park is one of three Korean students chosen as the recipients of Google Fellowships this year. A total of 54 students across the world in 12 fields were awarded this fellowship. Park’s research on computational psychotherapy using natural language processing (NLP) powered by machine learning earned him this year’s fellowship. He presented of learning distributed representations in Korean and their interpretations during the 2017 Annual Conference of the Association for Computational Linguistics and the 2018 Conference on Empirical Methods in Natural Language Processing. He also applied machine learning-based natural language processing into computational psychotherapy so that a trained machine learning model could categorize client's verbal responses in a counseling dialogue. This was presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics. More recently, he has been developing on neural response generation model and the prediction and extraction of complex emotion in text, and computational psychotherapy applications.
2019.09.17 View 17248
KAIST Develops IoT Platform for Food Safety A research team led by the KAIST Auto-ID Labs developed a GS1 international standard-based IoTs infrastructure platform dubbed Oliot (Open Language of Internet of Things). This platform will be applied to Wanju Local Food, the nation’s largest cooperative, and will be in operation from April 5. A total of eleven organizations participated in the development of Oliot, with KAIST as the center. This consortium is based on the GS1 international standard-based Oliot platform, which allows collecting and sharing data along the entire process of agrifood from production to processing, distribution, and consumption. It aims at increasing farm incomes and establishing a global ecosystem of domestic agriculture and stockbreeding that provides safe food. Wanju Local Food is now the world’s first local food co-op with a traceability system from the initial stage of production planning to end sales based on GS1 international standards, which will ensure food safety. KAIST has been sharing Oliot data in order to apply it to industries around the world. As of April 2018, approximately 900 enterprises and developers from more than 100 countries have downloaded it. Professor Daeyoung Kim from the School of Computing, who is also Research Director of Auto-ID Labs said, “We are planning to disseminate Oliot to local food cooperatives throughout the nation. We will also cooperate with other countries, like China, Holland, and Hong Kong to create a better ecosystem for the global food industry. “We are currently collaborating with related business to converge Oliot with AI or blockchain technology that can be applied to various services, such as healthcare and smart factories. Its tangible outcome will be revealed soon,” he added. Auto-ID Labs are a global research consortium of six academic institutions that research and develop new technologies for advancing global commerce, partnering with GS1 (Global Standard 1), a non-profit organization that established standards for global commerce such as introducing barcodes to the retail industry. The Auto-ID Labs include MIT, University of Cambridge, Keio University, Fudan University, ETH Zurich/University of St. Gallen, and KAIST. The consortium was supported by the Ministry of Science and ICT as well as the Institute for Information and Communications Technology Promotion for three years from 2015. The launching of Oliot at Wanju Local Food will be held on April 5.
2018.04.03 View 16297
Professor Key-Sun Choi Receives the Order of Service Merit Green Stripes from the Korean Government The award recognizes Professor Choi’s life-long research effort to make Korean language digitally available, both nationally and internationally. Professor Key-Sun Choi of the School of Computing at KAIST received the Order of Service Merit Green Stripes from the Korean government at the 569th Korean Language Day, held annually to commemorate the invention of the Korean language, Hangeul. The ceremony took place on October 9, 2015, at the Sejong Center in Seoul. Professor Choi has distinguished himself in the field of natural language processing (NLP), including Korean language. He developed a Korean NLP parser that enabled information processing and data analysis of Korean language, as well as a digital Korean dictionary, contributing to the advancement of Korean language-based information technology. Professor Choi also led the way to widespread use of Korean natural language in computing by developing and commercializing open source software to process the Korean language. He has served leading roles in many of the international academic societies and standardization organizations, among others, as the vice president of Infoterm (the International Information Center for Terminology), president of the Asia Federation of Natural Language Processing, vice chair of ISO/TC 37, a technical committee in the International Organization for Standardization (ISO), and a council member for the International Association of Machine Translation.
2015.10.08 View 14960
The Website of the KAIST Industrial Design Department Receives a Design Award The 10th QS-Apple Higher Education Conference and Exhibition took place on November 11-13, 2014 in Taipei, Taiwan. The conference was hosted by Quacquarelli Symonds, a British company specializing in education, which publishes annually its world university rankings. Apple stands for Asia Pacific Professional Leaders in Education. The QS-Apple conference supports the internationalization of Asia Pacific universities by providing opportunities for networking, exchanging best practices, and discussing recent developments in higher education. During the conference, the organizers presented the Creative Awards for best international education promotional designs in four categories: Website Pages, Video, Print Advertisement, and International Student Recruitment Brochures. KAIST’s Industrial Design Department received the Best Website Pages Award for their website in recognition of high levels of user convenience and satisfaction as well as English language services. A total of 39 universities in the Asia and Pacific region competed in this category, and Nanyang Technological University in Singapore came in second place, followed by Hong Kong Baptist University in third.
2014.11.13 View 13026
The 2014 CAMPUS Asia Summer Program The CAMPUS Asia (Collective Action for Mobility Program of University Students in Asia) is an academic exchange program originally proposed at the third presidential meeting of three nations, Korea, China, and Japan, which was held in May 2010 on Jeju Island. Under the proposal, three science and technology universities, Tsinghua University (China), KAIST (Korea), and Tokyo Institute of Technology (Japan), created a program in 2012 for academic and research collaboration, TKT CAMPUS Asia. Since then, each university has been hosting an exchange program in rotation. The 2014 summer program of TKT CAMPUS Asia was held from August 4th through 28th at KAIST. A total of 13 professors from the departments of chemical engineering, mechanical engineering, and life science, as well as 25 students participated. Under the summer program, three courses in biotechnology, mechanical engineering, and Korean language were offered. The universities gave credits for all courses. As part of TKT CAMPUS Asia, KAIST has also operated a semester study abroad and foreign exchange program and will begin a joint degree program by 2015. Professor Jung Kim of the Department of Mechanical Engineering at KAIST said, “Just as the Erasmus Program (European Community Action Scheme for the Mobility of University Students) contributed to the foundation of the European Union, we hope that the CAMPUS Asia will serve a similar goal for a greater Asia.”
2014.09.03 View 9073
Successful Development of Excavation System of Biomarkers containing Protein Decomposition Control Enzyme Information A Korean team of researchers successfully developed a biomarker excavation system named E3Net that excavates biomarkers containing information of the enzymes that control the decomposition of proteins. The development of the system paved the possibility of development of new high quality biomarkers. *Biomarker: Molecular information of unique patterns derived from genes and proteins that allow the monitoring of physical changes from genetic or environmental causes. Professor Lee Kwan Soo’s team (Department of Biological Sciences) composed of Doctorate candidate Han Young Woong, Lee Ho Dong Ph.D. and Professor Park Jong Chul published a dissertation in the April edition of Molecular and Cellular Proteomics. (Dissertation Title: A system for exploring E3-mediated regulatory networks of cellular functions). Professor Lee’s team compiled all available information of the enzyme that controls protein decomposition (E3 enzyme) and successfully compiled the inter-substrate network by extracting information from 20,000 biology related data base dissertations. The result was the development of the E3Net system that analyzes the related cell function and disease. Cells have a system that produces, destroys, and recycles proteins in response to the ever changing environmental conditions. Error in these processes leads to disease. Therefore finding the relationship between E3 enzymes that control the decomposition of proteins and the substrates will allow disease curing and prevention to become much easier. E3 enzyme is responsible for 80% of the protein decomposition and is therefore predicted to be related to various diseases. However the information on E3 enzyme and inter-substrate behavior are spread out among numerous dissertations and data bases which prevented methodological analysis of the role of the related cells and characteristics of the disease itself. Professor Lee’s team was successful in creating the E3Net that compiled 2,201 pieces of E3 substrate information, 4,896 pieces of substrate information, and 1,671 pieces of inter-substrate relationship information. This compilation allows for the systematic analysis of cells and diseases. The newly created network is 10 times larger than the existing network and is the first case where it is possible to accurately find the cell function and related diseases. It is anticipated that the use of the E3Net will allow the excavation of new biomarkers for the development of personalized drug systems. The research team applied the E3Net to find tens of new candidate biomarkers related to the major modern diseases like diabetes and cancer.
2012.05.30 View 19952