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Deep Learning Helps Explore the Structural and Strategic Bases of Autism
Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person’s behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the “bible” of mental health diagnosis. However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult. But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability. The technique, or rather suite of techniques, has enjoyed remarkable success in recent years in fields as diverse as voice recognition, translation, autonomous vehicles, and drug discovery. A group of researchers from KAIST in collaboration with the Yonsei University College of Medicine has applied these deep learning techniques to autism diagnosis. Their findings were published on August 14 in the journal IEEE Access. Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal grey and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition. These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy, the number of participants in these studies has been small, often under 50, and diagnostic performance drops markedly when applied to large sample sizes or on datasets that include people from a wide variety of populations and locations. “There was something as to what defines autism that human researchers and clinicians must have been overlooking,” said Keun-Ah Cheon, one of the two corresponding authors and a professor in Department of Child and Adolescent Psychiatry at Severance Hospital of the Yonsei University College of Medicine. “And humans poring over thousands of MRI scans won’t be able to pick up on what we’ve been missing,” she continued. “But we thought AI might be able to.” So the team applied five different categories of deep learning models to an open-source dataset of more than 1,000 MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) initiative, which has collected brain imaging data from laboratories around the world, and to a smaller, but higher-resolution MRI image dataset (84 images) taken from the Child Psychiatric Clinic at Severance Hospital, Yonsei University College of Medicine. In both cases, the researchers used both structural MRIs (examining the anatomy of the brain) and functional MRIs (examining brain activity in different regions). The models allowed the team to explore the structural bases of ASD brain region by brain region, focusing in particular on many structures below the cerebral cortex, including the basal ganglia, which are involved in motor function (movement) as well as learning and memory. Crucially, these specific types of deep learning models also offered up possible explanations of how the AI had come up with its rationale for these findings. “Understanding the way that the AI has classified these brain structures and dynamics is extremely important,” said Sang Wan Lee, the other corresponding author and an associate professor at KAIST. “It’s no good if a doctor can tell a patient that the computer says they have autism, but not be able to say why the computer knows that.” The deep learning models were also able to describe how much a particular aspect contributed to ASD, an analysis tool that can assist psychiatric physicians during the diagnosis process to identify the severity of the autism. “Doctors should be able to use this to offer a personalized diagnosis for patients, including a prognosis of how the condition could develop,” Lee said. “Artificial intelligence is not going to put psychiatrists out of a job,” he explained. “But using AI as a tool should enable doctors to better understand and diagnose complex disorders than they could do on their own.” -Profile Professor Sang Wan Lee Department of Bio and Brain Engineering Laboratory for Brain and Machine Intelligence https://aibrain.kaist.ac.kr/ KAIST
Sulfur-Containing Polymer Generates High Refractive Index and Transparency
Transparent polymer thin film with refractive index exceeding 1.9 to serve as new platform materials for high-end optical device applications Researchers reported a novel technology enhancing the high transparency of refractive polymer film via a one-step vapor deposition process. The sulfur-containing polymer (SCP) film produced by Professor Sung Gap Im’s research team at KAIST’s Department of Chemical and Biomolecular Engineering has exhibited excellent environmental stability and chemical resistance, which is highly desirable for its application in long-term optical device applications. The high refractive index exceeding 1.9 while being fully transparent in the entire visible range will help expand the applications of optoelectronic devices. The refractive index is a ratio of the speed of light in a vacuum to the phase velocity of light in a material, used as a measure of how much the path of light is bent when passing through a material. With the miniaturization of various optical parts used in mobile devices and imaging, demand has been rapidly growing for high refractive index transparent materials that induce more light refraction with a thin film. As polymers have outstanding physical properties and can be easily processed in various forms, they are widely used in a variety of applications such as plastic eyeglass lenses. However, there have been very few polymers developed so far with a refractive index exceeding 1.75, and existing high refractive index polymers require costly materials and complicated manufacturing processes. Above all, core technologies for producing such materials have been dominated by Japanese companies, causing long-standing challenges for Korean manufacturers. Securing a stable supply of high-performance, high refractive index materials is crucial for the production of optical devices that are lighter, more affordable, and can be freely manipulated. The research team successfully manufactured a whole new polymer thin film material with a refractive index exceeding 1.9 and excellent transparency, using just a one-step chemical reaction. The SCP film showed outstanding optical transparency across the entire visible light region, presumably due to the uniformly dispersed, short-segment polysulfide chains, which is a distinct feature unachievable in polymerizations with molten sulfur. The team focused on the fact that elemental sulfur is easily sublimated to produce a high refractive index polymer by polymerizing the vaporized sulfur with a variety of substances. This method suppresses the formation of overly long S-S chains while achieving outstanding thermal stability in high sulfur concentrations and generating transparent non-crystalline polymers across the entire visible spectrum. Due to the characteristics of the vapor phase process, the high refractive index thin film can be coated not just on silicon wafers or glass substrates, but on a wide range of textured surfaces as well. We believe this thin film polymer is the first to have achieved an ultrahigh refractive index exceeding 1.9. Professor Im said, “This high-performance polymer film can be created in a simple one-step manner, which is highly advantageous in the synthesis of SCPs with a high refractive index. This will serve as a platform material for future high-end optical device applications.” This study, in collaboration with research teams from Seoul National University and Kyung Hee University, was reported in Science Advances. (Title: One-Step Vapor-Phase Synthesis of Transparent High-Refractive Index Sulfur-Containing Polymers） This research was supported by the Ministry of Science and ICT’s Global Frontier Project (Center for Advanced Soft-Electronics), Leading Research Center Support Program (Wearable Platform Materials Technology Center), and Basic Science Research Program (Advanced Research Project).
KAIST Receives $57 Million Donation to Enhance Research
The largest amount since the opening of KAIST will fund ‘Singularity Professors’ KAIST Development Foundation Chairman Soo-Young Lee made a gift of real estate estimated at approximately $57 million on July 23. This is the largest donation KAIST has received since it was founded in 1971. The fund will establish the “Soo-Young Lee Science Education Foundation” and the proceeds of the foundation will go to the “Singularity Professors” as necessary resources to help make discoveries and design new approaches to accelerate breakthroughs. “KAIST should be the institute that will produce first Korean Nobel laureate in the field of science. I hope this fund will be utilized to enable Korea to stand out in this challenging time by accomplishing breakthroughs nobody has never imagined,” said Chairman Lee during the donation ceremony at KAIST’s campus in Daejeon. This is Chairman Lee’s third donation following the $6.7 million donation in 2012 and the $830,000 donation in 2016. Chairman Lee began her career as a journalist in 1963. In 1981, she started her own business by launching Kwangwon Ranch and became a successful businesswoman. In 1988, Chairman Lee established the real estate company Kwangwon Industries. After receiving an honorary doctorate from KAIST in 2012, she has served as the chairman of the KAIST Development Foundation from 2013. Chairman Lee expressed her intention to make another donation to KAIST in the near future during the news conference. “People matter most for advancing the world. KAIST has a very distinct mission to foster the brightest minds and will drive the nation to move forward. I have worked with KAIST for quite long time so that I have a strong belief that KAIST is the one that will not only make contributions to Korea but also to all humanity,” she explained. “For example, about one-fourth of the R&D manpower at Samsung Electronics is from KAIST. In 2019, Samsung Electronics recorded a revenue of approximately $206 billion which accounted for about 16% of national GDP. KAIST is the one that fosters global talents who are working at global company such as Samsung and many others.” KAIST President Sung-Chul Shin also expressed his deep respect for Chairman Lee’s decision, saying that the entire KAIST community will make every effort to keep up Chairman Lee’s noble idea encouraging KAIST to push forward and help realize KAIST’s role and mission. (END)
COVID-19 Update: Fall Semester to Continue Offering Classes Online
KAIST announced that the university would continue online classes through the fall semester. However, the university will conduct additional in-person classes for upper-level undergraduate lab classes and some graduate courses where on-site interaction was deemed to be highly necessary. Some 600-level graduate courses at the Daejeon campus and graduate courses at the Seoul campus will carry out both in-person and online classes. The fall semester will start from August 31. Provost and Executive Vice President Kwang Hyung Lee announced the fall semester plan in his letter to the entire student body on July 9. He said that the university decided to continue with online classes in consideration of the safety of KAIST community members and the current status of the COVID-19 spread. However, he said the new plan will help students choose class options between in-person and online classes. “Although the number of classes with two versions is limited, we believe this will help many students continue learning without the sustained face-to-face contact that is inherent in residential education,” Provost Lee said. In-person classes conducted in the fall semester will also be provided online for students who are not available for in-person classes. Students may choose the type of the classes they prefer according to their situation, among only the courses that will offer two versions. Professors will decide if they will conduct two versions of their classes. The Office of Academic Affairs is collecting the professors’ applications for conducting both versions until July 24. KAIST offered real-time online classes and pre-recorded KLMS (KAIST Learning Management System) classes during the spring semester with a very limited number of in-person lab classes for graduate courses and these two versions of online class will continue for fall semester. Provost Lee asked the students who will take the in-person classes to strictly observe all precaution measures as the university will do its best to abide by the government guidelines against the Covid-19 in preparation for the fall semester. “We will continue to make appropriate and safe accommodations for them,” said Provost Lee. Those who need to reside in on-campus dormitories are required to be approved for moving. The applications will open after all the in-person class schedules are fixed next month. However, students who were approved for staying in the dormitories last semester can move in without additional approval procedures for the fall semester. (END)
Professor J.H. Lee Wins the Innovators in Science Award
Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering won the Early-Career Scientist Award of the 2020 Innovators in Science Award. The New York Academy of Sciences administers the award in partnership with Takeda Pharmaceutical Company. The Innovators in Science Award grants two prizes of US $200,000 each year: one to an Early-Career Scientist and the other to a well-established Senior Scientist who have distinguished themselves for the creative thinking and impact of their rare disease research. The Senior Scientist Awardee is Dr. Adrian R. Krainer, at Cold Spring Harbor Laboratory whose research focused on the mechanisms and control of RNA splicing. Prof. Lee is recognized for his research investigating genetic mutations in stem cells in the brain that result in rare developmental brain disorders. He was the first to identify the causes of intractable epilepsies and has identified the genes responsible for several developmental brain disorders, including focal cortical dysplasia, Joubert syndrome—a disorder characterized by an underdevelopment of the brainstem—and hemimegaloencephaly, which is the abnormal enlargement of one side of the brain. “It is a great honor to be recognized by a jury of such globally respected scientists whom I greatly admire,” said Prof. Lee. “More importantly, this award validates research into brain somatic mutations as an important area of exploration to help patients suffering from devastating and untreatable neurological disorders.” Prof. Lee also is the Director of the National Creative Research Initiative Center for Brain Somatic Mutations, and Co-founder and Chief Technology Officer of SoVarGen, a biopharmaceutical company aiming to discover novel therapeutics and diagnosis for intractable central nervous system (CNS) diseases caused by low-level somatic mutation. The Innovators in Science Award is a limited submission competition in which research universities, academic institutions, government or non-profit institutions, or equivalent from around the globe with a well-established record of scientific excellence are invited to nominate their most promising Early-Career Scientists and their most outstanding Senior Scientists working in one of four selected therapeutic fields of neuroscience, gastroenterology, oncology, and regenerative medicine. The 2020 Winners will be honored at the virtual Innovators in Science Award Ceremony and Symposium in October 2020.
Professor Alice Haeyun Oh to Join GPAI Expert Group
Professor Alice Haeyun Oh will participate in the Global Partnership on Artificial Intelligence (GPAI), an international and multi-stakeholder initiative hosted by the OECD to guide the responsible development and use of AI. In collaboration with partners and international organizations, GPAI will bring together leading experts from industry, civil society, government, and academia. The Korean Ministry of Science and ICT (MSIT) officially announced that South Korea will take part in GPAI as one of the 15 founding members that include Canada, France, Japan, and the United States. Professor Oh has been appointed as a new member of the Responsible AI Committee, one of the four committees that GPAI established along with the Data Governance Committee, Future of Work Committee, and Innovation and Commercialization Committee. (END)
Research on the Million Follower Fallacy Receives the Test of Time Award
Professor Meeyoung Cha’s research investigating the correlation between the number of followers on social media and its influence was re-highlighted after 10 years of publication of the paper. Saying that her research is still as relevant today as the day it was published 10 years ago, the Association for the Advancement of Artificial Intelligence (AAAI) presented Professor Cha from the School of Computing with the Test of Time Award during the 14th International Conference on Web and Social Media (ICWSM) held online June 8 through 11. In her 2010 paper titled ‘Measuring User Influence in Twitter: The Million Follower Fallacy,’ Professor Cha proved that number of followers does not match the influential power. She investigated the data including 54,981,152 user accounts, 1,963,263,821 social links, and 1,755,925,520 Tweets, collected with 50 servers. The research compares and illustrates the limitations of various methods used to measure the influence a user has on a social networking platform. These results provided new insights and interpretations to the influencer selection algorithm used to maximize the advertizing impact on big social networking platforms. The research also looked at how long an influential user was active for, and whether the user could freely cross the borders between fields and be influential on different topics as well. By analyzing cases of who becomes an influencer when new events occur, it was shown that a person could quickly become an influencer using several key tactics, unlike what was previously claimed by the ‘accidental influential theory’. Professor Cha explained, “At the time, data from social networking platforms did not receive much attention in computer science, but I remember those all-nighters I pulled to work on this project, fascinated by the fact that internet data could be used to solve difficult social science problems. I feel so grateful that my research has been endeared for such a long time.” Professor Cha received both her undergraduate and graduate degrees from KAIST, and conducted this research during her postdoctoral course at the Max Planck Institute in Germany. She now also serves as a chief investigator of a data science group at the Institute for Basic Science (IBS). (END)
Unravelling Complex Brain Networks with Automated 3-D Neural Mapping
-Automated 3-D brain imaging data analysis technology offers more reliable and standardized analysis of the spatial organization of complex neural circuits.- KAIST researchers developed a new algorithm for brain imaging data analysis that enables the precise and quantitative mapping of complex neural circuits onto a standardized 3-D reference atlas. Brain imaging data analysis is indispensable in the studies of neuroscience. However, analysis of obtained brain imaging data has been heavily dependent on manual processing, which cannot guarantee the accuracy, consistency, and reliability of the results. Conventional brain imaging data analysis typically begins with finding a 2-D brain atlas image that is visually similar to the experimentally obtained brain image. Then, the region-of-interest (ROI) of the atlas image is matched manually with the obtained image, and the number of labeled neurons in the ROI is counted. Such a visual matching process between experimentally obtained brain images and 2-D brain atlas images has been one of the major sources of error in brain imaging data analysis, as the process is highly subjective, sample-specific, and susceptible to human error. Manual analysis processes for brain images are also laborious, and thus studying the complete 3-D neuronal organization on a whole-brain scale is a formidable task. To address these issues, a KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering developed new brain imaging data analysis software named 'AMaSiNe (Automated 3-D Mapping of Single Neurons)', and introduced the algorithm in the May 26 issue of Cell Reports. AMaSiNe automatically detects the positions of single neurons from multiple brain images, and accurately maps all the data onto a common standard 3-D reference space. The algorithm allows the direct comparison of brain data from different animals by automatically matching similar features from the images, and computing the image similarity score. This feature-based quantitative image-to-image comparison technology improves the accuracy, consistency, and reliability of analysis results using only a small number of brain slice image samples, and helps standardize brain imaging data analyses. Unlike other existing brain imaging data analysis methods, AMaSiNe can also automatically find the alignment conditions from misaligned and distorted brain images, and draw an accurate ROI, without any cumbersome manual validation process. AMaSiNe has been further proved to produce consistent results with brain slice images stained utilizing various methods including DAPI, Nissl, and autofluorescence. The two co-lead authors of this study, Jun Ho Song and Woochul Choi, exploited these benefits of AMaSiNe to investigate the topographic organization of neurons that project to the primary visual area (VISp) in various ROIs, such as the dorsal lateral geniculate nucleus (LGd), which could hardly be addressed without proper calibration and standardization of the brain slice image samples. In collaboration with Professor Seung-Hee Lee's group of the Department of Biological Science, the researchers successfully observed the 3-D topographic neural projections to the VISp from LGd, and also demonstrated that these projections could not be observed when the slicing angle was not properly corrected by AMaSiNe. The results suggest that the precise correction of a slicing angle is essential for the investigation of complex and important brain structures. AMaSiNe is widely applicable in the studies of various brain regions and other experimental conditions. For example, in the research team’s previous study jointly conducted with Professor Yang Dan’s group at UC Berkeley, the algorithm enabled the accurate analysis of the neuronal subsets in the substantia nigra and their projections to the whole brain. Their findings were published in Science on January 24. AMaSiNe is of great interest to many neuroscientists in Korea and abroad, and is being actively used by a number of other research groups at KAIST, MIT, Harvard, Caltech, and UC San Diego. Professor Paik said, “Our new algorithm allows the spatial organization of complex neural circuits to be found in a standardized 3-D reference atlas on a whole-brain scale. This will bring brain imaging data analysis to a new level.” He continued, “More in-depth insights for understanding the function of brain circuits can be achieved by facilitating more reliable and standardized analysis of the spatial organization of neural circuits in various regions of the brain.” This work was supported by KAIST and the National Research Foundation of Korea (NRF). Figure and Image Credit: Professor Se-Bum Paik, KAIST Figure and Image Usage Restrictions: News organizations may use or redistribute these figures and images, with proper attribution, as part of news coverage of this paper only. Publication: Song, J. H., et al. (2020). Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex. Cell Reports. Volume 31, 107682. Available online at https://doi.org/10.1016/j.celrep.2020.107682 Profile: Se-Bum Paik Assistant Professor firstname.lastname@example.org http://vs.kaist.ac.kr/ VSNN Laboratory Department of Bio and Brain Engineering Program of Brain and Cognitive Engineering http://kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea (END)
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster. The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics. Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain. In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community. During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence. Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women. (END)
Visualization of Functional Components to Characterize Optimal Composite Electrodes
Researchers have developed a visualization method that will determine the distribution of components in battery electrodes using atomic force microscopy. The method provides insights into the optimal conditions of composite electrodes and takes us one step closer to being able to manufacture next-generation all-solid-state batteries. Lithium-ion batteries are widely used in smart devices and vehicles. However, their flammability makes them a safety concern, arising from potential leakage of liquid electrolytes. All-solid-state lithium ion batteries have emerged as an alternative because of their better safety and wider electrochemical stability. Despite their advantages, all-solid-state lithium ion batteries still have drawbacks such as limited ion conductivity, insufficient contact areas, and high interfacial resistance between the electrode and solid electrolyte. To solve these issues, studies have been conducted on composite electrodes in which lithium ion conducting additives are dispersed as a medium to provide ion conductive paths at the interface and increase the overall ionic conductivity. It is very important to identify the shape and distribution of the components used in active materials, ion conductors, binders, and conductive additives on a microscopic scale for significantly improving the battery operation performance. The developed method is able to distinguish regions of each component based on detected signal sensitivity, by using various modes of atomic force microscopy on a multiscale basis, including electrochemical strain microscopy and lateral force microscopy. For this research project, both conventional electrodes and composite electrodes were tested, and the results were compared. Individual regions were distinguished and nanoscale correlation between ion reactivity distribution and friction force distribution within a single region was determined to examine the effect of the distribution of binder on the electrochemical strain. The research team explored the electrochemical strain microscopy amplitude/phase and lateral force microscopy friction force dependence on the AC drive voltage and the tip loading force, and used their sensitivities as markers for each component in the composite anode. This method allows for direct multiscale observation of the composite electrode in ambient condition, distinguishing various components and measuring their properties simultaneously. Lead author Dr. Hongjun Kim said, “It is easy to prepare the test sample for observation while providing much higher spatial resolution and intensity resolution for detected signals.” He added, “The method also has the advantage of providing 3D surface morphology information for the observed specimens.” Professor Seungbum Hong from the Department of Material Sciences and Engineering said, “This analytical technique using atomic force microscopy will be useful for quantitatively understanding what role each component of a composite material plays in the final properties.” “Our method not only will suggest the new direction for next-generation all-solid-state battery design on a multiscale basis but also lay the groundwork for innovation in the manufacturing process of other electrochemical materials.” This study is published in ACS Applied Energy Materials and supported by the Big Science Research and Development Project under the Ministry of Science and ICT and the National Research Foundation of Korea, the Basic Research Project under the Wearable Platform Materials Technology Center, and KAIST Global Singularity Research Program for 2019 and 2020. Publication:Kim, H, et al. (2020) ‘Visualization of Functional Components in a Lithium Silicon Titanium Phosphate-Natural Graphite Composite Anode’. ACS Applied Energy Materials, Volume 3, Issue 4, pp. 3253-3261. Available online at https://doi.org/10.1021/acsaem.9b02045 Profile: Seungbum Hong Professor email@example.com http://mii.kaist.ac.kr/ Materials Imaging and Integration Laboratory Department of Material Sciences and Engineering KAIST
The 10th KINC Fusion Research Awardees
The KAIST Institute for NanoCentury (KINC) recognized three distinguished researchers whose convergence studies made significant impacts. The KINC presented the 10th KINC Fusion Research Awards during a ceremony that took place at KAIST’s main campus in Daejeon on May 19. This year’s ‘best’ convergence research award went to a joint research group led by Professor Hee Tak Kim from the Department of Chemical and Biomolecular Engineering and Professor Sang Ouk Kim from the Department of Materials Science and Engineering. Their research, featured in the December 27 issue of Advanced Materials as a front cover article last year, introduced the world’s first high-energy efficiency, membraneless, flowless, zinc-bromine battery. This study, in which research professor Gyoung Hwa Jeong, postdoctoral researcher Yearin Byun, and PhD candidate Ju-Hyuck Lee took part as co-lead authors, is deemed as an example of a best practice in convergence research in which two groups’ respective expertise in the fields of carbon materials and electrochemical analysis created a synergistic effect. Professor Bumjoon Kim from the Department of Chemical and Biomolecular Engineering was also recognized for having published the most interdisciplinary research papers on polymer electronics and nanomaterials at home and abroad. Professor Hee-Tae Jung, the Director of KINC and the host of the KINC Fusion Research Awards, said, “The KINC is happy to announce the 10th awardees in nano-fusion research this year. Since convergence is crucial for making revolutionary changes, the importance of convergence studies should be recognized. Our institute will spare no effort to create a research environment suitable for convergence studies, which will be crucial for making a significant difference.” The KINC was established in June 2006 under the KAIST Institute with the mission of facilitating convergence studies by tearing down boarders among departments and carrying out interdisciplinary joint research. Currently, the institute is comprised of approximately 90 professors from 13 departments. It aims to become a hub of university institutes for nano-fusion research. (END)
Hubo Debuts as a News Anchor
HUBO, a humanoid robot developed by Professor Jun-Ho Oh’s team, made its debut as a co-anchor during the TJB prime time news 8 on May 14. “Un-contact" became the new normal after Covid-19 and many business solutions are being transformed using robotics. HUBO made two news reports on contactless services using robots in medical, manufacturing, and logistics industries. HUBO 2, the second generation of HUBO, appeared as a special anchor on the local broadcasting network’s special program in celebration of its 25th anniversary. HUBO is the champion of the 2015 DARPA Robotics Challenge held in the USA. Its FX-2 riding robot also participated in the Olympic torch relay during the 2018 PyeongChang Winter Olympics. Click here to watch a full video of HUBO anchoring the news. (END)
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