Highlights
Professor Yong Man Ro has been elevated to the grade of IEEE Fellow for the Class of 2026, with the citation “for contributions to Human-Centered Multimodal Signal Processing.” Recognized for bridging the gap between human perception and machine intelligence, Prof. Ro has established foundational frameworks in multimodal human signal analysis and developed the first human-centered personalized models for quantifying Virtual Reality (VR) quality. His authority in the global signal processing community is further evidenced by his widely cited research and his influential academic standing.
Building on this legacy of human-centric analysis, Prof. Ro is currently spearheading the future of AI through his research on Multimodal Large Language Models (MLLM) and Multimodal AI. His lab focuses on creating AI agents capable of “Inclusive Human Multimodal AI,” a vision recently validated by his achievement, which won the Outstanding Paper Award at ACL 2024, a top-tier AI conference. This research marks a leap toward empathetic Artificial General Intelligence (AGI) that can perceive human signals. Beyond his research, Prof. Ro continues to shape the field as an elected member of the Image, Video, and Multidimensional Signal Processing (IVMSP) Technical Committee of the IEEE Signal Processing Society, a member of the Editorial Board for IEEE Transactions on Image Processing (TIP), and as a mentor to over 100 Ph.D. and M.S. graduates who are now leading innovation across academia and top-tier tech research Institutes.
Dr. Jin Yeong Kim, a graduate of Professor Kyung Cheol Choi’s lab in our department, has been promoted to Executive Director (Vice President–level) in the latest regular personnel announcement at Samsung Display.
Mr. Kim earned his master’s degree in 2011 and his Ph.D. in 2014, after which he joined Samsung Display and has since served as a principal engineer in the Materials Development Team of the Small and Medium-Sized Display Business Division. He has led the development of Tandem structure materials for next-generation IT and automotive products, significantly contributing to the realization of high-reliability and high-efficiency displays and strengthening the company’s core product competitiveness. His outstanding research achievements and leadership have earned him deep trust within the organization, culminating in his promotion as an Executive Director in his 30s.
Currently, Executive Director Kim is spearheading the advancement of high-performance tandem materials and the development of key materials for small and medium-sized displays to prepare for the next wave of IT and automotive display technologies. He is expected to continue playing a pivotal role in enhancing Samsung Display’s global technological competitiveness and driving innovation in the future display industry.
Currently, LLM inference services rely entirely on dedicated accelerators and GPUs in data centers, requiring substantial financial and infrastructure investments for large-scale language model services. While high-performance consumer-grade GPUs—more affordable than data center GPUs—have become widely available at the edge outside data centers, structural limitations of existing LLM inference architectures prevent their efficient utilization in internet environments with limited communication infrastructure.
The research team developed SpecEdge, an edge-assisted inference framework to address these challenges. SpecEdge reduces LLM inference costs by effectively distributing computation between consumer-grade edge GPUs and data center GPUs. The framework also adopts speculative decoding techniques to enable smooth communication between edge GPUs and data center GPUs over the internet. Speculative decoding is a technique where a relatively small language model quickly generates multiple high-probability tokens, which are then verified by a large language model. SpecEdge deploys a small model on edge GPUs to generate high-probability token sequences at once, then sends them to data center GPUs for batch verification.
SpecEdge employs a strategy where edge GPUs continue generating tokens while waiting for verification results from the server. After initial token generation, the edge pre-generates additional tokens along the highest-probability path, allowing immediate utilization of pre-generated tokens when all verification results match. Additionally, server-side pipeline optimization intelligently batches verification requests from multiple edges to maximize server GPU utilization. While one edge GPU drafts tokens, the server verifies other requests, eliminating idle time and enabling processing of more requests.
This research demonstrates the potential to reduce dependence on data center GPUs by leveraging widely deployed edge GPUs. The SpecEdge framework, which can be extended to NPUs at the edge, addresses cost concerns and limited GPU availability in data centers, providing opportunities to deploy high-quality LLM services. This could lower barriers to entry in the AI service market and stimulate competition, laying the foundation for the development of Korea’s AI industry ecosystem.
Professor Dongsu Han stated, “We will continue research to enable the use of user edge devices as LLM infrastructure, beyond edge cloud GPUs,” adding that “utilizing user edge resources will reduce the cost burden on service providers, lower barriers to accessing high-quality LLMs, and serve as the foundation for AI for everyone.”
This research was conducted with Dr. Jinwoo Park and Master’s student Seunggeun Cho from KAIST. The findings will be presented as a Spotlight paper (top 3.2% of submissions) at the Annual Conference on Neural Information Processing Systems (NeurIPS), a top-tier international conference in artificial intelligence, held in San Diego, USA, from December 2–7 (Paper title: SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs).
A paper co-authored by Ph.D. candidates Seokjoon Kwon and Hee-Deok Jang, along with Professor Dong Eui Chang from our school’s Professor Dong Eui Chang’s Lab, has won the Excellence Paper Award at the Korea Artificial Intelligence Association (KAIA) Fall Conference.
The award-winning paper, titled “A Framework for Object Navigation Using Hierarchical Scene Representations,” proposes the HSG-ON (Hierarchical Scene Graph-based Object Navigation) framework to solve the Zero-Shot Object Goal Navigation task which is the challenge of an AI robot finding an object it has never encountered in an unfamiliar environment.
The framework constructs a Hierarchical Scene Graph (HSG) that represents the environment in a ‘room-workspace-object’ hierarchy. Based on HSG, the robot is enabled to strategically search the ‘workspace’ most likely to contain the target object. By narrowing the search space in a human-like manner, HSG-ON demonstrates superior performance in terms of success rate and efficiency compared to conventional methods, offering a powerful and efficient solution for the Zero-Shot Object Goal Navigation task.
Conventional wearable ultrasound sensors have been limited by low power output and poor structural stability, making them unsuitable for high-resolution imaging or therapeutic applications. The EE research team has now overcome these challenges by developing a flexible ultrasound sensor with statically adjustable curvature. This breakthrough opens new possibilities for wearable medical devices that can capture precise, body-conforming images and perform noninvasive treatments using ultrasound energy.
A research team led by Professor Hyunjoo Jenny Lee from the School of Electrical Engineering developed a “flex-to-rigid (FTR)” capacitive micromachined ultrasonic transducer (CMUT) capable of transitioning freely between flexibility and rigidity using a semiconductor wafer process (MEMS).
The team incorporated a low-melting-point alloy (LMPA) inside the device. When an electric current is applied, the metal melts, allowing the structure to deform freely; upon cooling, it solidifies again, fixing the sensor into the desired curved shape.
Conventional polymer-membrane-based CMUTs have suffered from a low elastic modulus, resulting in insufficient acoustic power and blurred focal points during vibration. They have also lacked curvature control, limiting precise focusing on target regions.
Professor Lee’s team designed an FTR structure that combines a rigid silicon substrate with a flexible elastomer bridge, achieving both high output performance and mechanical flexibility. The embedded LMPA enables dynamic adjustment and fixation of the transducer’s shape by toggling between solid and liquid states through electrical control.
As a result, the new sensor can automatically focus ultrasound on a specific region according to its curvature—without requiring separate beamforming electronics—and maintains stable electrical and acoustic performance even after repeated bending.
The device’s acoustic output reaches the level of low-intensity focused ultrasound (LIFU), which can gently stimulate tissues to induce therapeutic effects without causing damage. Experiments on animal models demonstrated that noninvasive spleen stimulation reduced inflammation and improved mobility in arthritis models.
In the future, the team plans to extend this technology to a two-dimensional (2D) array structure—arranging multiple sensors in a grid—to enable simultaneous high-resolution ultrasound imaging and therapeutic applications, paving the way for a new generation of smart medical systems.
Because the technology is compatible with semiconductor fabrication processes, it can be mass-produced and adapted for wearable and home-use ultrasound systems.
This study was conducted by Sang-Mok Lee, Xiaojia Liang (co–first authors), and their collaborators under the supervision of Professor Hyunjoo Jenny Lee. The results were published online on October 23 in npj Flexible Electronics (Impact Factor: 15.5).
- Paper title: “Flexible ultrasound transducer array with statically adjustable curvature for anti-inflammatory treatment”
- DOI https://doi.org/10.1038/s41528-025-00484-7
The research was supported by the Bio & Medical Technology Development Program (Brain Science Convergence Research Program) of the Ministry of Science and ICT (MSIT) and the Korea Medical Device Development Fund, a multi-ministerial R&D initiative.
The poplar (Populus alba) has a unique survival strategy: when exposed to hot and dry conditions, it curls its leaves to expose the ventral surface, reflecting sunlight, and at night, the moisture condensed on the leaf surface releases latent heat to prevent frost damage. Plants have evolved such intricate mechanisms in response to dynamic environmental fluctuations in diurnal and seasonal temperature cycles, light intensity, and humidity, but there have been few instances of realizing such a sophisticated thermal management system with artificial materials. Through this research, the EE research team has developed an artificial material that mimics the thermal management strategy of the poplar leaf, significantly increasing the applicability of power-free, self-regulating thermal management technology in applications such as building facades, roofs, and temporary shelters.
A research team led by Professor Young Min Song of the School of Electrical Engineering, in collaboration with Professor Dae-Hyeong Kim’s team at Seoul National University, has developed a flexible hydrogel-based ‘Latent-Radiative Thermostat (LRT)’ that mimics the natural heat regulation strategy of the poplar leaf.
The LRT developed by the research team is a bio-inspired thermal regulator that autonomously switches between cooling and heating modes. This technology is a new thermal management technique that can simultaneously realize latent heat regulation through the evaporation and condensation of water, and radiative heat regulation using light reflection and transmission, all within a single device.
The primary functional material is a composite that integrates lithium ions (Li+) and hydroxypropyl cellulose (HPC) within a polyacrylamide (PAAm) hydrogel. Li+ maintains warmth by condensing and absorbing moisture to regulate latent heat, and HPC changes between transparent and opaque states according to temperature changes, regulating the reflection and absorption of sunlight to switch between cooling and heating modes.
When the temperature rises, HPC molecules aggregate, causing the hydrogel to become opaque, which reflects sunlight and strengthens the natural cooling effect. The resulting LRT automatically switches among four thermal management modes based on the surrounding temperature, humidity, and sunlight.
►In night/cold environments below the dew point temperature, it maintains warmth by absorbing and condensing moisture in the air and releasing heat. ► On cold days with weak sunlight, it transmits sunlight and the absorbed moisture absorbs near-infrared radiation to produce a heating effect. ► In hot and dry conditions, internal moisture evaporates, resulting in powerful evaporative cooling. ► Under strong sunlight and high-temperature conditions, the HPC becomes opaque to reflect sunlight, and simultaneously, evaporative cooling operates to lower the temperature. That is, it is a bioinspired thermal management device that autonomously switches between cooling and heating modes according to the surrounding environment without requiring power.
Through this research, the LRT has demonstrated the performance to stay cooler in the summer and warmer in the winter. The research team confirmed that the thermal regulation properties can be finely tuned to various climate conditions by adjusting the concentrations of Li+ and HPC, and the durability and mechanical strength of the material were significantly improved by adding TiOs nanoparticles. In outdoor experiments, the LRT maintained temperatures up to 3.7 °C lower in the summer and up to 3.5 °C higher in the winter compared to conventional cooling materials. Furthermore, a simulation covering 7 climate zones (ASHRAE standards) showed an annual energy saving of up to 153 MJ/m2 compared to existing roof coatings. This study is a case of the engineering implementation of the sophisticated thermal management strategies observed in nature. It is anticipated to serve as a next-generation thermal management platform for environments where power-based cooling and heating are difficult, such as building facades, roofs, and temporary shelters.
In a statement, Professor Young Min Song said, “This research is significant as it technically reproduced nature’s intelligent thermal regulation strategy, presenting a thermal management device that self-adapts to seasonal and climate changes. It can be expanded into an intelligent thermal management platform applicable to various environments in the future.” This study was co-first authored by PhD candidate Hyung Rae Kim (School of Electrical Engineering, KAIST). Professor Young Min Song (School of Electrical Engineering, KAIST) participated as a corresponding author. The research was published online on November 4th in Advanced Materials (IF 26.8), a world-leading journal in the field of material science.
※ Paper Title: Hydrogel Thermostat Inspired by Photoprotective Foliage Using Latent and Radiative Heat Control,
DOI:https://doi.org/10.1002/adma.202516537
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2025-16063568, RS-2025-16902996, RS-2023-NR077254, RS-2022-NR068140). This work was supported by the InnoCORE program of the Ministry of Science and ICT(GIST InnoCORE KH0830). This work also was supported by the Technology Innovation Program(or Industrial Strategic Technology Development Program-Bio-industry Technology Development Project)(RS-2024-00467230, Development of a Digital Healthcare Device for Non-invasive Continuous Monitoring of Myocardial Infarction Biomarkers Based on Mid-Infrared Nano-Optical Filters) funded By the Ministry of Trade Industry & Energy(MOTIE, Korea)
In an era when recent cyberattacks on major telecommunications providers have highlighted the fragility of mobile security, researchers at the Korea Advanced Institute of Science and Technology have identified a class of previously unknown vulnerabilities that could allow remote attackers to compromise cellular networks serving billions of users worldwide.
The research team, led by Professor Yongdae Kim of KAIST’s School of Electrical Engineering, discovered that unauthorized attackers could remotely manipulate internal user information in LTE core networks — the central infrastructure that manages authentication, internet connectivity, and data transmission for mobile devices and IoT equipment.
The findings, presented at the 32nd ACM Conference on Computer and Communications Security in Taipei, Taiwan, earned the team a Distinguished Paper Award, one of only 30 such honors selected from approximately 2,400 submissions to one of the field’s most prestigious venues.
A New Class of Vulnerability
The vulnerability class, which the researchers termed “Context Integrity Violation” (CIV), represents a fundamental breach of a basic security principle: unauthenticated messages should not alter internal system states. While previous security research has primarily focused on “downlink” attacks — where networks compromise devices — this study examined the less-scrutinized “uplink” security, where devices can attack core networks.
“The problem stems from gaps in the 3GPP standards,” Professor Kim explained, referring to the international body that establishes operational rules for mobile networks. “While the standards prohibit processing messages that fail authentication, they lack clear guidance on handling messages that bypass authentication procedures entirely.”
The team developed CITesting, the world’s first systematic tool for detecting these vulnerabilities, capable of examining between 2,802 and 4,626 test cases — a vast expansion from the 31 cases covered by the only previous comparable research tool, LTEFuzz.
Widespread Impact Confirmed
Testing four major LTE core network implementations — both open-source and commercial systems — revealed that all contained CIV vulnerabilities. The results showed:
- Open5GS: 2,354 detections, 29 unique vulnerabilities
- srsRAN: 2,604 detections, 22 unique vulnerabilities
- Amarisoft: 672 detections, 16 unique vulnerabilities
- Nokia: 2,523 detections, 59 unique vulnerabilities
The research team demonstrated three critical attack scenarios: denial of service by corrupting network information to block reconnection; IMSI exposure by forcing devices to retransmit user identification numbers in plaintext; and location tracking by capturing signals during reconnection attempts.
Unlike traditional attacks requiring fake base stations or signal interference near victims, these attacks work remotely through legitimate base stations, affecting anyone within the same MME (Mobility Management Entity) coverage area as the attacker — potentially spanning entire metropolitan regions.
Industry Response and Future Implications
Following responsible disclosure protocols, the research team notified affected vendors. Amarisoft deployed patches, and Open5GS integrated the team’s fixes into its official repository. Nokia, however, stated it would not issue patches, asserting compliance with 3GPP standards and declining to comment on whether telecommunications companies currently use the affected equipment.
“Uplink security has been relatively neglected due to testing difficulties, implementation diversity, and regulatory constraints,” Professor Kim noted. “Context integrity violations can pose serious security risks.”
The research team, which included KAIST doctoral students Mincheol Son and Kwangmin Kim as co-first authors, along with Beomseok Oh and Professor CheolJun Park of Kyung Hee University, plans to extend their validation to 5G and private 5G environments. The tools could prove particularly critical for industrial and infrastructure networks, where breaches could have consequences ranging from communication disruption to exposure of sensitive military or corporate data.
The research was supported by the Ministry of Science and ICT through the Institute for Information & Communications Technology Planning & Evaluation, as part of a project developing security technologies for 5G private networks.
With mobile networks forming the backbone of modern digital infrastructure, the discovery underscores the ongoing challenge of securing systems designed in an era when such sophisticated attacks were barely conceivable — and the urgent need for updated standards to address them.
