Highlights
Seonjeong Lee, a Ph.D. student in Professor Seunghyup Yoo’s research group in our department, received the Young Researcher Award at the 2026 E-MRS Spring Meeting (European Materials Research Society), held in Strasbourg, France, from May 25 to 29, 2026.
Organized by the European Materials Research Society, the E-MRS Spring Meeting is one of the largest materials science conferences in Europe, bringing together leading scholars and researchers from around the world to share the latest achievements in materials science and technology.
Among its honors, the Young Researcher Award is a highly prestigious distinction presented through a rigorous review process to early-career researchers who have demonstrated original and outstanding research achievements and strong potential to lead the future of materials science.
At the conference, Seonjeong Lee presented a miniaturized high-resolution pressure sensor array that maintains high capacitance density even upon device miniaturization by utilizing the principle of electric double layers.
The research was presented as an oral presentation titled “Miniaturized Flexible Ion-Gel Pressure Sensors Based on Gradual Electric Double Layer Formation” and attracted significant attention from the academic community.
A paper proposing an aircraft autonomous piloting framework based on the humanoid robot pilot ‘PIBOT,’ developed by a research team led by Professor David Hyunchul Shim of the School of Electrical Engineering, was selected as the winner of the Best Paper Award among the papers published in the IEEE Robotics & Automation Magazine (IEEE RAM) in 2025.
The research team has successfully completed Phase 1 of the research since the project launched in 2021, and since 2024, they have been developing Phase 2 of the pilot robot, which features a human-like physique and joint structure suitable for actual aircraft piloting. In addition, they are pursuing collaborative research with relevant organizations to expand and apply this technology to various mobile vehicle piloting fields, such as ground vehicles and ships, as well as aircraft.
- Paper Title: “Toward Fully Autonomous Aviation: PIBOT, a Humanoid Robot Pilot for Human-Centric Aircraft Cockpits”,
- Paper Links: https://doi.org/10.1109/MRA.2024.3505774, https://ieeexplore.ieee.org/document/10798973/
The research project titled ‘Development of a Versatile/Secure/Intelligent Integrated Circuit (Future-IC) Platform for Future On-Device Physical AI System Innovation’ by Professor Minkyu Je’s lab has been selected for the 2026 Basic Research Program — Leader Research (Type A), organized by the Ministry of Science and ICT. The Leader Research program selects world-class researchers and provides intensive, long-term support over 9 years, representing one of Korea’s flagship basic research programs. Type A offers an annual research budget of approximately 800 million KRW.
With the arrival of the ‘Physical AI’ era — where AI expands into the physical world — new technological paradigms are being demanded in the field of on-device system semiconductors that power these systems. However, existing research has remained at the level of individually optimizing sensor interfaces, wireless communications, power management, and AI computing circuits, failing to overcome the fundamental barriers of fragmented market structures, security vulnerabilities, and scalability limitations.
To overcome these limitations, the research team aims to secure the world’s first original technology for VISIOn-IC (Versatile, Intelligent, and Secure On-Device IC), which integrates sensor/actuator interfaces, wireless communications, power management, and on-device AI computing circuits onto a single chip platform. Going beyond simple functional integration, the team pursues the realization of ‘AI-based autonomously operating system’ or ‘AI-defined system’ — in which an AI controller inside the chip independently recognizes and optimizes circuits — with Versatility, Security, and Autonomous Intelligence as its core values.
The VISIOn-IC platform technology developed through this research is expected not only to establish itself as a core enabling technology underpinning a wide range of Physical AI applications — including smart factories, healthcare, and wearables — but also make a significant contribution to the advancement of Korea’s system semiconductor industry.
Clear imaging deep inside the living brain has traditionally required advanced optical systems and precise correction techniques. A research team from our department has developed a physics-based AI computational algorithm that can restore blurred biological microscopy images more clearly without additional wavefront measurement hardware.
Professor Iksung Kang (School of Electrical Engineering), in collaboration with Professor Na Ji’s research team at UC Berkeley, has developed a technology that accurately corrects image aberrations in microscopes used for live biological imaging. The experimental design and algorithm development – the core components of this work – were led by Professor Kang during his postdoctoral fellowship in Professor Na Ji’s group. This method uses neural fields, a neural network-based approach that represents 3D spatial structures continuously to reconstruct clearer images and volumetric information.
The research team utilized two-photon fluorescence microscopy, a key technique for observing deep inside living biological tissue. This method generates fluorescence when two photons are absorbed nearly simultaneously, enabling localized imaging within biological samples. However, as light passes through thick tissue, differences in refractive index distort the optical wavefront, causing the image to become blurred, much like how objects appear distorted underwater. This phenomenon is known as optical aberration, in which wavefront distortions degrade the focus and clarity of an image.
Previously, correcting these distortions required additional complex and costly hardware, such as wavefront sensors, which measure how much the optical wavefront is distorted.
In contrast, the research team developed an algorithm that inversely calculates how light was distorted using only the acquired image data and corrects the distortion computationally. In other words, rather than simply sharpening a blurred image, the method incorporates the physical process of image formation to restore clearer images without additional wavefront measurement hardware.
The core of this technology is a machine learning algorithm based on neural fields. This algorithm models the distortion process that occurs as light propagates through biological tissue and the microscope system, enabling an integrated framework that simultaneously corrects optical aberrations caused by biological tissue, subtle motion of the living specimen, and mechanical alignment errors in the microscope.
As a result, the team demonstrated that clearer, higher-contrast images can be obtained from deep biological tissues without separate optical wavefront measurement or correction devices.
This research is particularly significant because it moves beyond the conventional approach that better imaging often requires more complex and expensive hardware. Instead, it shows that software-based computational algorithms can improve microscopy image quality. This approach is expected to help reduce the burden of research equipment and experimental procedures, and to support more precise biological imaging for a broader range of researchers.
Professor Iksung Kang stated, “This research shows the potential of combining optics and artificial intelligence to more accurately observe the inside of living biological systems. Moving forward, we plan to develop this into an intelligent optical imaging system where the microscope can identify the optimal imaging conditions.”
This study was published on April 13th in Nature Methods, a leading methodology journal in the life sciences.
※ Paper Title: Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields
※ Authors: Iksung Kang (KAIST, Co-corresponding & First Author), Hyeonggeon Kim, Ryan Natan, Qinrong Zhang, Stella X. Yu, & Na Ji (UC Berkeley, Co-corresponding Author)
What if an AI, when asked about a minister appointed last month, returned the name of a predecessor from a year ago? This example illustrates a critical limitation of current AI systems: their inability to reliably reflect up-to-date information. Our university’s research team has developed a new evaluation framework that automatically incorporates changes in real-world information and detects “temporal errors” that can appear plausible on the surface. The study is expected to enhance AI reliability by providing a systematic benchmarking framework.
A research team led by Professor Steven Euijong Whang from the School of Electrical Engineering, in joint research with Microsoft Research, has developed a system that automatically evaluates and diagnoses the temporal reasoning capabilities of Large Language Models (LLMs) using temporal database technology.
For AI to earn users’ trust, it must be able to accurately understand real-world information that changes over time. However, existing evaluation methods have largely focused on whether answers are simply right or wrong, or have examined only a narrow set of temporal relations, making them insufficient for evaluating the wide range of question scenarios that arise in real-world environments.
To overcome this challenge, the research team integrated “Temporal Database” design theory—an approach refined and validated over the past 40 years—into AI evaluation for the first time. By leveraging the temporal dependencies and relational structure of data, the technology can automatically generate 13 types of complex time-sensitive questions directly from the database, eliminating the need for researchers to manually create evaluation questions.
In particular, this technology marks a major innovation by replacing the conventional approach of manually writing evaluation questions with a data-driven method that generates them automatically. By automating the entire process—from question generation to answer derivation and verification—based on the database, it also reduces maintenance burden by eliminating the need to manually revise evaluation items.
When real-world information changes, the evaluation questions, answers, and verification criteria are automatically updated simply by revising the relevant data in the database. Although the latest information must still be provided by external data sources or administrators, the framework is designed to automatically conduct the evaluation once the data has been updated.
Additionally, going beyond traditional methods that assess only whether a final answer is correct or incorrect, the research team introduced a new metric that evaluates the factual validity of the dates or time periods used during the answering process. Using this metric, the team achieved a 21.7% improvement in detecting “Temporal Hallucinations”—cases in which an answer appears correct on the surface but is based on faulty temporal reasoning—compared with previous methods.
The database-based approach also improved evaluation efficiency. By eliminating the reliance on unnecessary data, the research team reduced the amount of input data required by an average of 51% compared with previous methods and demonstrated its effectiveness in reducing evaluation maintenance costs.
Professor Steven Euijong Whang stated, “This research shows that classical database design theory can play a crucial role in addressing the reliability challenges of today’s AI systems. By transforming large amounts of domain-specific data into evaluation resources, we expect this work to provide a practical foundation for verifying AI performance in various fields such as medicine and law.”
Soyeon Kim, a PhD student at KAIST, participated as the lead author of this study, and Jindong Wang (Microsoft Research, currently at William & Mary) and Xing Xie (Microsoft Research) participated as co-authors. The research results will be presented this April at ICLR 2026, the most prestigious academic conference in the field of artificial intelligence.
※ Paper Title: Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models
※ Paper Link: https://arxiv.org/abs/2508.02045
Meanwhile, this research was conducted with support from Microsoft Research, the National Research Foundation of Korea, and the Institute for Information & Communications Technology Planning & Evaluation (IITP) Global AI Frontier Lab projects (RS-2024-00469482, RS-2024-00509258).
