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Development of an Ultra-Stretchable Anti-Freezing hydrogel electrolyte based on liquid metal
The research group led by Prof. Sungjune Park from the Department of Chemical Engineering has developed an ultra-stretchable, anti-freezing hydrogel electrolyte using liquid metal particles. The material can stretch up to nine times its original length while maintaining stable electrochemical performance, even at ?20 °C. This work provides a promising platform for energy storage devices that must operate reliably under extreme environmental conditions. With the rapid growth of wearable electronics, there is increasing demand for energy storage systems that combine mechanical flexibility with environmental stability. However, conventional hydrogel electrolytes typically suffer from low mechanical strength and freezing at low temperatures, leading to significant performance degradation. The research group used liquid metal particles as an initiator for polymerization. Under ultrasonication, the bulk liquid metal was broken into fine particles, which then initiated the polymerization of acrylamide and acrylic acid to form the hydrogel. This process eliminates the need for external stimuli such as heat or ultraviolet irradiation, simplifying fabrication and improving scalability. The group added stearyl methacrylate (SMA), a hydrophobic material that does not mix well with water, to create physical crosslinking between polymer chains. These physical crosslinks act as reversible connections within the network. When an external force is applied, the bonds can break to dissipate energy and then easily reform once the stress is released, thereby imparting exceptional stretchability and mechanical robustness to the material. As a result, the elongation at break (defined as the maximum stretch before the material fails) reached up to 900% of its original length. After soaking the hydrogel in a lithium chloride (LiCl) solution, it exhibited anti-freezing properties by suppressing hydrogen bonding between water molecules. It maintains both ionic conductivity and mechanical flexibility even at ?20 °C, where conventional hydrogel systems typically fail. Energy storage devices fabricated with this electrolyte retained 98% of their performance after 45,000 charge-discharge cycles. The research group noted, “For practical applications, it is essential to ensure long-term stability and reproducibility in large-area manufacturing processes.” Prof. Park stated, “This work introduces a new design strategy for hydrogel electrolytes based on liquid metal and provides a viable platform for next-generation wearable electronics and flexible energy storage systems operating under extreme conditions.” The research results were published on March 13 in Nano-Micro Letters. ▲Schematic illustration of the fabrication process and device structure of the liquid metal-based hydrogel electrolyte Published in Nano-Micro Letters SKKU RESEARCH STORY Ultra-Stretchable Anti-Freezing Hydrogel Electrolytes Cross-Linked by Liquid Metal Particle Initiators Toward Soft Energy Storage Devices Access Publication (DOI) SP Sungjune Park PURE Profile →
- No. 377
- 2026-06-02
- 406
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Development of Bayesian Inference for Hidden Dependence Structures in Multi-Group High-Dimensional Data
The research team of Professor Kyoungjae Lee of the Department of Statistics at Sungkyunkwan University, through joint research with Professor Won Chang of Seoul National University and Professor Xuan Cao of the University of Cincinnati, developed Bayesian inference for the hidden dependence structures of multi-group high-dimensional data. A Dependence Map in High-Dimensional Data In today’s scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing. In such data, an important problem is to learn the dependent structures connecting the variables and to identify a “dependence map” that reveals hidden information in massive datasets. For example, in climate data, temperatures in nearby regions may be related to one another, and in genomic data, genes located in adjacent positions may act together. If such dependence can be incorporated into inference, more efficient inference is possible than analyzing each variable separately. Development of the j-LANCE Method for Joint Inference of Dependence Across Multiple Groups The j-LANCE (joint LocAl depeNdence CholEsky) method proposed in this study focuses on the fact that, in real data such as genomic and climate data, variables have a natural ordering and are mainly related to nearby neighboring variables. Based on this idea, the method estimates the extent to which each variable is connected to neighboring variables and is designed to learn similar structures across multiple groups while allowing group-specific differences. In many existing methods, data from multiple groups are either analyzed separately or simplified by assuming that all groups have the same structure. In contrast, this study uses a Markov random field prior so that similarities and differences across groups can be flexibly learned from the data. Simultaneously Achieving Theoretical Accuracy and Fast Computation An important achievement of this study is that it simultaneously attains theoretical accuracy and computational efficiency even in high-dimensional settings. This study theoretically proved that j-LANCE can accurately estimate the dependence structures of multiple groups, and also showed that the rate at which the estimates approach the true values is nearly minimax-optimal. In addition, the methodology was designed to enable Bayesian inference without using MCMC, a complex iterative computation procedure, thereby securing the advantage that fast analysis is possible even for high-dimensional data. Practical Applicability Confirmed Through Climate Data Analysis In this study, ERA5 data were used to analyze temperatures at 30 locations in the Pacific Northwest region of the United States from 2019 to 2021, and the dependence structure of temperatures across regions was estimated based on a spatial ordering that reflects wind flow. As a result, j-LANCE was found to capture similar dependence patterns across years while also detecting distinctive dependence structures that appeared in a specific year. This confirmed the practical applicability of j-LANCE to real data, and the method is expected to be applicable in a wide range of fields that require simultaneous analysis of complex data from multiple groups, including climate, genomics, finance, and sensor time series. *This research achievement was published in Bayesian Analysis, an international journal in the field of statistics. Published in bayesian analysis SKKU RESEARCH STORY The Joint Local Dependence Cholesky Prior for Bandwidth Selection Across Multiple Groups Access Publication (DOI) KL Kyoungjae Lee Professor Profile → Figure 1. Temperature heatmap of ERA5 climate data Figure 2. Cholesky factors estimated from ERA5 climate data. Each column corresponds, from right to left, to 2019, 2020, and 2021, and each row shows, from top to bottom, the results of j-LANCE, year-specific independent LANCE, the penalized likelihood-based inference method, and the group graphical lasso method
- No. 376
- 2026-05-26
- 634
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SKKU Research Team Develops Experimentally Validated AI Model to Predict the Virulence of Tomato Yellow Leaf Curl Virus
A CBBL research team led by Professor Balachandran Manavalan from the Department of Integrative Biotechnology at Sungkyunkwan University has developed DeepTYLCV, an accurate and interpretable artificial intelligence model for predicting the virulence of Tomato Yellow Leaf Curl Virus (TYLCV). The study was conducted with co-first authors Dr. Nattanong Bupi, Hariharan Sangaraju, and Duong Thanh Tran, was published in the leading plant science journal Plant Communications (Impact Factor: 11.6; JCR: 6/273; Top 2.2% in the Plant Sciences category). TYLCV is one of the most destructive viral pathogens affecting tomato production worldwide. Severe TYLCV strains can cause leaf curling, yellowing, stunted growth, and major yield losses. In recent years, highly virulent strains have continued to spread across regions and, in some cases, overcome genetic resistance in tomato cultivars. These challenges highlight the urgent need for accurate, early, scalable, and sequence-based disease surveillance. Prof. Manavalan’s team has been working extensively at the interface of biology and artificial intelligence, developing AI-based solutions for peptide therapeutics, prediction of RNA/DNA modifications, protein function analysis, toxicity prediction, plant science, and biomedical applications. In 2023, the team developed IML-TYLCV, the first genome-based TYLCV severity prediction tool, which was published in the high-impact journal Research (IF: 10.9). However, IML-TYLCV was mainly trained on Korean isolates, limiting its applicability to globally diverse TYLCV strains. This challenge motivated the development of DeepTYLCV, a more robust AI framework for predicting TYLCV virulence across global viral isolates. Unlike conventional field diagnosis or image-based AI models, which depend on visible symptoms and can be influenced by environmental conditions, DeepTYLCV uses viral genome-derived sequence information. This enables the model to identify mild and severe strains before symptom-based confirmation and provides a scalable strategy for monitoring emerging viral variants. DeepTYLCV integrates protein language model embeddings with a hybrid architecture that combines a Transformer encoder and a multi-scale convolutional neural network, enabling the model to capture both global sequence patterns and local virulence-associated motifs. By combining deep sequence representations with optimized conventional feature descriptors, DeepTYLCV achieved superior predictive performance compared with the previous IML-TYLCV model. A key strength of this study is its experimental validation. The research team performed blind predictions for 15 TYLCV isolates, including both international reference isolates and Korean field isolates. These predictions were validated using tomato plant infection assays, symptom severity scoring, and viral accumulation analysis. Remarkably, DeepTYLCV achieved 100% concordance between predicted and experimentally observed virulence classes, demonstrating its practical value for identifying emerging severe TYLCV variants. This work provides a powerful example of how AI, viral genomics, and experimental plant pathology can be integrated to support precision agriculture and plant disease management. DeepTYLCV may serve as a valuable tool for early viral surveillance, resistance breeding programs, and rapid assessment of newly emerging TYLCV strains. This research was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT, Republic of Korea (Grant No. RS-2024-00344752). Additional support was provided by the BK21 FOUR Project of the Department of Integrative Biotechnology, Sungkyunkwan University (SKKU), Republic of Korea. Published in plant communications SKKU RESEARCH STORY DeepTYLCV: An interpretable and experimentally validated AI model for predicting virulence of different tomato yellow leaf curl virus strains Access Publication (DOI) BM BALACHANDRAN MANAVALAN PURE Profile→ Figure 1. Overview of the DeepTYLCV framework. The framework involves six key stages: (A) Collection and pre-processing of global TYLCV genomes into open reading frames. (B) Projection and stacking of PLM/NLP embeddings to capture sequence context. (C) A hybrid Transformer encoder and Multi-scale CNN module for learning global and local virulence patterns. (D) Selection of optimal conventional descriptor features. (E) A multi-layer perceptron (MLP) classifier for severity prediction. (F) Deployment of a user-friendly web server. Figure 2. Experimental validation of DeepTYLCV predictions through symptom development and viral quantification in tomato plants. Plants were agro-inoculated with 15 TYLCV infectious clones. (A) Prediction probabilities from the current method, DeepTYLCV, and the previous method, IML-TYLCV. (B) Viral DNA accumulation at 21 days. (C) Symptom severity was monitored for 21 days. (D) Visual symptoms of infected plants at 21 days. (E) PCR detection confirming viral infection.
- No. 375
- 2026-05-15
- 1910
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Professor Junghee Bae’s Research Team Identifies Performance Differences in Social Enterprises by Legal Form
The research team led by Professor Junghee Bae from the Department of Social Welfare at Sungkyunkwan University analyzed the full population dataset of certified social enterprises in South Korea to compare the social and economic performance of Work Integration Social Enterprises (WISEs) across different legal forms—nonprofit, for-profit, and cooperative organizations. Work Integration Social Enterprises are organizations that pursue both social and economic goals by providing employment opportunities to vulnerable populations who are marginalized in the labor market, while generating revenue through business activities. These enterprises play a crucial role in promoting social integration and economic self-sufficiency among disadvantaged groups. In South Korea, under the Social Enterprise Promotion Act enacted in 2007, organizations must adopt legally recognized forms—such as corporations, social welfare foundations, nonprofit organizations, or cooperatives—in order to receive official certification as social enterprises. The findings reveal that even among social enterprises with the same objective of creating jobs for vulnerable populations, performance varies significantly depending on legal form. In particular, nonprofit social enterprises were found to employ a larger number of vulnerable individuals and to have a higher proportion of such employees in their workforce, indicating stronger social performance compared to for-profit and cooperative counterparts. Moreover, nonprofit social enterprises demonstrated relatively higher net income by leveraging diverse revenue sources, including government subsidies, private donations, and public-sector market sales. In contrast, for-profit and cooperative social enterprises showed relatively higher working hours and wage levels for vulnerable employees. However, these organizations tended to rely more heavily on private market revenues, which was associated with comparatively lower overall financial performance. This study highlights that the choice of legal form plays a critical role in shaping both social value creation and financial sustainability in social enterprises, providing empirical support for institutional theory, which emphasizes the influence of institutional environments on organizational performance. In particular, amid the recent rapid growth of corporation-type social enterprises in South Korea, the findings suggest the need for balanced ecosystem development and stronger policy support for nonprofit social enterprises, especially in light of their effectiveness in achieving the core mission of employing vulnerable populations. The study was published in the leading international journal in the nonprofit field, Nonprofit and Voluntary Sector Quarterly, Volume 55, Issue 2. Published in nonprofit and voluntary sector quarterly SKKU RESEARCH STORY Work integration social enterprises with different legal forms: Performance comparison between nonprofit, for-profit, and cooperative organizations Access Publication (DOI) JB Junghee Bae Profile →
- No. 374
- 2026-05-15
- 616
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From Common Natural Sweetener to High-Performance Energy Material
Professor Kyungwho Choi’s team (co-first authors: Thien Trung Luu and Bui Minh Quang) of the School of Mechanical Engineering at Sungkyunkwan University, in collaboration with Professor Jinsoo Kim’s team in the Department of Chemical Engineering at Kyung Hee University, proposed a strategy that simultaneously overcomes the limitations of conventional hydrogel-based triboelectric nanogenerators (TENGs) — namely low output performance, poor mechanical strength, and insufficient transparency — by utilizing biomimetic stevia. By incorporating stevia into polyvinyl alcohol (PVA), the abundant hydroxyl groups (-OH) simultaneously reinforced the hydrogen bond-based crosslinking structure and crystalline domains, dramatically improving both mechanical strength and ionic conductivity. As a result, the stevia-PVA hydrogel TENG (S-TENG) demonstrated approximately 2–5 times greater mechanical strength and 3–8 times higher electrical output compared to conventional TENGs based on 2D materials, biomaterials, and transparent materials, while maintaining over 70% visible light transmittance. The tensile strength exceeded 25 MPa (in the hydrated state) with an elongation at break surpassing 510%. Furthermore, the research team demonstrated that the S-TENG maintained stable output (~800 V) through 16,000 contact-separation cycles, and confirmed no degradation in electrical output after 30 days of storage at room temperature. The stevia hydrogel can also be recycled via a water-assisted dissolution and re-gelation process, retaining a high output voltage of approximately 600 V after recycling, thus demonstrating its potential as an eco-friendly material. In addition, the research team attached the S-TENG to various body parts — including the wrist, elbow, knee, finger, and throat — and utilized it as a self-powered sensor for detecting diverse human body motions. The rise time in response to finger bending was as fast as 13 ms, and among eleven machine learning models evaluated for motion classification, the XGBoost algorithm achieved the highest classification accuracy of 95.29%. Professor Kyungwho Choi, the corresponding author, stated: "It is highly significant that we successfully developed a hydrogel electrode derived from biomass-based stevia that simultaneously improves transparency, mechanical performance, and electrical output while also securing recyclability. We plan to continue research on applying this technology to a wide range of fields, including IoT-based wearable devices, rehabilitation monitoring, and intelligent human-machine interfaces." This research was supported by the 4th BK21 Future HRD Education and Research Center for Human-Centered Convergence Mechanical Solution and by the Korea government (MSIT). The results were published in Advanced Materials(IF 26.8, within the top 3% of JCR) in April 2026. In addition, this paper was selected for the inside front cover of Advanced Materials. ▲Schematic diagram of the structure and motion recognition system of a stevia-enhanced PVA hydrogel-based wearable sensor ▲ Selected as the Inside Front Cover article in the journal Advanced Materials Published in Advanced Materials High-Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning-Assisted Motion Recognitions Access Publication (DOI) KC Kyungwho Choi PURE Profile →
- No. 373
- 2026-05-11
- 1302
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Does Personalized Virtual Try-On Turn Imagination into Reality?
When shopping for clothing online, how confident can we be without actually trying the product on? In digital shopping environments, consumers often experience uncertainty—such as “Will this really suit me?”—due to the inability to physically interact with products. To address this limitation, virtual try-on technology has emerged. More recently, beyond basic virtual fitting, personalized virtual try-on technologies that reflect individual body shapes and styles have been rapidly advancing. Professor Seeun Kim of Sungkyunkwan University, in collaboration with a research team from Oklahoma State University, conducted an empirical study examining the impact of personalized virtual try-on on consumer decision-making. The research focused particularly on how easily consumers can imagine products and how this process contributes to psychological confidence in purchase decisions. The findings revealed that personalized virtual try-on significantly enhances product imagination. By viewing virtual images that closely resemble their own bodies, consumers are able to vividly imagine themselves wearing the product. This imagination extends beyond a simple cognitive process and directly influences decision-making. The more vividly consumers can imagine a product, the more comfortable and confident they feel about their choices. This suggests that virtual try-on reduces uncertainty—such as “Will this product suit me?”—and facilitates more stable decision-making. Interestingly, these effects were not uniform across all consumers. The study identified spatial processing perception—an individual cognitive trait—as a key moderating factor. The effects of virtual try-on were stronger among consumers with lower spatial processing ability. This can be explained by the fact that individuals who have difficulty mentally visualizing products rely more heavily on the visual information provided by virtual try-on. In contrast, consumers with higher spatial processing ability can already imagine products effectively, making the additional benefits of virtual try-on relatively limited. In other words, personalized virtual try-on is not merely a “better technology,” but rather a technology that is more beneficial for certain consumers. This study is meaningful in that it uncovers the underlying mechanism through which virtual try-on goes beyond visual experience to influence consumers’ psychological decision-making processes. By identifying how product imagination translates into decision comfort, the research provides important implications for designing consumer experiences in online shopping environments. Looking ahead, fashion and e-commerce companies should move beyond simply adopting new technologies and instead develop personalized strategies that align with consumers’ cognitive characteristics to deliver more effective shopping experiences. The study has been published in the internationally recognized SSCI journal Journal of Research in Interactive Marketing. Published in Journal of Research in Interactive Marketing SKKU RESEARCH STORY Unveiling product imagination and decision comfort through personalized virtual try-on: the moderating role of spatial processing perception Available Access Publication (DOI) SK Seeun Kim SKKU Professor →
- No. 372
- 2026-05-07
- 1112
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Revolutionizing Clinical Trials with Machine Learning
Professor Yeonhee Park of the Department of Statistics at Sungkyunkwan University has developed a novel statistical framework — MARGO (Machine Learning-Assisted Adaptive Randomization for Group Sequential Trials Based on Overlap Weights) — that makes machine learning practically applicable in clinical trial design. This work provides the first rigorous solution to the fundamental statistical challenges that arise when integrating ML/AI-driven decision-making into the scientifically demanding environment of clinical trials. The Promise and the Barrier: Why ML/AI Alone Is Not Enough Machine learning and artificial intelligence have garnered widespread attention as transformative tools for personalized treatment assignment in clinical trials. In particular, adaptive randomization — which dynamically adjusts treatment allocation based on accumulating trial data — is a promising approach for improving patient outcomes by steering more participants toward more effective treatments. However, applying this approach in practice can introduce a critical statistical problem. When patient characteristics (e.g., biomarkers) are used to guide treatment assignment, systematic imbalances can emerge between treatment groups. This covariate imbalance leads to biased treatment effect estimates and an inflated type I error rate, risking false conclusions. The problem is further compounded in group sequential designs, which include planned interim analyses for early stopping decisions. Machine Learning Meets Causal Inference: A Two-in-One Solution To address this fundamental challenge, MARGO integrates machine learning-based predictive models with overlap weights (OW), a propensity score–based approach widely used in causal inference to adjust for covariate imbalance. MARGO uses patient covariate information to predict the probability of treatment success via machine learning, then uses these predictions to preferentially assign patients to the more effective treatment. Simultaneously, OW corrects covariate imbalance across treatment groups, effectively controlling the bias and type I error inflation induced by adaptive randomization. The framework was evaluated using four machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Multi-Layer Perceptron (MLP). Rigorously Validated Performance Through extensive simulation studies, MARGO demonstrated superior performance over conventional fixed randomization and existing adaptive randomization methods across three key dimensions. First, MARGO allocated a greater proportion of patients to the more effective treatment. Second, it maintained the overall type I error rate below the target threshold of 0.05 — even in scenarios where conventional methods inflated the error rate to as high as 0.08–0.18. Third, it preserved high statistical power under alternative scenarios while reducing the number of treatment failures. Together, these results demonstrate that MARGO can simultaneously improve the ethical standards and scientific integrity of clinical trials. Beyond "Using AI" — Toward "Trusting AI in Clinical Trials" The most important contribution of this research goes beyond simply applying machine learning to clinical trials — it rigorously resolves the fundamental statistical problems that emerge in that process. MARGO is designed to accommodate a wide range of AI models and holds broad potential for extension to precision medicine and data-driven decision-making across diverse fields. This study was published in Statistics in Medicine in 2025. Published in Statistics in Medicine (2025) SKKU RESEARCH STORY MARGO: Machine Learning-Assisted Adaptive Randomization for Group Sequential Trials Based on Overlap Weights Access Publication (DOI) YP Yeonhee Park SKKU Professor → Figure 1. Design Framework for Adaptive Randomization with Interim Analyses Figure 2. Simulation results: Controlled Type I Error Rates Under the Nominal Level
- No. 371
- 2026-05-07
- 700
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SKKU Revolutionizes Battery Manufacturing with Density Dry Electrode Technology; Targets Foundry Commercialization
A research team led by Professor Young-Jun Kim at the Sungkyunkwan Advanced Institute of Nano Technology (SAINT) of SKKU has announced a breakthrough in "Dry Electrode" technology-a next-generation manufacturing process for batteries. The team has secured original technology for electrodes with world-class energy density by developing materials optimized for solvent-free electrode processing, a feat expected to shift the paradigm of battery production. Dry electrode technology eliminates the use of toxic liquid solvents in the production of lithium-ion and all-solid-state batteries. By directly compacting solid raw materials into electrode films, the process removes the need for energy-intensive drying stages, making it both eco-friendly and highly cost-efficient. This field is currently a focal point for global industry leaders, including Tesla, as companies race to secure dominance in the future battery market. To overcome the chronic challenges of dry processing-specifically the difficulty of uniform mixing and large-scale production-Professor Kim’s team developed a "One-body" material that integrates active materials (for energy storage) and conductive agents (for electron conduction) into a single architecture. This innovation enables the mass production of high-quality, high-loading electrodes. The technical reliability and performance of this method were rigorously validated through collaborative simulations with Professor Yong-Min Lee’s team at Yonsei University. "Dry electrode technology is more than just an eco-friendly process; it is the ultimate solution to dramatically enhancing battery performance, quality, and safety," said Professor Young-Jun Kim. "The specialized materials and production techniques we've developed will serve as a critical stepping stone to significantly reducing manufacturing costs while ensuring global competitiveness in battery performance.“ Beyond academic achievement, the team is aggressively pursuing commercialization. Through Corenergy Solution, a laboratory-backed startup, the team plans to launch a "Battery Electrode Foundry" business specializing in dry electrode manufacturing. In collaboration with other SKKU faculty members with extensive industry experience at South Korean battery giants such as Samsung SDI and LG Energy Solution, the venture aims to advance dry electrode design and cell manufacturing technologies to strengthen the domestic battery ecosystem. This research was supported by the Nano-Material Technology Development Program through the National Research Foundation of Korea (NRF). The team's findings on dry cathode technology were published in Joule (IF 35.4), a premier global journal in the energy field, while their research on anodes was featured in the online edition of Carbon Energy (IF 24.2), gaining worldwide academic recognition. SKKU RESEARCH STORY A continuous carbon nanotube sheath enables ultrahigh energy density and fast charging in dry-processed thick electrodes Joule (DOI) Dry-Processed Graphite Electrodes Enabling Ultra-High Areal Capacity and Stable Fast-Charging Performance Carbon Energy (DOI) YK Young Jun Kim PURE Profile → ▲ Overview of Dry Electrode Manufacturing Process and Breakthrough Material Technologies for Battery Innovation
- No. 370
- 2026-04-28
- 1158
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Prof. Eun Ryung Lee Receives the 2025 Korea Statistical Researcher of the Year for Her Annals of Statistics Paper
Professor Eun Ryung Lee of the Department of Statistics at Sungkyunkwan University received the 2025 2nd Korea Statistical Researcher of the Year in recognition of her paper “Efficient Functional Lasso Kernel Smoothing for High-Dimensional Additive Regression”, which was published in Annals of Statistics in August 2024. The award ceremony was held on August 28, 2025, at the 14th National Statistics Development Forum in Seoul. This award, presented by Statistics Korea, honors outstanding researchers who have made significant contributions to the development of statistics. The paper addresses a fundamental challenge in modern data analysis: how to identify truly important variables and accurately estimate their nonlinear effects when the number of variables is much larger than the sample size. In ultra-high-dimensional settings, it is difficult to achieve variable selection, flexible modeling, computational feasibility, and statistical inference at the same time. This study provides a new solution to that problem. Professor Lee and her collaborators developed a new kernel-based methodology that combines functional Lasso with smooth backfitting. The proposed method can automatically select important variables while flexibly estimating their effects through nonlinear functions, and it is supported by both computationally efficient algorithms and rigorous theoretical analysis. In addition, the study introduces a debiased inference procedure, making it possible not only to improve prediction accuracy but also to construct confidence intervals and conduct hypothesis testing. The proposed method was further applied to large-scale gene expression and anticancer drug response data from cancer cell lines, where it showed strong empirical performance and successfully identified biologically meaningful genes associated with drug response. The study is expected to have broad impact in bioinformatics, precision medicine, finance, environmental science, and other fields where high-dimensional data are increasingly common. This award recognizes both the originality and the practical importance of Professor Lee’s contribution to modern statistical methodology. Published in Annals of Statistics SKKU RESEARCH STORY Efficient functional Lasso kernel smoothing for high-dimensional additive regression Annals of Publication (DOI) EL Eun Ryung Lee Profile→ ▲ A three-stage graphical abstract — "input → method → output" summarizing the core idea of fLasso-SBF Left (Input) A grid of scatter plots for many candidate covariates, with the total number allowed to be much larger than the sample size. Most covariates (grey) carry essentially no information about the response and behave like pure noise, while only the three highlighted ones (red, green, blue) are truly active with genuine nonlinear effects. This depicts the high-dimensional sparse additive regression setting that motivates the paper. Middle (Method) The proposed fLasso-SBF method minimizes an objective that combines kernel-based smooth backfitting with a functional Lasso penalty. Its solution is obtained by iteratively applying a simple "soft-threshold + projection" update, which adds only a single thresholding step to the standard smooth backfitting algorithm and keeps both the implementation and the theoretical analysis clean. Right (Output) Overlay of the estimated component functions produced by fLasso-SBF. Only the three genuinely active components are recovered as smooth curves, while the estimates for the remaining inactive covariates are automatically shrunk to zero. Variable selection and nonparametric function estimation are thus carried out simultaneously in a single procedure, and the accompanying debiased version further enables pointwise confidence intervals and hypothesis testing.
- No. 369
- 2026-04-28
- 807
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Photoactivatable Oligoelectrolytes Engendering Pyroptotic Vesicles
A research team led by Professor Jin Yong Lee from the Department of Chemistry (Co-first author: HyoungChul Ham), in collaboration with research teams from Korea University and the National University of Singapore, has developed a next-generation phototherapeutic agent, ‘NDI-COE’. This agent induces pyroptosis (inflammatory cell death) in hypoxic tumor tissues by directly oxidizing intracellular water. Designed to overcome the oxygen-dependency limits of conventional photodynamic therapy by intercalating into the cell membrane to oxidize water, this research was published in the Journal of the American Chemical Society (IF: 15.7) on January 28, 2026 Notably, Professor Lee's team elucidated the superior photochemical mechanism of NDI-COE at the molecular level based on Density Functional Theory (DFT) calculations. Non-covalent interaction (NCI) analysis revealed that NDI-COE forms double hydrogen bonds with water molecules, stably trapping them with a much stronger binding energy (-5.21 kcal/mol) compared to the control group. Furthermore, electronic structure analysis demonstrated that the spin-orbit coupling (SOC) efficiency for the transition to the highly reactive triplet state (T1) upon light irradiation was approximately 7.5 times higher (2.87 cm-1) than the control, proving its outstanding photochemical reactivity. Moreover, redox potential calculations verified that the excited-state oxidation potential of NDI-COE (-0.39 V) provides the thermodynamic conditions to promote spontaneous electron transfer to oxygen molecules, thereby generating reactive oxygen species highly efficiently. The properties of this novel material, quantitatively characterized through these DFT calculations, are expected to serve as a crucial theoretical foundation for designing innovative and precise next-generation phototherapeutics capable of overcoming hypoxic tumor environments. ▲(A) Non-Covalent Interaction (NCI) analysis of NDI-COE. Blue indicates hydrogen bond, green indicates van der Waals (vdW) interaction, and red indicates steric repulsion, respectively. (B) Bond length and binding site between NDI-COE and water. (C) Calculated Raman spectral comparison of NDI-COE in the presence of water. (D) NCI analysis of Ben-COE. (E) Bond length and binding site between Ben-COE and water. (F) Calculated Raman spectral comparison of Ben-COE in the presence of water. Energy level diagram and photophysical processes of (G) Ben-COE and (H) NDI-COE. In 3G, S0 : neutral molecule in the singlet ground state D0 : ground state doublet of the anion formed after accepting one electron. AEA: adiabatic electron affinity. (I) Ground state and excited state redox potentials (V vs NHE) were calculated by DFT using the polarizable continuum model (PCM) with water as a solvent. (J) Ground state and excited state redox potentials (E??, E??d, E??*, E??d*; V vs NHE) and Frontier orbital energies (HOMO, LUMO; eV) of NDI-COE and Ben-COE, calculated by DFT using PCM with water as a solvent. Published in journal of the american chemical society SKKU RESEARCH STORY Photoactivatable Oligoelectrolytes Engendering Pyroptotic Vesicles Access Publication (DOI) JL Jinyong Lee PURE Profile →
- No. 368
- 2026-04-24
- 1106
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SKKU Publishes Report on Technological Advances and Research Trends in Perovskite Solar Cells
Sungkyunkwan University announced the publication of a comprehensive report analyzing technological advancements and global research trends in perovskite solar cells (PSCs), which are emerging as a next-generation solar energy technology. The report was jointly prepared by the research group of Professor Nam-Gyu Park from the Department of Chemical Engineering at SKKU and the university’s Performance Analysis Team, in collaboration with global academic data company Clarivate. It covers the fundamental concepts and technological evolution of perovskite solar cells, as well as the global research landscape and key trends in the field. The report highlights a major turning point in 2012, when researchers at SKKU successfully developed the world’s first solid-state perovskite solar cell. Since then, the technology has achieved rapid efficiency improvements over a short period and has emerged as a promising next-generation solar technology. It is now considered a key solution with the potential to complement or even replace conventional silicon-based solar cells. Based on data from Web of Science, the report also analyzes research performance by country, institution, and researcher. The findings show that the field of perovskite solar cells has grown rapidly since 2012, with China, the United States, and South Korea leading global research efforts. Among them, Sungkyunkwan University demonstrates world-class research competitiveness, ranking highly in key indicators such as publication output, citation impact, and the proportion of top 1% highly cited papers, establishing itself as a major research hub in the field. The report further emphasizes that perovskite solar cells are a critical technology from both carbon neutrality and ESG perspectives, owing to their high efficiency, low-cost fabrication processes, and lightweight and flexible properties. In particular, advancements in tandem solar cell structures that surpass the efficiency limits of conventional solar cells are expected to further accelerate their commercialization potential. Professor Nam-Gyu Park stated, “As future energy systems are expected to be largely electrified, the importance of electrification technologies continues to grow. Solar energy, which produces no carbon emissions, is anticipated to play a central role in the future energy landscape. In this context, low-cost, high-efficiency perovskite solar cells are expected to serve as a key technology in the next-generation energy society. Sungkyunkwan University has played a pivotal role from the early conceptual development to technological advancements in this field, and will continue to lead global research efforts.” Meanwhile, Clarivate and Sungkyunkwan University plan to co-host a global webinar in June to highlight research trends in perovskite solar cells. Published with Clarivate SKKU RESEARCH STORY Perovskite Solar Cells: A Roadmap for Advancing Future Energy Innovation Report Download NP Nam-Gyu Park Profile → → ▲ Diverse device architecture and efficiency progress in PSCs
- No. 367
- 2026-04-20
- 1334
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SKKU Researchers Develop Breakthrough “Gas Battery” Technology that Generates Electricity from Greenhouse Gases
Professor Ji-Soo Jang from the Department of Nanoengineering, in collaboration with Professor Taekwang Yoon of Ajou University and Professor Hansel Kim of Chungbuk National University, has developed a novel energy device that generates electricity during the process of capturing greenhouse gases. The research team introduced a new concept device termed the Gas Capture and Electricity Generator (GCEG), which produces electrical power as greenhouse gases are adsorbed from the atmosphere. This innovation goes beyond conventional approaches that merely capture greenhouse gases, transforming them into a usable energy resource. Amid growing global efforts to address climate change, carbon capture, utilization, and storage (CCUS) technologies have gained attention. However, existing CCUS systems typically require substantial energy input for gas collection and processing. To overcome this limitation, the research team proposed a fundamentally new mechanism that directly converts the physicochemical energy generated during gas adsorption on electrode surfaces into electrical energy. The developed GCEG device consists of an asymmetric structure combining carbon-based electrodes with hydrogel materials. When greenhouse gases such as nitrogen oxides (NOx) or carbon dioxide (CO?) are adsorbed, charge redistribution and ion migration occur within the device, enabling continuous direct current (DC) power generation without any external power source. In essence, atmospheric pollutants act as the “fuel” for electricity generation, simultaneously purifying the environment while supplying energy. This technology is expected to be widely applicable in self-powered smart environmental sensors, battery-free IoT systems, and industrial facilities where large volumes of emissions are generated. In such settings, it could enable simultaneous energy harvesting and carbon reduction. In particular, its integration into distributed energy systems is anticipated to accelerate the realization of carbon neutrality. Professor Ji-Soo Jang stated, “This research demonstrates that greenhouse gases are not merely pollutants to be managed, but can serve as a new energy resource. We aim to further develop this technology into an environmental platform that not only achieves carbon neutrality but also generates energy.” The research findings were published in Energy & Environmental Science (Impact Factor: 31.0), one of the world’s leading journals in materials science, and were selected as a Front Cover article in recognition of their excellence and originality. Published in energy & environmental science SKKU RESEARCH STORY Electrical power generation from asymmetric greenhouse gas capture Access Publication (DOI) JJ Ji-Soo Jang Profile → ▲ Cover image of the paper on the greenhouse gas adsorption-based power generation device, “Gas Capture and Electricity Generator (GCEG)” ▲ Schematic illustration explaining the working principle of the greenhouse gas adsorption-based power generation device, “Gas Capture and Electricity Generator (GCEG)”
- No. 366
- 2026-04-16
- 1912
