Kicheol Lee | Engineering | Best Researcher Award

Published on by Invention Awards

Dr. Kicheol Lee | Engineering | Best Researcher Award

Dr. Kicheol Lee | Halla University/RISE Project Group | South Korea

Dr. Kicheol Lee is a research professor specializing in civil and structural engineering, with a strong record in foundation engineering, numerical modelling, and new technology development. His work spans artificial intelligence (machine learning, deep learning), probabilistic and statistical methods, field applications in geotechnical/tunnel/foundation engineering, and reliability-based design (LRFD). He has been recognized with multiple best paper and presentation awards from the Korea Geosynthetics Society and the Korea Geotechnical Society. His expertise in numerical simulation (particularly via ABAQUS), and integration of AI/ML with civil engineering systems, has made him a leading figure in predictive modeling, anomaly detection, and structural reliability. Dr. Lee’s contribution lies in bridging advanced computational methods with practical engineering challenges, especially in ensuring safety, resilience, and sustainability of infrastructure. Dr. Lee’s current research is deeply interdisciplinary, merging geotechnical engineering, structural health monitoring, and intelligent systems to create safer, data-driven infrastructure solutions.His ongoing work under the Gangwon RISE Project aims to transform urban safety and sustainability by employing augmented and virtual reality technologies for real-time disaster visualization and early warning.

Author’s Profile

Scopus | Orcid

Early Academic Pursuits

Dr. Kicheol Lee began his academic journey in Civil and Environmental Engineering at Incheon National University, where he earned his Bachelor’s degree (2015), Master’s degree (2017), and Doctorate (Ph.D., 2021). His early research concentrated on geotechnical and foundation engineering, particularly the mechanical behavior of pile groups and the evaluation of soil–structure interactions through numerical and experimental methods. His doctoral dissertation, “Evaluation of Resistance Factors of Pile Groups Consisting of Drilled Shafts Embedded in Sandy Ground under Axial Load through Numerical Analysis,” established his expertise in reliability-based foundation design (LRFD) and computational modeling using ABAQUS, laying the groundwork for his later innovations in smart infrastructure systems.

Professional Endeavors

Dr. Lee’s professional career seamlessly bridges academia, industry, and national research initiatives, reflecting his commitment to advancing digitally enhanced civil infrastructure technologies. He currently serves as a Research Professor at Halla University under the RISE Project Group (since September 2025), where he leads the Gangwon RISE Project focused on developing advanced safety and green city technologies through the integration of Digital Twin and 3D data. Prior to this role, he was a Principal Researcher at the Korea Institute of Structural Integrity Research (2024–2025), where he led national R&D projects centered on innovative construction technologies and safety inspection systems. From 2021 to 2024, he served as Research Director at UCI Tech Co., Ltd., managing government-funded initiatives that merged IoT and augmented reality (AR) technologies for infrastructure maintenance and smart monitoring applications. Across these roles, Dr. Lee has demonstrated a clear progression from applied geotechnical engineering toward the fusion of engineering mechanics, intelligent systems, and data science to create more resilient, sustainable, and intelligent civil infrastructure.

Contributions and Research Focus

Dr. Lee’s interdisciplinary research bridges geotechnical engineering with artificial intelligence, probability, and information technologies to develop data-driven and intelligent systems for the monitoring, design, and maintenance of civil infrastructures. His expertise spans artificial intelligence—particularly the application of convolutional and recurrent neural networks (CNNs and RNNs) for anomaly detection, predictive modeling, and data-driven decision-making in structural health monitoring—as well as foundation and tunnel engineering, focusing on advanced modeling and soil–structure interaction analysis. He is also skilled in numerical analysis using ABAQUS to simulate complex geotechnical phenomena and evaluate soil–structure responses. In addition, Dr. Lee integrates reliability and probabilistic design principles through statistical modeling, Monte Carlo simulations, and Bayesian inference within LRFD-based design frameworks. His innovative contributions extend to smart infrastructure and safety systems, including the development of AI-enabled inspection robots, reversible thermochromic materials for black-ice prevention, and UAV-based soil monitoring systems utilizing hyperspectral imaging. He has led or contributed to 11 major national R&D projects funded by various Korean ministries—including those of Education, Environment, Land, Transport, Industry, and SMEs & Startups—addressing challenges in smart cities, environmental protection, and disaster prevention, all aimed at advancing sustainable and resilient civil infrastructure.

Impact and Influence

Dr. Lee’s scholarly influence is reflected in his prolific publication record, with over 50 peer-reviewed journal papers—15 indexed in SCI/SCI(E), 34 in Korean journals, and 2 in Scopus. His research has appeared in leading international journals such as Applied Sciences, Sustainability, Remote Sensing, Polymers, and Tunnelling and Underground Space Technology. His academic excellence has been recognized through several prestigious awards, including the Best Paper Presentation Awards from the Korea Geosynthetics Society and the Korea Geotechnical Society in 2020, and the Best Paper Award from the Korea Geosynthetics Society in 2019. Complementing his scholarly achievements, Dr. Lee holds 15 registered patents in the Republic of Korea, showcasing his technological innovation in civil engineering through the development of smart barriers, reversible paints for road safety, and advanced pile systems. Beyond research, he actively contributes to the professional community as an Editorial Board Member of the Korea Geosynthetics Society (2024–Present), and as Assistant Administrator of both the Low-Carbon Construction Committee and the Incheon Regional Committee of the Korean Geotechnical Society (since 2023). Through these roles, Dr. Lee fosters academic collaboration, encourages the dissemination of innovation, and advances sustainable engineering practices in the civil infrastructure domain.

Academic Cites

Dr. Lee’s work is frequently cited in research concerning geotechnical reliability, foundation engineering, and smart civil technologies. His papers on hyperspectral soil analysis and negative skin friction in piles have become valuable references in data-integrated geotechnical research. By bridging machine learning with traditional civil engineering models, his methodologies have influenced new approaches to predictive maintenance and risk-based infrastructure management in both academia and industry.

Legacy and Future Contributions

Dr. Kicheol Lee embodies a new generation of civil engineers who seamlessly integrate artificial intelligence, sustainability, and resilience into traditional infrastructure systems. His pioneering work on AI-driven monitoring, Digital Twin simulations, and smart geotechnical materials is reshaping the future of infrastructure safety and environmental protection. Looking ahead, Dr. Lee aspires to expand the application of augmented reality (AR) and digital twin technologies for real-time disaster prediction and response, develop autonomous robotic systems for structural inspection and maintenance, and contribute to global initiatives promoting smart and sustainable urban development in the face of climate change. His long-term vision is centered on building data-informed, intelligent, and resilient civil infrastructure systems that not only enhance public safety and operational efficiency but also minimize environmental impact—paving the way for the realization of next-generation smart and sustainable cities.

Featured Publications

Lee, K. (2024). Verification of construction method for smart liners to prevent oil spill spread in onshore. Sustainability, 16(23), 10626. https://doi.org/10.3390/su162310626

Lee, K. (2023). Proposal of construction method of smart liner to block and detect spreading of soil contaminants by oil spill. International Journal of Environmental Research and Public Health, 20(2), 940. https://doi.org/10.3390/ijerph20020940

Lee, K. (2022). Spectrum index for estimating ground water content using hyperspectral information. Sustainability, 14(21), 14318. https://doi.org/10.3390/su142114318

Lee, K. (2022). Prediction of ground water content using hyperspectral information through laboratory test. Sustainability, 14(17), 10999. https://doi.org/10.3390/su141710999

Lee, K. (2021). Analysis of vertical earth pressure acting on box culverts through centrifuge model test. Applied Sciences, 12(1), 81. https://doi.org/10.3390/app12010081

Lee, K. (2020). Numerical analysis of the contact behavior of a polymer-based waterproof membrane for tunnel lining. Polymers, 12(11), 2704. https://doi.org/10.3390/polym12112704

Lee, K. (2020). Analysis of effects of rock physical properties changes from freeze–thaw weathering in Ny-Ålesund region: Part 2—Correlations and prediction of weathered properties. Applied Sciences, 10(10), 3392. https://doi.org/10.3390/app10103392

Lee, K. (2020). Analysis of effects of rock physical properties changes from freeze–thaw weathering in Ny-Ålesund region: Part 1—Experimental study. Applied Sciences, 10(5), 1707. https://doi.org/10.3390/app10051707

Mr. Chibuzo Nwabufo Okwuosa | Fault Detection | Best Researcher Award

Published on by Invention Awards

Mr. Chibuzo Nwabufo Okwuosa | Fault Detection | Best Researcher Award

Kumoh National Institute of Technology, South Korea.

Okwuosa Chibuzo Nwabufo is a Research Ph.D. Scholar at Kumoh National Institute of Technology 🇰🇷, South Korea, specializing in Mechanical Engineering. With a strong foundation in machine learning, deep learning, and real-time fault diagnostics, his work emphasizes bridging theoretical innovation with industrial application. Chibuzo is passionate about Prognostics and Health Management (PHM), Explainable AI (XAI), and digital twin technologies, aiming to create smart, AI-driven maintenance systems for next-generation industries.

Profile

Scopus
Orcid
Google Scholar

🎓 Education

Chibuzo earned both his Master’s and is currently pursuing his Ph.D. in Mechanical Engineering from Kumoh National Institute of Technology, South Korea. His academic focus has been consistently rooted in intelligent fault diagnostics, predictive maintenance, and real-time monitoring technologies.

💼 Experience

With over four completed and two ongoing research projects, Chibuzo has hands-on experience in both academia and industry. Notable projects include real-time diagnostics for diaphragm pumps, fault analysis in induction motors, and zinc phosphating coating processes. He has collaborated on industry-sponsored projects and led initiatives involving advanced data-driven solutions for predictive maintenance.

🔬 Research Interests

His key research domains include:

🔧 Prognostics and Health Management (PHM)

🤖 Machine Learning & Deep Learning

🧠 Explainable AI (XAI)

🌐 Digital Twin Technologies

⚙️ Real-time Fault Diagnostics

🏆 Awards & Grants

Chibuzo’s research has been supported by prestigious Korean government grants:

IITP Innovative Human Resource Development for Local Intellectualization

ITRC Program (MSIT, Korea)
These grants facilitated collaborations with industry leaders and funded cutting-edge research in diagnostics and manufacturing innovation.

📚 Selected Publications

🆕 Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems

📅 2025-04-13 | Sensors
🔗 DOI: 10.3390/s25082449
👨‍🔬 Contributors: Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora, Jang-Wook Hur
📌 Application of deep learning and high-resolution imaging for defect detection on challenging industrial surfaces.

⚙️ A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

📅 2024-10-09 | Journal of Sensor and Actuator Networks
🔗 DOI: 10.3390/jsan13050064
👨‍🔬 Contributors: Joon-Hyuk Lee, Chibuzo Nwabufo Okwuosa, Baek Cheon Shin, Jang-Wook Hur
📌 Fault detection in mechanical components using spectral features and XGBoost.

🔍 Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

📅 2024-02-29 | Electronics
🔗 DOI: 10.3390/electronics13050926
👨‍🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Intelligent transformer fault diagnosis using statistical signal analysis and feature engineering.

⚡ Enhancing Transformer Core Fault Diagnosis and Classification through Hilbert Transform Analysis of Electric Current Signals

📅 2024-01-18 | Preprint
🔗 DOI: 10.20944/preprints202401.1371.v1
👨‍🔬 Contributors: Daryl Domingo, Akeem Bayo Kareem, Chibuzo Nwabufo Okwuosa, Paul Michael Custodio, Jang-Wook Hur
📌 Preprint focusing on enhanced signal processing for electrical fault classification.

🧠 An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

📅 2023 | IEEE Access
🔗 DOI: 10.1109/ACCESS.2023.3266865
👨‍🔬 Contributors: Chibuzo Nwabufo Okwuosa, Jang-Wook Hur
📌 Machine learning-based fault classification in squirrel cage induction motors under low-load conditions.

 

 

 

Dr. Xiuling Wang | Molecular biology | Best Researcher Award

Published on by Invention Awards

Dr. Xiuling Wang | Molecular biology | Best Researcher Award

Shandong University, China.

Dr. Xiuling Wang is a researcher at the State Key Laboratory of Microbial Technology, Shandong University. Her expertise lies in DNA editing technology, focusing on the mining, modification, and biosynthesis of microbial natural products. She completed her master’s and doctoral studies under Prof. Jun Fu at Shandong University, following her undergraduate research at Shandong Agricultural University under Prof. Binghai Du, where she investigated plant-rhizosphere growth-promoting bacteria.

Profile

Scopus

📚 Education

Dr. Xiuling Wang holds a Ph.D. in Molecular Biology from Shandong University, where she further advanced her expertise in the biological sciences. She also earned a Master’s degree in Microbial Technology from Shandong University, deepening her understanding of the practical applications of microorganisms. Dr. Wang's academic journey began with a Bachelor's degree in Agricultural Sciences from Shandong Agricultural University, laying the foundation for her career in research. Her work focuses on the intersection of molecular biology, microbial technology, and agricultural sciences, contributing to advancements in these fields.

💼 Experience

Dr. Xiuling Wang is a dedicated researcher at the State Key Laboratory of Microbial Technology, Shandong University, where she explores cutting-edge advancements in microbial science. She is also actively involved in the National Key Research & Development Program of China (2018YFA0900400), contributing to innovative research initiatives aimed at addressing critical scientific and industrial challenges. Her expertise in molecular biology and microbial technology positions her at the forefront of groundbreaking developments in these fields.

🔬 Research Interests

Molecular Biology

DNA Editing & Recombinant Systems

Microbial Natural Product Biosynthesis

Silent Gene Cluster Activation

🏆 Awards & Achievements

Principal Investigator in a National Key Research & Development Program of China

Two China Invention Patents (Application No: 202411054594.4 & CN202411058039.9)

Discovered a novel lipopeptide (Plantariitin A) with anti-inflammatory properties

📄 Publications

A recombineering system for Bacillus subtilis based on the native phage recombinase pair YqaJ/YqaK
Q. Liu, R. Li, H. Shi, Y. Zhang, J. Fu, X. Wang, Engineering Microbiology, 2023.

Genomics-Driven Discovery of Plantariitin A, a New Lipopeptide in Burkholderia plantarii DSM9509
Xiuling Wang, Zhuo Zhang, Jun Fu, Ruijuan Li
Molecules, 2025, 30(4), 868
DOI: 10.3390/molecules30040868

 

 

Assoc. Prof. Dr. Riccardo Sacco | Mathematical Modeling in Medicine | Best Researcher Award

Published on by Invention Awards

Assoc. Prof. Dr. Riccardo Sacco | Mathematical Modeling in Medicine | Best Researcher Award

Icahn School of Medicine at Mount Sinai Hospital New York, United States.

Riccardo Sacco is an Italian mathematician and engineer based in New York, USA. He is currently an Associate Scientist at the Icahn School of Medicine, Mount Sinai Hospital, with over two decades of expertise in numerical analysis, computational modeling, and applied mathematics. Riccardo has published extensively, mentoring numerous students and contributing significantly to research in ophthalmology and engineering applications.

Profile

Orcid

🎓 Education

Assoc. Prof. Dr. Riccardo Sacco is a distinguished academic and researcher with expertise in electronic engineering and applied mathematics. Holding a combined B.Sc./M.Sc. in Electronic Engineering from Politecnico di Milano (1989) and a Ph.D. in Applied Mathematics from Università degli Studi di Milano (1993), he has significantly contributed to the field through innovative research and teaching. Following his doctoral studies, Dr. Sacco pursued post-doctoral fellowships at CNR and Università degli Studi di Milano (1993–1995), further advancing his knowledge in applied mathematics. Currently, he serves as an Associate Professor, sharing his expertise and fostering the next generation of engineers and mathematicians.

💼 Experience

Assoc. Prof. Dr. Riccardo Sacco has built a remarkable academic and professional career, blending teaching, research, and global collaboration. Since 2024, he has served as an Associate Scientist at the Icahn School of Medicine, Mount Sinai Hospital in New York, USA, where he contributes to groundbreaking research in applied mathematics and computational methods. Prior to this role, he spent over two decades at Politecnico di Milano (PoliMi), serving as an Associate Professor of Numerical Analysis from 2001 to 2024, and earlier as an Assistant Professor in Numerical Analysis from 1995 to 2001.

Dr. Sacco’s academic journey is enriched by several international appointments as a Visiting Scientist, including at AT&T Bell Labs (1990), Georgia Tech (2003), Université de Strasbourg (2015), and North Carolina State University (2018). These experiences reflect his commitment to advancing numerical analysis and fostering global scientific collaboration.

🔬 Research Interests

Numerical Analysis and Computational Modeling: Application in engineering, medicine, and electronics

Ophthalmology: Modeling aqueous humor dynamics and intraocular pressure

Multi-physics and Multi-scale Problems: Nano-bio-electronics and semiconductor modeling

🏆 Awards and Honors

2010: Co-author of the Most Downloaded Article in Computer Methods in Applied Mechanics and Engineering

2001: Featured in Who’s Who in the World, 18th Edition

📚 Selected Publications

Reduced-Order Model for Cell Volume Homeostasis: Application to Aqueous Humor Production
Mathematical and Computational Applications
2025-01-24 | Journal Article
Contributors: Riccardo Sacco, Greta Chiaravalli, Giovanna Guidoboni, Anita Layton, Gal Antman, Keren Wood Shalem, Alice Verticchio, Brent Siesky, Alon Harris

Reduced-Order Model for Cell Volume Homeostasis: Application to Aqueous Humor Production
Preprint
2024-12-19
Contributors: Riccardo Sacco, Greta Chiaravalli, Giovanna Guidoboni, Anita Layton, Gal Antman, Keren Wood Shalem, Alice Verticchio, Brent Siesky, Alon Harris

The Role of Conventional and Unconventional Adaptive Routes in Lowering Intraocular Pressure: Theoretical Model and Simulation
Physics of Fluids
2023-06-01 | Journal Article
Contributors: Riccardo Sacco, Greta Chiaravalli, Gal Antman, Giovanna Guidoboni, Alice Verticchio, Brent Siesky, Alon Harris

Nanoparticle-Based Retinal Prostheses: The Effect of Shape and Size on Neuronal Coupling
Photonics
2022-09-29 | Journal Article
Contributors: Greta Chiaravalli, Guglielmo Lanzani, Riccardo Sacco

Corrections to “Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling”
IEEE Transactions on Biomedical Engineering
2020-10 | Journal Article
Contributors: Giovanna Guidoboni, Lorenzo Sala, Moein Enayati, Riccardo Sacco, Marcela Szopos, James M. Keller, Mihail Popescu, Laurel Despins, Virginia H. Huxley, Marjorie Skubic