Kwang-Ho Seok | Artificial Intelligence | Best Researcher Award

Published on by Award and Honors

Prof. Kwang-Ho Seok | Artificial Intelligence | Best Researcher Award

Professor | Global Cyber University | South Korea

Professor Kwang Ho Seok is a distinguished scholar specializing in Artificial Intelligence, Extended Reality, Robotics, and Data Analysis at the School of AI Convergence, Global Cyber University, South Korea. where he developed expertise in robotics, control systems, and intelligent algorithms. With over fifteen years of academic and research experience, he has collaborated with leading institutions such as ETRI and Hyundai Motor Company, spearheading projects in power system automation, AR/VR education, and smart device optimization. His research integrates AI, human-computer interaction, and digital learning environments to enhance immersive technologies and autonomous robotic systems. Widely published in SCIE and Scopus-indexed journals, he has authored 17 documents with 79 citations across 78 referencing papers. His work reflects a strong commitment to advancing intelligent, human-centered innovation in AI-driven technologies.

Profiles : Scopus | Google Sholar 

Featured Publications : 

Seok, K. H., Kim, Y. H., & Son, W. H. (2021). Using visual guides to reduce virtual reality sickness in first-person shooter games. JMIR Serious Games, 9(3). (Cited by 42)

Seok, K. H., & Kim, Y. S. (2015). An in-vehicle application providing system based on driver’s biodata. Journal of Sensors. Hindawi. (Cited by 36)

Seok, K. H., & Kim, Y. S. (2016). A state of the art of power transmission line maintenance robots. Journal of Electrical Engineering and Technology, 11(5), 1412–1422. (Cited by 48)

Seok, K. H., & Kim, Y. H. (2020). A virtual reality sickness reduction based on real-time individual characteristics. International Journal of Advanced Science and Technology, 29(3), 5999–6009. (Cited by 28)

Licheng Liu | Computer Science | Best Researcher Award

Published on by Award and Honors

Assoc. Prof. Dr. Licheng Liu | Computer Science | Best Researcher Award 

Associated professor, at Hunan University, China.

Dr. Licheng Liu (刘立成) is an Associate Professor at the School of Electrical and Information Engineering, Hunan University, China. He earned his Ph.D. from the University of Macau under the mentorship of Prof. C.L. Philip Chen, a Fellow of the IEEE and a Member of the European Academy of Sciences. Dr. Liu is recognized as a Yue Lu Scholar and serves as a Ph.D. advisor. He is a Senior Member of IEEE and has received the Hunan Provincial Outstanding Young Scientist Fund. His research interests encompass deep learning, broad learning systems, and sparse manifold learning. He has authored nearly 50 papers in top-tier journals and conferences, including IEEE Transactions on Cybernetics, Neural Networks and Learning Systems, and Circuits and Systems for Video Technology. His work has garnered over 1,265 citations and an h-index of 17.

Professional Profile

Scopus

🎓 Education 

Dr. Liu’s academic journey began with a Bachelor’s degree in Mathematics and Physics from China University of Geosciences (Wuhan) in 2010. He then pursued a Master’s degree in Mathematics at Hunan University, graduating in 2012. His doctoral studies were completed at the University of Macau in 2016, where he worked under the supervision of Prof. C.L. Philip Chen. Throughout his education, Dr. Liu focused on areas such as sparse representation, image processing, and machine learning, laying a strong foundation for his subsequent research endeavors.

💼 Experience

Dr. Liu commenced his professional career as an Assistant Professor at Hunan University’s School of Electrical and Information Engineering in 2016. By 2019, he was promoted to Associate Professor and was honored as a Yue Lu Scholar. In his academic role, Dr. Liu has supervised numerous graduate students and has been actively involved in various research projects, particularly those funded by the National Natural Science Foundation of China. His research contributions have significantly advanced the fields of image restoration, face hallucination, and noise reduction in visual data.

🔬 Research Interests 

Dr. Liu’s research interests are centered on deep learning, broad learning systems, and sparse manifold learning. He is particularly focused on developing novel algorithms and models to enhance image restoration, low-light object detection, and low-quality image recognition. His work aims to address challenges in visual data processing, such as noise reduction and image enhancement, by leveraging advanced machine learning techniques. Dr. Liu’s innovative approaches have led to the development of robust models capable of improving the quality and accuracy of visual data interpretation in various applications.

🏆 Awards 

Dr. Liu has received several prestigious awards throughout his career. In 2016, he was honored with the Macao SAR Graduate Student Science and Technology Research Award by the Macao Science and Technology Development Fund. In 2018, he was recognized as a Yue Lu Scholar by Hunan University. His excellence in teaching was acknowledged in 2021 when he received the First-Class Teaching Achievement Award from Hunan University. The following year, he was awarded the Special Prize for Higher Education Teaching Achievement by the Hunan Provincial Department of Education. In 2023, Dr. Liu received the National Teaching Achievement Award (Second Class), and in 2024, he was named an Outstanding Master’s Thesis Advisor in Hunan Province. Additionally, he was honored with the Third Prize in Natural Science by the Chinese Association of Automation in 2024.

📚Top Noted  Publications 

Dr. Liu has authored nearly 50 research papers, with 25 published in IEEE/ACM journals. Notable publications include:

1. Weighted Joint Sparse Representation for Removing Mixed Noise in Image (2017)

  • Journal: IEEE Transactions on Cybernetics, 47(3), 600–611.

  • Summary: This paper introduces a method for removing mixed noise in images using a weighted joint sparse representation. The approach aims to effectively address challenges posed by mixed noise types in image processing.

2. Robust Face Hallucination via Locality-Constrained Bi-Layer Representation (2018)

  • Journal: IEEE Transactions on Cybernetics, 48(4), 1189–1201.

  • Summary: The authors propose a robust face hallucination method that utilizes a locality-constrained bi-layer representation. This technique enhances face image resolution while maintaining robustness against noise and outliers. europepmc.org

3. Mixed Noise Removal via Robust Constrained Sparse Representation (2018)

  • Journal: IEEE Transactions on Circuits and Systems for Video Technology, 28(9), 2177–2189.

  • Summary: This paper presents a robust constrained sparse representation method for removing mixed noise from images. The approach adapts to different noise types and effectively restores image quality. figshare.com

4. Discriminative Face Hallucination via Locality-Constrained and Category Embedding Representation (2021)

  • Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 7314–7325.

  • Summary: The authors introduce a discriminative face hallucination method that combines locality-constrained representation with category embedding. This approach improves the quality of face image super-resolution by considering category-specific information.

5. Modal-Regression-Based Broad Learning System for Robust Regression and Classification (2023)

  • Journal: IEEE Transactions on Neural Networks and Learning Systems, 35(9), 12344–12357.

  • Summary: This paper proposes a modal-regression-based broad learning system to enhance robustness in regression and classification tasks. The method addresses challenges posed by noisy and outlier-prone data, improving model performance. pubmed.ncbi.nlm.nih.gov

Conclusion

Dr. Licheng Liu demonstrates exceptional strength as a mid-career researcher with an outstanding publication record, robust funding history, and recognized academic leadership in AI and image processing. His ability to balance theoretical innovation with practical application is evident in his funded projects and impactful publications.

Jiseong Byeon | Computer Science | Best Researcher Award

Published on by Award and Honors

Mr. Jiseong Byeon | Computer Science | Best Researcher Award 

Mr. Jiseong Byeon at Department of Industrial and Systems Engineering, Dongguk University, South Korea.

Jiseong Byeon is a passionate and emerging researcher in the field of artificial intelligence and computer vision, currently pursuing an M.S. in Industrial and Systems Engineering at Dongguk University, Seoul. With a multidisciplinary academic background combining global business and systems engineering, Jiseong brings a unique blend of strategic thinking and technical expertise. His research is centered around the development of intelligent image-based systems, particularly in the medical domain. He has experience working with advanced deep learning frameworks and has contributed to projects involving 3D human modeling and predictive analytics. Known for his curiosity and collaborative spirit, he aims to advance healthcare and human-computer interaction through innovative AI models. 📸🧠💡

Professional Profile

ORCID

🎓 Education

Jiseong Byeon is currently enrolled in a Master’s program in Industrial and Systems Engineering at Dongguk University, Seoul, beginning in September 2024. He previously earned his Bachelor of Arts in Global Business from Dong-A University in Busan, graduating in August 2024. His educational journey has been a unique blend of global business principles and technical problem-solving, giving him a diverse perspective on interdisciplinary research. During his undergraduate years, Jiseong began exploring data science and AI applications, which led him to transition fully into research-focused engineering. Through academic coursework, hands-on lab experiences, and independent study, he has built a solid foundation in data analytics, deep learning, and applied computer vision techniques. 🏫📚🧑‍🎓

💼 Experience

Jiseong Byeon has amassed valuable research experience across both undergraduate and graduate levels. Currently serving as a Graduate Researcher at Dongguk University since September 2024, he is engaged in developing models for 3D human body reconstruction using Vision Transformer architectures. This cutting-edge work aims to transform how AI interprets and renders human anatomy in digital formats. Previously, from March 2022 to August 2024, he worked as an Undergraduate Research Assistant at Dong-A University. There, he contributed to building encoding-based click prediction models and performed in-depth crime factor analysis using Seoul city data. These diverse experiences have honed his data interpretation skills and technical creativity, preparing him for advanced research and real-world AI application. 🖥️🔍📊

🔬 Research Interests

Jiseong Byeon’s research interests lie at the intersection of artificial intelligence, computer vision, and human modeling. His key areas include Image-to-Image Translation using the Pix2Pix framework, 3D Human Body Modeling, and Vision Transformers for medical applications. He is deeply motivated to apply deep learning algorithms to tasks that require detailed visual interpretation—especially those in the medical field where accurate prediction can significantly enhance outcomes. His work also explores how AI can be used for real-time inference and post-surgical visualization, such as predicting body shape changes. Additionally, Jiseong is keen on exploring the scalability of such models for widespread, ethical, and efficient implementation. 🤖🧬👨‍⚕️

🏆 Awards

While still early in his research career, Jiseong Byeon has shown exceptional promise and has been consistently recognized by his academic mentors for his innovation and diligence. He has been nominated for several internal research awards at Dong-A University, particularly for his work on crime prediction modeling and click prediction systems. His transition to graduate-level research was also supported by faculty recommendations based on the excellence of his undergraduate research projects. With his first peer-reviewed publication accepted and increasing involvement in high-impact research domains, he is a strong candidate for early-career research recognition and award nominations. 🏅📈🌟

📚 Top Noted Publications

Byeon has contributed to a peer-reviewed article that showcases the application of deep learning in medical image analysis:

The paper titled “Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation” by Kim, M., Byeon, J., Chang, J., and Youm, S., published in Applied Sciences in 2025, presents a novel approach to forecasting post-liposuction body contours using RGB image-to-image translation techniques.

Key Details:

  • Authors: M. Kim, J. Byeon, J. Chang, and S. Youm

  • Publication Year: 2025

  • Journal: Applied Sciences

  • Citation Count: Cited by 3 articles as of 2025

Research Highlights:

The study focuses on leveraging RGB image-to-image translation methods to predict the outcomes of liposuction procedures. By utilizing preoperative images, the model aims to generate realistic visualizations of post-surgical body shapes, enhancing patient consultations and surgical planning.

Related Works:

While direct citations of this paper are limited, related research in the domain includes:

  • Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: This study introduces a deep learning approach to generate 3D body models from 2D images, facilitating obesity monitoring and body shape analysis. scholarworks.dongguk.edu+2Dongguk University+2MDPI+2

  • Development of an Obesity Information Diagnosis Model Reflecting Body Type Information Using 3D Body Information Values: This research emphasizes the use of 3D body data to enhance obesity diagnosis models, reflecting detailed body type information. MDPI+4ResearchGate+4MDPI+4

  • Predictive Model for Abdominal Liposuction Volume in Patients with Obesity Using Machine Learning: This study develops a machine learning model to predict liposuction volumes, aiding in surgical planning for obese patients.

Conclusion

Jiseong Byeon is a highly promising early-career researcher with a strong foundation in computer vision, deep learning, and real-world applications. His current trajectory suggests significant potential for future impact in both academic and applied AI research. While it may be slightly early for a top-tier “Best Researcher Award”, he is exceptionally well-positioned for a “Rising Star” or “Promising Researcher” recognition. With continued publication, international exposure, and leadership development, he could become a strong contender for major awards in the near future.

Nicolas Karakatsanis | Medicine and Dentistry | Best Researcher Award

Published on by Award and Honors

Assoc. Prof. Dr. Nicolas Karakatsanis | Medicine and Dentistry | Best Researcher Award 

Associate Professor of Biomedical Engineering in Radiology, at Weill Cornell Medicine, United States.

Dr. Nikolaos A. Karakatsanis is a distinguished nuclear physicist specializing in molecular imaging and medical physics. Currently, he serves as an Assistant Professor at Weill Cornell Medical College and holds several leadership roles in radiology and biomedical engineering. His work spans cutting-edge research in cardiovascular PET/MR imaging, nuclear medicine physics, and quantitative image reconstruction. Dr. Karakatsanis’s contributions to the field have led to advancements in medical imaging technology, benefiting both clinical and research applications. With numerous awards and high-impact publications, he remains at the forefront of nuclear medicine innovation, including notable work in PET scanning and software development.

Professional Profile

Scopus

ORCID

Google Scholar

Education 🎓

Dr. Karakatsanis received his Joint Bachelor’s & Master’s in Electrical & Computer Engineering from the National Technical University of Athens in Greece, graduating in 2005. He completed his Ph.D. in the same field at NTUA in 2010, focusing on medical imaging technologies. During his academic journey, he broadened his expertise as a Visiting Scholar at UCLA’s Crump Institute for Molecular Imaging and through various postdoctoral research fellowships at prestigious institutions, including Johns Hopkins Medical Institutions and Geneva University Hospitals, where he specialized in PET imaging and data analysis.

Experience 💼

Dr. Karakatsanis’s professional career spans both academic and clinical domains. He served as a Research Scientist at Mount Sinai’s Biomedical Engineering & Imaging Institute, advancing cardiovascular imaging techniques. His role as an Assistant Professor at Weill Cornell Medical College focuses on biomedical engineering in radiology, where he leads innovative research in nuclear imaging. Additionally, he contributes to various professional organizations, including the Society of Nuclear Medicine & Molecular Imaging. Dr. Karakatsanis’s diverse background in both academic and clinical settings allows him to integrate theory and practice in advancing medical technologies.

Research Interests 🔬

Dr. Karakatsanis’s research interests are centered on improving molecular imaging techniques, particularly PET and PET/MR imaging. His work aims to enhance image quality and accuracy in clinical applications, especially in cardiovascular imaging. He is passionate about developing and optimizing software for medical imaging and data analysis. His expertise also extends to simulating imaging systems and advancing nuclear physics instrumentation. Dr. Karakatsanis is actively engaged in interdisciplinary research, collaborating across various fields to drive innovation in medical diagnostics and therapeutics.

Awards 🏆

Dr. Karakatsanis has received numerous prestigious awards throughout his career. He was honored with the Thomaidio Award for Best Master’s Thesis in 2005 and Best Journal Publication in 2006. He has also been awarded several Trainee Grants from the IEEE Nuclear Plasma & Sciences Society. His work on PET imaging was recognized with the Original Research Article featured in Physics in Medicine and Biology (PMB) in 2016, which was ranked among the top 10 most popular PMB articles. In 2023, he was awarded the Hal O’Brien Rising Star Award in Nuclear Medicine.

Top Noted Publications 📚

Dr. Karakatsanis’s scholarly contributions include over 70 peer-reviewed research articles, many of which have had a significant impact on the field of medical imaging. Some of his notable publications include:

  • PyTomography: A Python Library for Medical Image Reconstruction
    • Authors: L.A. Polson, R. Fedrigo, C. Li, A. Rahmim, C.F. Uribe
    • Journal: SoftwareX, 2025
    • Overview: This article discusses a Python library for reconstructing medical images, focusing on the advancements and applications in PET imaging.
  • [18F]-Fluoroestradiol (FES) Brain PET in Estrogen Receptor Positive Breast Cancer
    • Authors: J. Ivanidze, A. Sharbatdaran, A. McCalla, J.P.S. Knisely, R.R. Ramakrishna
    • Journal: European Journal of Radiology, 2024
    • Overview: This study evaluates the role of [18F]-Fluoroestradiol (FES) PET in assessing estrogen receptor-positive breast cancer patients, particularly focusing on brain metastases.
  • Assessment of Dual Time Point Protocols in FDG PET/CT
    • Authors: N. Reshtebar, S.A. Hosseini, M. Zhuang, N.A. Karakatsanis, P. Sheikhzadeh
    • Journal: Medical Physics, 2024
    • Overview: A virtual clinical study evaluating dual time-point protocols for producing parametric Ki images in FDG PET/CT scans.
  • [1-11C]-Butanol PET Reveals Impaired Brain to Nasal Turbinates Pathway in Aging Amyloid Positive Subjects
    • Authors: N.H. Mehta, X. Wang, S.A. Keil, G.C.Y. Chiang, M.J. de Leon
    • Journal: Fluids and Barriers of the CNS, 2024
    • Overview: This study uses [1-11C]-Butanol PET to investigate impaired pathways in aging subjects with amyloid, with a focus on the brain and nasal turbinates.
  • [Ga68] DOTATATE PET/MRI-guided Radiosurgical Treatment in Meningiomas
    • Authors: J. Ivanidze, S. Chang, A. Haghdel, J.D. Palmer, J.P.S. Knisely
    • Journal: Neuro-Oncology, 2024
    • Overview: The paper discusses how PET/MRI-guided radiosurgery is used for treatment planning and response assessment in patients with meningiomas.
  • DOTATATE PET/MR Imaging in Differentiating Meningiomas
    • Authors: J. Kim, S.J.C. Chang, A. Haghdel, J.P.S. Knisely, J. Ivanidze
    • Journal: American Journal of Neuroradiology, 2024
    • Overview: Focuses on the differentiation between secondary-progressive and de novo World Health Organization Grade 3 meningiomas using DOTATATE PET/MRI imaging.
  • Brain Fluid Clearance After Traumatic Brain Injury Measured Using Dynamic PET
    • Authors: T.A. Butler, J.J. Schubert, N.A. Karakatsanis, F.E. Turkheimer, S.A. Shah
    • Journal: Neurotrauma Reports, 2024
    • Overview: This article investigates brain fluid clearance post-traumatic brain injury using dynamic PET imaging, offering insights into brain recovery and function.

Conclusion

Based on his exceptional academic qualifications, groundbreaking research contributions, notable professional achievements, and recognition within the scientific community, Dr. Nikolaos A. Karakatsanis is undoubtedly a strong candidate for the Best Researcher Award. His ability to innovate and lead in the field of Nuclear Medicine and Biomedical Imaging positions him as a leader in his domain. While there are areas for further growth, particularly in expanding interdisciplinary collaborations and public engagement, his achievements to date make him a deserving recipient of this prestigious award.