Dr. Kwang-Sig Lee | Artificial Intelligence | Excellence in Research

Korea University Anam Hospital AI Center | South Korea

Publication Profile

INTRODUCTION 🧑‍🎓

Dr. Kwang-Sig Lee is a distinguished academician, research leader, and innovator specializing in the integration of medical and social informatics. Currently serving as Vice Center Chief at the AI Center of Korea University Anam Hospital, Dr. Lee’s groundbreaking research combines artificial intelligence with diverse datasets, such as genetic, image, numeric, and text data. His expertise in AI-driven systems has led to revolutionary work in healthcare, focusing on predictive AI models and multimodal machine learning. His work has earned significant recognition, influencing both academic circles and real-world medical applications.

EARLY ACADEMIC PURSUITS 🎓

Dr. Lee’s academic journey began at Kon Kuk University in Seoul, South Korea, where he earned a Bachelor’s degree in Economics and Physics (1994-1999), with a GPA of 3.52/4.00. He furthered his studies at Iowa State University (1999-2004), earning dual Master’s degrees in Economics and Sociology, graduating with a GPA of 3.69/4.00. Dr. Lee’s pursuit of advanced studies led him to Johns Hopkins University, where he obtained his Ph.D./MSE in Sociology and Applied Mathematics with a focus on health systems, securing a GPA of 3.63/4.00.

PROFESSIONAL ENDEAVORS 💼

Dr. Lee’s professional career spans across multiple prestigious institutions. At present, he holds the role of Vice Center Chief at Korea University’s AI Center in the Medical School. His previous roles include serving as a Research Associate Professor at the same institution, and Senior Research Fellow positions at various research agencies, including Acorn, Enliple, Agilesoda, and National Health Insurance Service. Dr. Lee has also worked as an Assistant Professor at Yonsei University and Sungkyunkwan University, contributing his expertise in preventive medicine and biostatistics.

CONTRIBUTIONS AND RESEARCH FOCUS ON ARTIFICIAL INTELLIGENCE 🧬

Dr. Lee’s contributions to the field of medical and social informatics are vast and impactful. His research primarily focuses on the application of artificial intelligence (AI) in healthcare, especially through the use of machine learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models. His work explores the integration of genetic, image, numeric, and text data, thereby providing a more holistic approach to solving complex health-related challenges. Dr. Lee is known for his expertise in “Wide & Deep Learning,” a method that synthesizes multiple AI approaches to offer more powerful insights and predictive models.

IMPACT AND INFLUENCE 🌍

Dr. Lee’s innovative research has earned significant recognition both in academic and industrial circles. His work has been published in over 45 SCIE-indexed journals with a combined impact factor of 168. His Field-Weighted Citation Impact is 1.76, surpassing the average citation impact of KAIST in 2021. His expertise is frequently sought in both teaching and consultation, with Dr. Lee being an influential member of various academic committees. His ability to mentor graduate students and lead multidisciplinary research projects has significantly shaped the landscape of AI and healthcare.

ACADEMIC CITATIONS AND PUBLICATIONS 📚

Dr. Lee’s extensive publication record spans notable journals such as Cancers, European Radiology, International Journal of Surgery, and Journal of Medical Systems. His contributions have greatly impacted the scientific community, with a robust body of work related to medical AI, health informatics, and predictive modeling. In addition to his research articles, Dr. Lee has served as a guest editor for journals like Frontiers in Bioscience and Applied Sciences, as well as a reviewer for top-tier publications such as Nature Communications and Journal of Big Data.

HONORS & AWARDS 🏆

Throughout his academic career, Dr. Lee has been the recipient of numerous accolades, including full financial support during his tenure at Johns Hopkins University and Iowa State University. Additionally, he has been awarded for his academic excellence during his undergraduate studies at Kon-Kuk University. Dr. Lee’s consistent recognition for research excellence is evident from his impactful publications and active participation in the academic community, including his involvement in major research projects funded by government agencies.

FINAL NOTE ✨

Dr. Kwang-Sig Lee’s career is a testament to his dedication to advancing AI in medicine and social sciences. His expertise in combining machine learning with healthcare applications has paved the way for new innovations in predictive medicine, offering vital contributions to global healthcare systems. As he continues to lead research projects at the AI Center at Korea University, Dr. Lee’s legacy will be defined by his commitment to improving public health through cutting-edge technology and interdisciplinary research.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Dr. Lee’s future endeavors are poised to have a lasting impact on the fields of AI and healthcare. As the Vice Center Chief at one of the top medical schools, his work is expected to advance predictive healthcare models, fostering the development of more personalized and efficient healthcare solutions. With his expertise in “Wide & Deep Learning,” Dr. Lee is likely to continue influencing both the academic community and industry, shaping the future of AI-driven healthcare innovations.

TOP NOTES PUBLICATIONS 📚

Article: Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old

Authors: K. Lee, Kwang-sig; B. Ham, Byung-joo
Journal: Psychiatry Investigation
Year: 2024

Article: A Machine Learning-Based Decision Support System for the Prognostication of Neurological Outcomes in Successfully Resuscitated Out-of-Hospital Cardiac Arrest Patients

Authors: S. Lee, Sijin; K. Lee, Kwang-sig; S. Park, Sang-hyun; S. Lee, Sung-woo; S. Kim, Sujin
Journal: Journal of Clinical Medicine
Year: 2024

Article: Clinical and Dental Predictors of Preterm Birth Using Machine Learning Methods: The MOHEPI Study

Authors: J. Park, Jung-soo; K. Lee, Kwang-sig; J. Heo, Ju-sun; K. Ahn, Ki-hoon
Journal: Scientific Reports
Year: 2024

Article: Explainable Artificial Intelligence on Safe Balance and Its Major Determinants in Stroke Patients

Authors: S. Lee, Sekwang; E. Lee, Eunyoung; K. Lee, Kwang-sig; S.B. Pyun, Sung Bom
Journal: Scientific Reports
Year: 2024

Dr. Giuliana. Ramella | Artificial Intelligence | Best Researcher Award

National Research Council, Italy

Professional Profile 👨‍🎓

Scopus Profile

Orcid Profile

Research Gate Profile

Early Academic Pursuits 🎓

Dr. Giuliana Ramella embarked on her academic journey with a strong foundation in Physics, specializing in Cybernetics, from the University of Naples “Federico II”, Italy, where she obtained her Laurea degree in 1990. Her early career was distinguished by a fellowship granted by the Italian National Research Council (CNR) at the Institute of Cybernetics “E. Caianiello”, which marked the beginning of her deep engagement with interdisciplinary research. Her academic background was further enriched by participation in several international and national schools, covering topics from biophysics to machine vision, which laid the groundwork for her future research endeavors.

Professional Endeavors 🧑‍🔬

Dr. Ramella’s professional career at the CNR has spanned decades, beginning in 1991 with a fellowship at the Institute of Cybernetics, now known as the Institute of Applied Sciences and Intelligent Systems (CNR-ISASI). Over the years, she transitioned into permanent research roles, contributing significantly to numerous research projects. Her professional milestones include being a visiting researcher at LIAMA (Sino-French Laboratory) in Beijing, China, and overseeing major research initiatives such as those focused on image processing and the conservation of cultural heritage.

Contributions and Research Focus 🔬

Dr. Ramella’s research spans multiple fields, including image processing, artificial intelligence, neurosciences, and cultural heritage conservation. Notable contributions include leadership in projects such as CNR-IAC-CNR DIT.AD021.077 (focused on color image processing) and the Campania Imaging Infrastructure for Research in Oncology. Her work blends theoretical and practical applications, particularly in the intersection of machine learning and image analysis, demonstrating her commitment to advancing computational methods in complex scientific domains.

Impact and Influence 🌍

Dr. Ramella has made a significant impact both in Italy and internationally, particularly in the fields of biophysics, neurosciences, and cultural heritage preservation. She has led several high-profile projects, influencing the development of automated systems for monitoring and diagnosing cultural heritage, as well as data analysis systems for oncology research. Her leadership in educational coordination has also contributed to the professional development of individuals in specialized fields, including image and data management.

Academic Citations and Scholarly Recognition 📚

Throughout her career, Dr. Ramella has built a robust academic reputation, frequently cited for her work in machine learning frameworks like Pytorch, TensorFlow, and Keras, as well as her contributions to computer vision. Her involvement in international workshops and conferences further underscores her standing as a thought leader in the scientific community. Additionally, her research has had a profound effect on the fields of visual perception and neuroscience, solidifying her as a key figure in these interdisciplinary areas.

Technical Skills and Expertise 💻

Dr. Ramella is highly skilled in a range of programming languages, including Matlab, C/C++, and Python, which are essential tools for her work in data analysis and machine learning. She has a strong command over popular machine learning frameworks like Pytorch, TensorFlow, and Keras, which she applies to advanced research in image processing, signal analysis, and high-dimensional data modeling. Her technical expertise is fundamental to her contributions to automated systems in the analysis of cultural artifacts and medical imaging.

Teaching Experience 🍎

In addition to her research, Dr. Ramella has played an essential role in educational coordination. She co-led specialist courses for unemployed individuals and workers in mobility, aimed at developing technical skills for sectors like image management and building heritage monitoring. Her role in shaping the professional development of students in the fields of image analysis and information technology has left a lasting impact on both the academic and professional communities.

Legacy and Future Contributions 🔮

Looking ahead, Dr. Ramella’s legacy is poised to continue making waves in the fields of artificial intelligence and machine learning, especially in the areas of healthcare, cultural heritage conservation, and data-driven methodologies. Her ongoing involvement in projects like the Agritech research program, funded by the European Union through Next Generation EU, speaks to her future aspirations to contribute to cutting-edge research and technological advancements. Dr. Ramella’s future work promises to leave an indelible mark on interdisciplinary fields, continuing her legacy of innovation and impact across global scientific communities.

Top Noted Publications 📖

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Remote Sensing
Year: 2023

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Journal of Mathematical Imaging and Vision
Year: 2023

Filtered Polynomial Interpolation for Scaling 3D Images

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Electronic Transactions on Numerical Analysis
Year: 2023

Lagrange–Chebyshev Interpolation for Image Resizing

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Mathematics and Computers in Simulation
Year: 2022

Saliency-based Segmentation of Dermoscopic Images Using Colour Information

Author: Ramella, G.
Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Year: 2022

Kwang-Sig Lee | Artificial Intelligence | Excellence in Research