Kwang-Sig Lee | Artificial Intelligence | Excellence in Research

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

Korea University Anam Hospital AI Center | South Korea

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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

Ying-Chih Sun | Business Intelligence and Analytics | Best Researcher Award

Assist Prof Dr. Ying-Chih Sun | Business Intelligence and Analytics | Best Researcher Award

Assist Prof Dr. Ying-Chih Sun at East Central University, United States

👨‍🎓Professional Profiles

📚 Summary

Dr. Ying-Chih Sun is an Assistant Professor of Business Administration – Information Technology Management at East Central University. With a Ph.D. in Information Systems Engineering and Management and a strong academic background in economics, data analytics, and IT management, his research focuses on AI applications in healthcare, telehealth, and supply chain management. He is passionate about integrating business analytics with IT solutions to improve operational efficiencies across industries.

🎓 Education

Dr. Sun holds a Ph.D. in Information Systems Engineering and Management (ISEM) from Harrisburg University of Science and Technology (2023), where he graduated with a perfect 4.0 GPA. He also earned M.S. degrees in Economics from the University at Buffalo (2018) and Texas A&M University (2010), and an M.B.A. in Applied Economics (Summa Cum Laude) from National Taiwan Ocean University (2005). He completed his undergraduate studies in International Trade at Tamkang University (2003).

💼 Professional Experience

Dr. Sun is currently serving as an Assistant Professor at East Central University, teaching courses in Business Economics, Data Analytics, and Cloud Management. Before this, he was an Assistant Professor in Residence at Bradley University (2023-2024), where he contributed to research and teaching in business analytics. He also worked as a Research Assistant at Harrisburg University (2019-2023), conducting research on telehealth and healthcare delivery. Previously, he served as an IT Research Data Analyst at SUNY Buffalo and held roles at the Institute of Economics, Academia Sinica and Tri-Service General Hospital in Taiwan.

📝 Academic Publications & Conferences

Dr. Sun has presented at major conferences like the DSI Conference 2024, AMCIS 2024, and MWAIS 2024. His research papers focus on a range of topics, including telehealth efficiency, AI in healthcare, digital reputation in business schools, and supply chain optimization. His recent work on “Evaluating Telehealth Efficiency” and “Enhancing Judicial Decision-Making with AI” exemplifies his interdisciplinary approach to solving complex business and healthcare challenges.

💻 Technical Skills

Dr. Sun possesses advanced proficiency in data analytics tools like SQL, STATA, and Excel for statistical modeling and large data analysis. He is also familiar with Python and has hands-on experience in cloud management and telehealth technology. His technical skills enable him to approach research from both an analytical and technological perspective, focusing on improving efficiency in healthcare and business operations.

👨‍🏫 Teaching Experience

Dr. Sun has extensive teaching experience, having taught courses at East Central University (2024-present) in Business Economics, Data Analytics, and Cloud Management. He previously taught at Bradley University (2023-2024), covering Business Analytics and Business Analytics Software. Additionally, during his time at SUNY at Buffalo (2015-2018), he served as a Grader and Teaching Assistant for various econometrics courses and contributed to graduate-level teaching in Computational Econometrics.

🔬 Research Interests

Dr. Sun’s primary research interests lie at the intersection of AI, telehealth, and business analytics. He explores how data-driven decision-making can enhance healthcare delivery and operational efficiency, with a focus on telemedicine applications and the use of machine learning for healthcare and business management. His work on optimizing AI investments and supply chain management has important implications for improving cost-effectiveness and decision-making in businesses and healthcare systems.

 

📖 Top Noted Publications

A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

Authors: Sun, Y.-C., Cosgun, O., Sharman, R., Mulgund, P., Delen, D.
Journal: Decision Analytics Journal
Year: 2024

The impact of policy and technology infrastructure on telehealth utilization

Authors: Sun, Y.-C., Cosgun, O., Sharman, R.
Journal: Health Services Management Research
Year: 2024

The Effect of Varying User Risk Levels on Perceived Social Presence in Mental Health Chatbots

Authors: Thimmanayakanapalya, S.S., Singh, R., Mulgund, P., Sun, Y.-C., Sharman, R.
Conference: 29th Annual Americas Conference on Information Systems (AMCIS 2023)
Year: 2023