Kwang-Sig Lee | Artificial Intelligence | Excellence in Research

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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 đź’Ľ

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 đź“š

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 đź”®

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 đź“š

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

Qinxu Ding | Machine Learning and AI Applications | Best Researcher Award

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Dr. Qinxu Ding | Machine Learning and AI Applications | Best Researcher Award

Dr. Qinxu Ding at Singapore University of Social Sciences, Singapore

PROFILES👨‍🎓

EARLY ACADEMIC PURSUITS đź“š

This accomplished individual began their academic journey with a B.S. in Information and Numerical Science from Nankai University, Tianjin, China, graduating in July 2015 with an impressive GPA of 89/100. Their undergraduate studies laid a strong foundation in Mathematical Statistics, Probability Theory, and Data Mining, which paved the way for further academic exploration. They pursued a Ph.D. in Computational Mathematics at Nanyang Technological University, completing their thesis on Numerical Treatment of Certain Fractional and Non-fractional Differential Equations in April 2020.

PROFESSIONAL ENDEAVORS đź’Ľ

Currently, this individual serves as a Lecturer and DBA Supervisor in the Finance Programme at the Business School of the Singapore University of Social Sciences (SUSS) since July 2021. Their teaching portfolio includes various fintech courses such as Machine Learning and AI for FinTech, Blockchain Technology, and Risk Management for Finance and Technology. Prior to this role, they were a Research Fellow at the Alibaba-NTU Singapore Joint Research Institute, focusing on Explainable Artificial Intelligence.

CONTRIBUTIONS AND RESEARCH FOCUS 🔍

Their research interests lie at the intersection of applied machine learning, computational mathematics, and blockchain technology in fintech. They have made significant contributions through publications in top-tier AI conferences, including ICLR, NeurIPS, AAAI, and CIKM, as well as prestigious journals in computational mathematics and fintech.

IMPACT AND INFLUENCE 🌟

The individual has played a pivotal role in enhancing the understanding of machine learning models and their applicability in real-world financial scenarios. Their work on adversarial attacks in deep neural networks and the development of explainable AI frameworks has positioned them as a thought leader in the field, influencing both academic and industrial practices.

ACADEMIC CITES đź“–

Their groundbreaking research has been recognized in multiple high-impact venues, highlighting their innovative approaches to complex problems in fintech. Notable publications include works on adversarial defenses, counterfactual explanations, and recommendation systems, reflecting a robust commitment to advancing knowledge in AI and fintech.

LEGACY AND FUTURE CONTRIBUTIONS 🌱

As a Chartered Fintech Professional and a member of various academic committees, this individual continues to contribute to the fintech community. Their involvement in organizing the Global Web3 Eco Innovation Summit 2022 illustrates their dedication to fostering innovation and collaboration in the fintech space. Looking ahead, they aim to further bridge the gap between academia and industry, shaping the future of fintech through research and education.

TOP NOTED PUBLICATIONS đź“–

Stable neural ode with lyapunov-stable equilibrium points for defending against adversarial attacks
    • Authors: Q. Kang, Y. Song, Q. Ding, W. P. Tay
    • Journal: Advances in Neural Information Processing Systems
    • Year: 2021
Non-polynomial Spline Method for Time-fractional Nonlinear Schrödinger Equation
    • Authors: Q. Ding, P. J. Y. Wong
    • Journal: 15th International Conference on Control, Automation, Robotics and …
    • Year: 2018
A hybrid bandit framework for diversified recommendation
    • Authors: Q. Ding, Y. Liu, C. Miao, F. Cheng, H. Tang
    • Journal: Proceedings of the AAAI Conference on Artificial Intelligence
    • Year: 2021
Quintic non-polynomial spline for time-fractional nonlinear Schrödinger equation
    • Authors: Q. Ding, P. J. Y. Wong
    • Journal: Advances in Difference Equations
    • Year: 2020
A survey on decentralized autonomous organizations (DAOs) and their governance
    • Authors: Q. Ding, D. Liebau, Z. Wang, W. Xu
    • Journal: World Scientific Annual Review of Fintech
    • Year: 2023
The skyline of counterfactual explanations for machine learning decision models
    • Authors: Y. Wang, Q. Ding, K. Wang, Y. Liu, X. Wu, J. Wang, Y. Liu, C. Miao
    • Journal: Proceedings of the 30th ACM International Conference on Information …
    • Year: 2021

Amir Reza Rahimi | artificial intelligence | Best Researcher Award

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Mr. Amir Reza Rahimi, artificial intelligence, Best Researcher Award

Mr. Amir Reza Rahimi at Universidad de Valencia, Spain

Professional Profile

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Summary

Amir Reza Rahimi is a Ph.D. candidate at the University of Valencia, specializing in Language, Literature, and Cultures and their applications. He has extensive teaching experience in English at universities, high schools, and language institutes in Iran. Rahimi has also conducted workshops on using technology in English language teaching. His research, focusing on psycholinguistics, computer-assisted language learning (CALL), and educational technology, has been published in leading journals and presented at international conferences.

Education

Amir Reza Rahimi holds a Master’s degree in English Language Teaching from Shahid Rajaee Teacher Training University in Tehran, Iran, where his thesis focused on the impact of Massive Open Online Courses (MOOCs) on the structural relationship between online regulation and online motivational self-systems among Iranian EFL learners. He earned his Bachelor’s degree in English Language Teaching from the University of Mohaghegh Ardabili in Ardabil, Iran. Currently, he is pursuing a Ph.D. in Language, Literature, and Cultures and its Applications at the University of Valencia, Spain, with research interests spanning computer-assisted language learning (CALL), educational technology, and psycholinguistics.

Professional Experience

Amir Reza Rahimi has amassed significant experience in English language education, having taught at various universities, high schools, and language institutes throughout Iran. His career includes conducting workshops aimed at integrating technology into English language teaching methodologies. Rahimi’s expertise extends to research in psycholinguistics, computer-assisted language learning (CALL), and educational psychology, contributing extensively to scholarly publications and presenting his work at prestigious international conferences. Currently a Ph.D. candidate at the University of Valencia, Spain, Rahimi continues to explore innovative approaches in language education and teacher development.

Research Interests

Amir Reza Rahimi’s research interests encompass several facets of language education and technology integration. His work primarily focuses on computer-assisted language learning (CALL), exploring the impact of Massive Open Online Courses (MOOCs) on language learners’ motivational self-systems and educational outcomes. Rahimi is also deeply engaged in psycholinguistics, investigating the cognitive processes involved in language acquisition and proficiency development. His research extends to educational technology, where he explores innovative methods and tools to enhance language teaching and learning experiences. Rahimi’s scholarly pursuits aim to contribute to theoretical advancements in the fields of language education and educational psychology, addressing contemporary challenges and opportunities in digital learning environments.

Conferences and Workshops

  • WorldCALL conference, Chiang Mai, Thailand
  • 21st Asia TEFL International Conference
  • TELLSI19 Conference, University of Birjand, Iran
  • AELTE 2022, Ankara, Turkey

Academic Membership

  • 2017-Present: Teaching English Language and Literature Society of Iran (TELLSI)

Technical Skills

  • SPSS, LISREL, AMOS, PLS-SEM, MAXQDA
  • Computer literacy (2018-2020)

Amir Reza Rahimi’s extensive background in teaching, research, and professional development showcases his expertise and contributions to the field of English Language Teaching and Educational Technology.

đź“– Publication Top Noted

Article: Correction to: The role of 21-century digital competence in shaping pre-service language teachers’ 21-century digital skills: the Partial Least Square Modeling Approach (PLS-SEM)

    • Authors: Amir Reza Rahimi, Zahra Mosalli
    • Journal: Journal of Computers in Education
    • Year: 2024

Article: Exploring the direct and indirect effects of EFL learners’ online motivational self-system on their online language learning acceptance: the new roles of current L2 self and …

    • Authors: Amir Reza Rahimi, Zahra Mosalli
    • Journal: Asian-Pacific Journal of Second and Foreign Language Education
    • Year: 2024

Article: The role of ChatGPT readiness in shaping language teachers’ language teaching innovation and meeting accountability: A bisymmetric approach

    • Authors: Amir Reza Rahimi, Ana Sevilla-PavĂłn
    • Journal: Computers and Education: Artificial Intelligence
    • Year: 2024

Article: The role of twenty-first century digital competence in shaping pre-service teacher language teachers’ twenty-first century digital skills: the Partial Least Square Modeling …

    • Authors: Amir Reza Rahimi, Zahra Mosalli
    • Journal: Journal of Computers in Education
    • Year: 2024

Article: A tri-phenomenon perspective to mitigate MOOCs’ high dropout rates: the role of technical, pedagogical, and contextual factors on language learners’ L2 motivational selves, and …

    • Authors: Amir Reza Rahimi
    • Journal: Smart Learning Environments
    • Year: 2023