Assist. Prof. Dr. Yuchae Jung | Generative AI | Distinguished Scientist Award

Assist. Prof. Dr. Yuchae Jung | Generative AI | Distinguished Scientist Award

Assist. Prof. Dr. Yuchae Jung, Open Cyber University of Korea , South Korea.

Dr. Yuchae Jung is an accomplished Affiliated Professor at KAIST School of Computing, Seoul, South Korea. With an interdisciplinary background spanning computer science, medical sciences, and artificial intelligence, she brings a unique integration of biomedical knowledge and computational innovation to her research. Over the years, Dr. Jung has held key academic and research roles in prestigious institutions, including Harvard Medical School and State University of New York. Her professional journey reflects a strong commitment to advancing digital healthcare, AI-driven diagnostics, and computational biology. πŸ§ πŸ’»πŸ§¬

Professional Profile

Google Scholar

πŸŽ“ Education Background

Dr. Jung earned her Ph.D. and M.S. in Medical Science from The Catholic University of Korea (2008, 2002), following her undergraduate degree in Computer Science from Sookmyung Women’s University in 2000. This solid academic foundation has enabled her to contribute innovatively to both computer science and medical informatics. πŸŽ“πŸ“š

πŸ§ͺ Professional Experience

Dr. Jung is currently affiliated with KAIST’s School of Computing as a professor. She has previously held significant roles at The Catholic University of Korea, Boin IT, Seoul National University, and Sookmyung Women’s University. She has also conducted postdoctoral research at Brigham & Women’s Hospital (Harvard Medical School) and State University of New York. Her professional engagements include lectures, research leadership, and AI-based system development across medical and computing fields. πŸ₯πŸ–₯οΈπŸ“Š

πŸ… Awards and Honors

Dr. Jung has been the Principal Investigator of several prestigious grants from organizations such as the Ministry of SMEs and Startups, National Library of Korea, Ministry of Science, and Ministry of Education. Her projects span from NLP-based clinical dialogue systems to cancer therapy algorithms and bioinformatics applications in glioblastoma research. She was also honored as a keynote speaker by The Korean Society of Pathologists. πŸ†πŸ“œπŸ‡°πŸ‡·

πŸ”¬ Research Focus

Her core research interests lie in Medical AI, including deep transfer learning for digital pathology image analysis, clinical Natural Language Processing (Bio-NLP), and cancer genomics (TFs, repeat sequences, miRNAs). She also explores gene expression network analysis in cancer and functional informatics for precision diagnostics. Her work bridges cutting-edge AI with real-world healthcare applications. πŸ§¬πŸ€–πŸ“ˆ

βœ… Conclusion

Dr. Yuchae Jung is a pioneering figure in interdisciplinary AI and bioinformatics, contributing impactful research to cancer genomics and healthcare AI. With a dynamic academic trajectory and a clear focus on translational science, she continues to be a driving force in computational medicine and smart health systems. Her extensive contributions position her as a deserving candidate for recognition in digital healthcare innovation. πŸŒπŸ’‘πŸ‘©β€βš•οΈ

πŸ“ Top Publications Highlights

  1. Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning
    πŸ“… Published: 2021 in MDPI Sensors
    πŸ“Š Cited by: 39 articles (Google Scholar)
    πŸ” A groundbreaking study applying deep transfer learning for pathology image classification.

  2. Impact of tumor purity on immune gene expression and clustering analyses across multiple cancer types
    πŸ“… Published: 2018 in Cancer Immunology Research
    πŸ“Š Cited by: 107 articles
    πŸ”¬ Investigates how tumor purity affects gene expression in cancer immunology.

  3. Hybrid-Aware Model for Senior Wellness Service in Smart Home
    πŸ“… Published: 2017 in MDPI Sensors
    πŸ“Š Cited by: 25 articles
    🏑 Explores smart health monitoring using a hybrid AI model in smart homes.

  4. Aneuploidy meets network analysis: leveraging copy number alterations
    πŸ“… Published: 2017 in Translational Cancer Research
    πŸ“Š Cited by: 15 articles
    🧬 Integrates systems biology with cancer genomics.

  5. Cancer stem cell targeting: Are we there yet?
    πŸ“… Published: 2015 in Archives of Pharmacal Research
    πŸ“Š Cited by: 55 articles
    πŸ’‘ Reviews strategies to target elusive cancer stem cells.

  6. Systemic approaches identify Z-ajoene as a GBM stem cell-specific targeting agent
    πŸ“… Published: 2014 in Molecules and Cells
    πŸ“Š Cited by: 40+ articles
    πŸ§ͺ Identifies garlic-derived compound with anti-glioblastoma activity.

  7. Numb regulates glioma stem cell fate and growth
    πŸ“… Published: 2012 in Stem Cells
    πŸ“Š Cited by: 100+ articles
    πŸ“ˆ A critical study in stem cell regulation in glioma.

  8. GEAR: Genomic Enrichment Analysis of Regional DNA Copy Number Changes
    πŸ“… Published: 2008 in Bioinformatics
    πŸ“Š Cited by: 80+ articles
    🧬 Proposes a novel method for regional DNA copy number analysis.

  9. DNA methylation patterns of ulcer-healing genes in gastric cancers
    πŸ“… Published: 2010 in Journal of Korean Medical Science
    πŸ“Š Cited by: 35 articles
    πŸ”¬ Connects epigenetics with cancer pathology.

  10. PathCluster: a framework for gene set-based hierarchical clustering
    πŸ“… Published: 2008 in Bioinformatics
    πŸ“Š Cited by: 90+ articles
    πŸ“‚ Presents a tool widely adopted in gene expression analysis.

 

Young-Chan Lee | Generative AI | Excellence in Research

Prof. Young-Chan Lee | Generative AI | Excellence in Research

Professor, Dongguk University, South Korea

🌟 Dr. Young-Chan Lee is a distinguished professor at Dongguk University, Korea, where he has been serving since 2004. With a rich academic and leadership background, he also holds key roles such as Dean of the Continuing Education Institute and the Institute of Ecology Education. Dr. Lee’s contributions extend globally, including positions at universities in Vietnam and Malaysia. His dedication to information systems and management science has earned him a stellar reputation in both academia and industry.

Publication Profile

ORCID

Education

πŸŽ“ Dr. Lee completed his Ph.D. in Management Science from Sogang University in 2003, specializing in data mining, system dynamics, and e-commerce strategy. He also holds an M.A. in Management Science from the same institution, where he concentrated on multi-objective decision-making models, and a B.A. in Business Administration with a focus on finance, econometrics, and management science.

Experience

πŸ’Ό Over his career, Dr. Lee has taken on leadership roles at Dongguk University, including Dean of the School of Business Administration and Office of International Affairs. He has also worked internationally as an Adjunct Professor at Ton Duc Thang University in Vietnam and as a Senior Researcher at INTI International University in Malaysia. His academic career is complemented by editorial roles in several prestigious journals.

Research Focus

πŸ”¬ Dr. Lee’s research interests lie in data mining, machine learning for business analytics, knowledge management, system dynamics, and fintech innovation. He is particularly known for applying systems thinking and multi-criteria decision-making to tackle complex business and management challenges.

Awards and Honours

πŸ† Dr. Lee has received numerous accolades, including multiple Best Paper Awards from leading associations such as the Korea Association of Information Systems. His work has also earned recognition on a global scale, including the Most Cited Paper Award from Elsevier and the Top Downloaded Paper Award from Wiley.

Publication Top Notes

Enhancing Financial Advisory Services with GenAI: Consumer Perceptions and Attitudes Through Service-Dominant Logic and Artificial Intelligence Device Use Acceptance Perspectives

Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation

Enhancing Financial Advisory Services with GenAI: Consumer Perceptions and Attitudes through SDL and AIDUA Perspectives