Prof. Dr. Chen-Tung Chen | Performance Analysis | Best Researcher Award

Prof. Dr. Chen-Tung Chen | Performance Analysis | Best Researcher Award

Prof. Dr. Chen-Tung Chen, National United University, Taiwan

Chen-Tung Chen, a renowned academic in Industrial Engineering, holds a distinguished career as a professor at the Department of Information Management, National United University, Taiwan, since 2005. He earned his Bachelor’s and Master’s degrees in Industrial Engineering from National Tsing-Hua University, Taiwan, in 1987. In 1995, he completed his Ph.D. in Industrial Engineering and Management at National Chiao-Tung University, Taiwan. With an extensive academic background, Professor Chen has made significant contributions to various fields, including decision support systems, knowledge management, project management, data mining, and supply chain management. His research integrates fuzzy set theory and multiple criteria decision-making to address complex issues in the aforementioned domains. He has published numerous influential articles in prominent academic journals, shaping the evolution of Industrial Engineering and Information Management in Taiwan and beyond. His work continues to inspire students and professionals alike, making him a key figure in his field.

Professional Profile:

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Suitability for Best Researcher Award:

Professor Chen-Tung Chen is highly suitable for the Best Researcher Award due to his remarkable contributions to the fields of Industrial Engineering and Information Management. His work in fuzzy set theory and multiple criteria decision-making has shaped the way complex problems in areas like project management, supply chain management, and knowledge management are approached. Professor Chen’s interdisciplinary expertise, particularly in decision support systems and data mining, has not only advanced academic knowledge but has also driven innovation in practical applications, benefiting both businesses and research communities. His pioneering research and continuous focus on bridging theory with practice demonstrate his leadership and impact in his field.

🎓Education 

Chen-Tung Chen’s academic journey began with a Bachelor’s and Master’s degree in Industrial Engineering from the Department of Industrial Engineering at National Tsing-Hua University, Taiwan, in 1987. Driven by a passion for research and academia, he pursued his Ph.D. in Industrial Engineering and Management at National Chiao-Tung University, Taiwan, which he completed in 1995. His doctoral research further deepened his understanding of decision-making processes and their applications in management systems. This solid educational foundation provided the groundwork for his academic career, allowing him to delve into cutting-edge research areas such as fuzzy set theory, decision support systems, and knowledge management. Throughout his career, Professor Chen has remained committed to the continuous pursuit of knowledge, contributing to advancements in his field. His education, along with his rigorous research and teaching experience, has positioned him as a respected leader in the fields of Industrial Engineering and Information Management.

🏢Experience 

Professor Chen-Tung Chen has had an esteemed career in academia, holding the position of Professor at the Department of Information Management, National United University, Taiwan, since 2005. Prior to this, he earned a solid foundation in Industrial Engineering and Management, where he gained extensive experience in both teaching and research. His early academic journey included earning his Bachelor’s and Master’s degrees in Industrial Engineering from National Tsing-Hua University and his Ph.D. from National Chiao-Tung University, Taiwan. Over the years, Professor Chen has contributed to the growth of his department by offering innovative courses and mentoring numerous graduate students. His interdisciplinary expertise spans several areas, including fuzzy set theory, project management, data mining, and supply chain management. His influence extends beyond the classroom as he has become a key figure in advancing research in decision support systems, knowledge management, and e-business. His vast experience in academia has made him an invaluable resource for students and the research community.

🏅Awards and Honors 

Professor Chen-Tung Chen has been widely recognized for his outstanding contributions to the fields of Industrial Engineering and Information Management. Over the years, he has received several academic accolades for his excellence in research and teaching. As a respected figure in the academic community, he has earned recognition from numerous prestigious journals, where his work has been published. His research, particularly in the application of fuzzy set theory and multiple criteria decision-making, has garnered attention and praise from his peers. Professor Chen’s ability to bridge the gap between theory and practice has led to his involvement in significant projects in the fields of supply chain management, knowledge management, and e-business. His work has had a profound impact on both the academic and professional realms, earning him awards for his contributions to decision support systems and project management. His dedication to advancing his field continues to inspire colleagues, students, and researchers globally.

🔬Research Focus 

Professor Chen-Tung Chen’s research interests are centered on several critical areas in Industrial Engineering and Information Management. His primary focus lies in the application of fuzzy set theory and multiple criteria decision-making techniques to complex decision-making problems in various fields, including project management, supply chain management, and knowledge management. His work in developing decision support systems has led to innovative solutions for optimizing business processes and improving decision-making efficiency. Professor Chen also explores the role of data mining in extracting actionable insights from large datasets, enhancing organizational performance. His research in e-business focuses on improving digital transformation strategies and decision-making processes in modern enterprises. Additionally, his contributions to the design of supply chain management systems aim to create more efficient and responsive global supply networks. Overall, Professor Chen’s research aims to bridge theoretical knowledge with practical applications, providing valuable tools for both academic and professional communities.

Publication Top Notes:

  1. “Extensions of the TOPSIS for group decision-making under fuzzy environment”
    • Citations: 5275
  2. “A fuzzy approach for supplier evaluation and selection in supply chain management”
    • Citations: 2532
  3. “Acute toxicity and biodistribution of different sized titanium dioxide particles in mice after oral administration”
    • Citations: 1526
  4. “Acute toxicological effects of copper nanoparticles in vivo”
    • Citations: 1358
  5. “Diverse applications of nanomedicine”
    • Citations: 1315

 

 

 

 

 

Prof. Wan Quan Liu | Big Data Analysis | Best Researcher Award

Prof. Wan Quan Liu | Big Data Analysis | Best Researcher Award

Prof. Wan Quan Liu, Sun Yat-sen University, China

Prof. Wan Quan Liu is a prominent professor at the School of Intelligent System Engineering at Sun Yat-sen University, where he has been serving since 2021. He earned his Ph.D. in Electrical Engineering from Shanghai Jiaotong University (1991-1993) and holds a Master of Science in Operational Research and Control from the Institute of Systems Science at the Chinese Academy of Science (1985-1988), as well as a Bachelor’s degree in Mathematics from Qufu Normal University (1981-1985). Previously, he was an ARC Fellow and Senior Lecturer at Curtin University of Technology from 2000 to 2021. Prof. Liu’s research focuses on computer vision, deep learning networks, optimization, and intelligent control systems, where he has made significant contributions that advance these fields.

Professional Profile

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Suitability for the Best Researcher Award:

Prof. Wan Quan Liu’s combination of an extensive educational background, significant research contributions, and recognition in the form of awards makes him an excellent candidate for the Best Researcher Award. His work in computer vision, deep learning, and intelligent control systems is highly relevant in today’s technology-driven landscape, with implications for various sectors including robotics, automation, and artificial intelligence.

The recognition he has received, both at the national and provincial levels, further solidifies his status as a leading researcher in his field. His ongoing research and publications contribute to advancements in critical technologies, making a tangible impact on both academia and industry.

Educational Background:

Prof. Wan Quan Liu earned his PhD in Electrical Engineering from Shanghai Jiaotong University (1991-1993). He holds a Master of Science in Operational Research and Control from the Institute of Systems Science at the Chinese Academy of Science (1985-1988) and a Bachelor’s degree in Mathematics from Qufu Normal University (1981-1985).

Academic Experience:

Currently, Prof. Liu is a professor at the School of Intelligent System Engineering at Sun Yat-sen University (2021-present). Prior to this, he held various positions, including ARC Fellow and Senior Lecturer at Curtin University of Technology (2000-2021).

Research Interests:

Prof. Liu specializes in computer vision, deep learning networks, optimization, and intelligent control systems, contributing significantly to advancements in these fields.

Awards and Recognition:

His exceptional work has earned him several accolades, including:

  • 2023: National Talented Researcher from the National Education Committee
  • 2022: Pearl Leading Researcher from Guangdong Province

Publication Top Notes:

  • Title: AFS-FCM with Memory: A Model for Air Quality Multi-dimensional Prediction with Interpretability
    • Publication Year: 2024
  • Title: Efficient and Fast Joint Sparse Constrained Canonical Correlation Analysis for Fault Detection
    • Publication Year: 2024
  • Title: Efficient and Robust Sparse Linear Discriminant Analysis for Data Classification
    • Publication Year: 2024
  • Title: FedREM: Guided Federated Learning in the Presence of Dynamic Device Unpredictability
    • Publication Year: 2024
  • Title: Invertible Residual Blocks in Deep Learning Networks
    • Publication Year: 2024