Prof Dr. Jong-Chen Chen, National YunLin University of Science and Technology, Taiwan
Jong-Chen Chen is a Full Professor in the Department of Information Management at the National Yunlin University of Science and Technology, Taiwan, where he has served since 2000. He earned his M.S. in Computer Science and Electrical Engineering from the University of Texas at Arlington in 1986 and his Ph.D. in Computer Science from Wayne State University in 1993. Dr. Chen is renowned for developing a neuromolecular model that abstracts biological structure-function relationships into a system’s structure, allowing for rich intraneuronal dynamics. This model enables the system to self-organize and learn, with applications in robotic maze navigation, image recognition, data differentiation, and more. His research spans areas such as evolvable hardware, brain-like computer simulation, bio-computing, evolutionary computation, and pattern recognition. He has published extensively in journals including Sensors, Applied Science, Biomimetics, Evolutionary Computation, and Neurocomputing.
Professional Profile:
ORCID
Summary of Suitability for Best Researcher Award:
Dr. Jong-Chen Chen demonstrates strong qualifications for the “Best Researcher Award” due to his significant contributions
in various fields of computer science, robotics, and biomedical applications. He has extensive experience, having earned an
M.S. degree in computer science and electrical engineering and a Ph.D. in computer science. His academic journey,
particularly his tenure as a full professor since 2000, reflects a longstanding commitment to research and education.
Education:
- M.S. in Computer Science and Electrical Engineering (1986)
- University of Texas at Arlington, USA
- Ph.D. in Computer Science (1993)
- Wayne State University, USA
Work Experience:
- Full Professor (2000 – Present)
- Department of Information Management, National Yunlin University of Science and Technology, Taiwan
- Developed a neuromolecular model (1993), abstracting biological structure-function relationships into system structure, specifically focusing on intraneuronal dynamics.
Key Contributions:
- Applied neuromolecular models in fields like robotic maze navigation, image recognition, and biomimetic robotics.
- Published research in prestigious journals including Sensors, Applied Science, Biomimetics, Algorithms, Evolutionary Computation, Neuro-computing, and BioSystems.
Research Interests:
- Evolvable hardware
- Brain-like computer simulation
- Ecosystem simulation
- Bio-computing
- Artificial life
- Molecular electronics
- Evolutionary computation
- Genetic programming
- Pattern recognition
Publication top Notes:
Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients
Applying an Artificial Neuromolecular System to the Application of Robotic Arm Motion Control in Assisting the Rehabilitation of Stroke Patients—An Artificial World Approach
Bridging the Finger-Action Gap between Hand Patients and Healthy People in Daily Life with a Biomimetic System
Using Artificial Neuro-Molecular System in Robotic Arm Motion Control-Taking Simulation of Rehabilitation as an Example
Effectiveness of Companion Robot Care for Dementia: A Systematic Review and Meta-Analysis
Using Homemade Pressure Device to Improve Plantar Pressure-A Case Study on the Patient with Lower Limb Lymphedema