Yen-Wei Chu is a Distinguished Professor at the Institute of Genomics and Bioinformatics and a Joint Appointment Professor at the Institute of Molecular Biology, National Chung Hsing University (NCHU), Taiwan. He earned his Ph.D. in Computer Science from National Chiao Tung University (NCTU), Taiwan.

His research focuses on integrating data mining, machine learning, and artificial intelligence (AI) to develop intelligent decision-making systems across multiple domains, aligning with the advancements of Industry 4.0 and the Internet of Things (IoT). His laboratory is dedicated to constructing AI-driven models for applications in bioinformatics, medical informatics, smart precision agriculture, and food science.

In recent years, his team has successfully developed low-carbon smart rice cultivation technologies and is currently pioneering AI-driven digital breeding solutions to mitigate food security risks posed by extreme climate change.

Natural Computing & Bioinformatics Lab (NCBLab) – Introduction

Natural Computing & Bioinformatics Lab (NCBLab) at National Chung Hsing University (NCHU), Taiwan, is dedicated to the integration of artificial intelligence (AI), machine learning, and big data analytics in bioinformatics, medical informatics, precision agriculture, and food science. Our research aims to develop intelligent computational models and predictive systems that enhance data-driven decision-making across multiple disciplines.

Our lab specializes in:

• Bioinformatics & Medical Informatics: Developing AI-powered tools for genomic analysis, disease prediction, and precision medicine applications.
• Smart Agriculture & Digital Breeding: Utilizing AI to optimize crop cultivation, improve breeding efficiency, and address food security challenges posed by climate change.
• AI & Machine Learning: Constructing advanced deep learning models for biological data analysis and intelligent decision support.

Our team has successfully developed low-carbon smart rice cultivation techniques and continues to innovate in digital breeding technologies to tackle future agricultural and environmental challenges. By integrating cutting-edge computational approaches, we strive to drive advancements in biomedical sciences, sustainable agriculture, and smart technology applications.