Dr. Li Weigang, China nationality, graduated from Northeastern University, professor and doctoral supervisor of Wuhan University of Science and Technology, and distinguished professor, a scholar in Chutian, Hubei. He has profound academic foundation and rich practical experience in the field of intelligent manufacturing.
Dr. Li Weigang presided over and participated in many general projects of National Natural Science Foundation, Hubei R&D Key Plan, Wuhan Youth Science and Technology Morning Light Plan, Donghu 355 1 Optics Valley Talent Plan and enterprise scientific research projects. These projects cover intelligent manufacturing, industrial internet, big data analysis and other fields, and related achievements have been applied by many enterprises, making an important contribution to promoting the domestic intelligent manufacturing technology level.
Dr. Li Weigang's research results have not only been widely recognized by academic circles, but also received attention and praise from the industry. His research team has rich practical experience and technical accumulation in the field of intelligent manufacturing, which has provided strong support for the development of intelligent manufacturing in China.
Li Weigang's research direction
Dr. Li Weigang's research interests mainly include modeling, control and optimization of complex industrial processes, big data mining, machine learning and intelligent algorithms, as well as new methods and applications of artificial intelligence and deep learning.
In the modeling, control and optimization of complex industrial processes, he devoted himself to studying how to establish accurate mathematical models to describe and predict various phenomena in industrial production processes, and to explore effective control strategies and methods to optimize production processes and improve production efficiency and product quality.
In big data mining, machine learning and intelligent algorithms, he is concerned about how to use large-scale data to extract useful information and knowledge, and analyze and predict various trends and patterns in industrial production process through machine learning and intelligent algorithms to provide support for decision-making.
In artificial intelligence and deep learning new methods and their applications, he devoted himself to studying how to use artificial intelligence and deep learning technology to improve the intelligent level of industrial production, optimize the production process by establishing efficient models and algorithms, and improve production efficiency and product quality.