Suleman Mazhar (Senior Member, IEEE) received the postdoctoral degree in information and communication engineering from Georgetown University, Washington, DC, USA, in 2015, and the Ph.D degree in information and communication engineering from Tokyo University, Tokyo, Japan, in 2010.,He loves teaching machine learning and data science and spends leisure time in software consultancy, field work, protecting endangered species in the rivers and endangered beings on the roads. He is currently a Professor with the College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, China. His research interests include acoustics and machine learning.

Dr. Wei Fang is currently a Professor and Doctoral Supervisor at the Nanjing University of Information Science and Technology in China, also with State Key Laboratory for Novel Software Technology, Nanjing University. He is a Visiting Senior Research Fellow at King's College London and Visiting Scholar at University of Florida. He is also a distinguished member of China Computer Federation, and executive committee member of Computer Application Committee of China Computer Federation. He has published 70+ papers in conferences and journals, including 50+ SCI indexed papers, obtained 17 national invention patents, and won more than 40 honors. His research interests cover Artificial Intelligence for Science, Big Data in Meteorology, Machine Learning, and Computer Vision.

Speech Title: AI Applications in Meteorological Research

Abstract: Amidst the artificial intelligence (AI) boom in the world, the latest application emerges in meteorology, where AI technology has been used to enhance the accuracy of weather forecasts, offering a more effective coping mechanism for weather disasters during a summer challenged by heat waves, heavy downpours and typhoons. The talk will provide a comprehensive overview of the latest applications of AI in meteorological research. Topics will include development history of AI applications in Meteorology, similarities and differences between AI and Meteorology, time-series prediction, and future work.