报 告 人：郭毅可 教授
Data assimilation is a methodology to incorporate observed data into a prediction model in order to improve numerical forecasting. Data assimilation methods have strongly increased in sophistication to better fit their application requirements and circumvent their implementation issues. Nevertheless, DA approaches are incapable of overcoming fully their unrealistic assumptions, particularly linearity, normality and zero error covariances. With the rapid developments in recent years, deep learning shows great capability in approximating nonlinear systems, and extracting high-dimensional features. Machine learning algorithms are capable of assisting or replacing the traditional methods in making forecasts, without the assumptions of the conventional methods.
Yike Guo is the Dean of School of Computer Engineering and Science Shanghai University, and also a Professor of Computing Science in the Department of Computing at Imperial College London. He is a Senior Member of the IEEE and a Fellow of the Royal Society. He is the founding Director of the Data Science Institute at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine. Professor Guo has published over 200 articles, papers and reports.