主讲人简介：Xuerong Mao (毛学荣)，英国Strathclyde大学数学与统计系教授、爱丁堡皇家学会会士、教育部海外名师、长江讲座教授、东华大学兼职特聘教授

报告摘要：In this talk we will explain how the ordinary differential equations (ODEs) are not enough to model the underlying stochastic quantity and why stochastic differential equations (SDEs) appear naturally. Several well-known SDE models will be presented including the Nobel prize winning model in finance, stochastic SIS epidemic model,  stochastic Lotka-Volterra model in population dynamics.  We will then explain how SDE models differ significantly from ODE models and reveal the crucial role of noise. We will then emphasise that  the use of SDE models depend on the estimation of system parameters. In the case when the model has only a few parameters, we show how they can be estimated by the classical statistical methods, e.g., the least-square method; while when there are lots of parameters, we will show how the deep learning plays its crucial role.