学 术 报 告

报告人：王海燕 教授

Arizona State University, USA

题目1：Minimum speeds and traveling waves for non-cooperative reaction-diffusion systems

时间：6 月 9 日（星期二），下午 2:00-3:00

地点：致远楼105

题目2：Spatio-temporal modeling of health information in social media and application in control of influenza

时间：6月10日（星期三），上午10:00-11:00

地点：致远楼102

欢迎各位老师和同学参加！

王海燕教授， 1997年在美国密歇根州立大学获得数学博士和计算机硕士学位，现为美国亚利桑那州立大学数学系教授，主要从事非线性微分方程、反应扩散系统、生物数学、无穷维动力系统、网络信息扩散的建模等研究，兼任《Mathematical Biosciences and Engineering》，《Advances in Differential Equations and Control Processes》等杂志编委、智利研究基金会评审专家，美国路易斯安那州立大学科研竞争力评审专家。在国际著名学术期刊发表SCI论文60多篇，引用次数超过3000次。

报告摘要

Abstract 1: In this talk, we shall discuss the linear determinacy for a class of partially cooperative reaction-diffusion systems and several diffusive SIR models in epidemiology. The SIR models are typical predator-prey models. We characterize the spreading speed as the slowest speed of a family of traveling wave solutions. Our results are applied to a non-cooperative system describing interactions between ungulates and grass. We shall identify conditions for a population of ungulates can invade an infinite grassland. For the diffusive SIR models, we show that the existence of traveling waves is determined by the basic reproduction number of the corresponding ordinary differential equations and wave speeds. We also talk about some recent developments in the direction including the spatial spread of non-monotone integro-difference equations models.

Abstract 2:Social media such as Twitter has gained tremendous popularity in information dissemination. Modeling information spreading in online social networks has become a challenging problem. Most of dynamical models arising from social media only involve ordinary differential equations which describe static or collective social processes over time. Building on intuitive friendship hops in social media, we recently propose to use partial differential equation models to describe the temporal and spatial characteristics of information diffusion in online social networks. In this talk, I will examine a diffusive logistic model based on cyber-distance and geocoded data in Twitter to model diffusion of health related information such as influenza tweets in Twitter. We demonstrate that it can be used to real-timely monitor spread of flu related information in social media, and help control spread of influenza.